{ "name": "Make AI Playbook", "description": "The Make AI Playbook is a free, 70+ use-case roadmap that helps teams move from manual work to AI-powered operations across four maturity stages: Build, Accelerate, Scale and Lead. Published by Make, the visual automation platform used by 500,000+ organizations to build AI agents and automated workflows.", "url": "https://playbook.make.com", "canonicalDashboardUrl": "https://playbook.make.com/dashboard", "dateModified": "2026-07-24T09:47:32.161Z", "totalUseCases": 89, "stages": [ "Build", "Accelerate", "Scale", "Lead" ], "license": "All rights reserved. Use cases may be cited with attribution to Make.", "citation": { "preferredUrl": "https://playbook.make.com", "anchorPattern": "https://playbook.make.com/dashboard#use-case-" }, "useCases": [ { "slug": "research-prospects-on-demand", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-research-prospects-on-demand", "title": "Research prospects on demand", "teamName": "Sales", "useCaseName": "Sales", "stage": "Build", "problem": "Account executives manually research prospects before every engagement, searching across the web, business directories, and multiple data sources to compile a comprehensive profile of companies they are about to contact. This repetitive process is performed multiple times daily, consumes significant preparation time, and delays outreach to potential customers.", "solution": "An AI agent accessible through a chat interface that researches prospects on demand. When an account executive provides a company name or domain, the agent dynamically searches across web sources, business directories, and data providers to compile a comprehensive prospect profile, adapting its research approach based on what information is available and what it discovers along the way. The agent delivers conversational summaries and responds to follow-up questions to provide increasingly specific or tailored insights as needed.", "what": "An AI agent accessible through a chat interface that researches prospects on demand. When an account executive provides a company name or domain, the agent dynamically searches across web sources, business directories, and data providers to compile a comprehensive prospect profile, adapting its research approach based on what information is available and what it discovers along the way. The agent delivers conversational summaries and responds to follow-up questions to provide increasingly specific or tailored insights as needed.", "who": "Account executives preparing for prospect engagements and sales outreach.", "impactBullets": [ "Account executives receive comprehensive prospect intelligence on demand through a conversational interface, enabling faster and better-informed outreach." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Research prospect companies on demand, compile profiles from multiple sources, and deliver conversational summaries tailored to each engagement.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/36869/rMXcUA7uJdG.jpeg?v=1774938613275", "destinationUrl": "https://we.make.com/public/shared-scenario/rMXcUA7uJdG/lead-researcher-agent", "hasSharedScenario": true }, { "slug": "generate-sales-enablement-decks-from-account-and-pipeline-data", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-generate-sales-enablement-decks-from-account-and-pipeline-data", "title": "Generate sales enablement decks from account and pipeline data", "teamName": "Sales", "useCaseName": "Sales", "stage": "Build", "problem": "Sales teams spend hours assembling decks for each opportunity by copying slides and hunting data across internal systems. This manual work causes inconsistent messaging, missed updates, and delays in responding to new accounts.", "solution": "Automatically generates tailored presentation decks at scale by pulling relevant data from internal systems, removing most manual effort and ensuring consistency across new opportunities and accounts.", "what": "Automatically generates tailored presentation decks at scale by pulling relevant data from internal systems, removing most manual effort and ensuring consistency across new opportunities and accounts.", "who": "Sales teams, account executives, solution engineers, and sales enablement teams ", "impactBullets": [ "Automated deck generation cuts prep time, ensures consistent messaging, and keeps sales collateral current." ], "setupTime": "3–6 hours", "automationType": "AI Automation", "marketingTagLine": "Auto-generate tailored sales decks from pipeline data to cut prep time, ensure consistency, and keep collateral current.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "create-sales-follow-up-sequences-for-every-lead-stage", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-create-sales-follow-up-sequences-for-every-lead-stage", "title": "Create sales follow-up sequences for every lead stage", "teamName": "Sales", "useCaseName": "Sales", "stage": "Build", "problem": "Leads, demo requests, no-shows, and low-touch accounts enter from multiple sources, but routing and follow-up are inconsistent. Teams rely on manual sequences, causing delays, generic messaging, and missed handoffs.", "solution": "Create smart follow-up sequences in sales intelligence tools and creates a data pipeline for new leads, demo requests, no shows, low-touch accounts, etc. Leverages AI for human in the loop content optimization - ensuring you get the right message to the right person at the right time.", "what": "Create smart follow-up sequences in sales intelligence tools and creates a data pipeline for new leads, demo requests, no shows, low-touch accounts, etc. Leverages AI for human in the loop content optimization - ensuring you get the right message to the right person at the right time.", "who": "Sales‑led organizations — especially SDR, BDR, and Account Executive teams ", "impactBullets": [ "Event-driven follow-up sequences improve speed, personalization, and conversion across every lead stage." ], "setupTime": "3–6 hours", "automationType": "AI Automation", "marketingTagLine": "Event-driven follow-up sequences deliver personalized outreach faster, improving conversions at every lead stage.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-update-and-publish-customer-support-knowledge-base-articles", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-update-and-publish-customer-support-knowledge-base-articles", "title": "Draft, update, and publish customer support knowledge base articles", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Build", "problem": "Product changes, feature details, and support notes are scattered, so customer-facing docs quickly become outdated and inconsistent. Writing and updating guides is repetitive, hard to standardize, and often misses edge cases or release timing.", "solution": "Creates clear, user-friendly help guides and documentation by gathering inputs like product updates, feature details, or knowledge-base items. It drafts structured guides and publishes them to a CMS, database app, or internal wiki, updating existing docs when product changes occur.", "what": "Creates clear, user-friendly help guides and documentation by gathering inputs like product updates, feature details, or knowledge-base items. It drafts structured guides and publishes them to a CMS, database app, or internal wiki, updating existing docs when product changes occur.", "who": "Ideal for SaaS companies, support teams, product teams, and any organisation needing up‑to‑date internal or external documentation", "impactBullets": [ "Help support teams keep customer-facing documentation accurate, consistent, and easier to maintain." ], "setupTime": "2–4 hours", "automationType": "AI Automation", "marketingTagLine": "Keep support docs accurate and effortless with automated drafting, updates, and publishing for every product change.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "escalate-overdue-support-tickets", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-escalate-overdue-support-tickets", "title": "Escalate overdue support tickets", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Build", "problem": "Unresolved support tickets often sit in the queue waiting on input from other teams, leading to missed deadlines, stalled progress, and a poor customer experience due to lack of timely follow-up.", "solution": "The automation regularly checks the support ticketing system for tickets that remain unresolved past a defined time threshold, captures key ticket details (ID, status, assignee, priority), and sends internal alerts to the right team members or communication channels so overdue tickets are quickly addressed.", "what": "The automation regularly checks the support ticketing system for tickets that remain unresolved past a defined time threshold, captures key ticket details (ID, status, assignee, priority), and sends internal alerts to the right team members or communication channels so overdue tickets are quickly addressed.", "who": "Customer support teams and support operations responsible for managing ticket queues and coordinating with internal stakeholders.", "impactBullets": [ "Overdue tickets are proactively flagged and escalated, reducing time-to-resolution and improving customer satisfaction." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Catch overdue support tickets early with automated alerts that speed resolution and keep customers satisfied.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "resolve-support-inquiries-from-knowledge-base", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-resolve-support-inquiries-from-knowledge-base", "title": "Resolve support inquiries from knowledge base", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Build", "problem": "Customer support teams spend significant time manually searching internal knowledge bases to answer repetitive inquiries. When documentation is incomplete or outdated, agents must perform their own research or escalate tickets unnecessarily, leading to slow response times and inconsistent answers across the team.", "solution": "An AI agent that receives customer inquiries from your chatbot and dynamically determines the best way to resolve them - querying the internal knowledge base to find relevant documentation, and intelligently deciding when the available knowledge is insufficient and a web search is needed to enrich its response. The agent adapts its research approach based on the nature and complexity of each inquiry rather than following a fixed lookup sequence.", "what": "An AI agent that receives customer inquiries from your chatbot and dynamically determines the best way to resolve them - querying the internal knowledge base to find relevant documentation, and intelligently deciding when the available knowledge is insufficient and a web search is needed to enrich its response. The agent adapts its research approach based on the nature and complexity of each inquiry rather than following a fixed lookup sequence.", "who": "Customer support agents handling inbound inquiries, support managers looking to reduce escalations, and customers who benefit from faster and more consistent responses.", "impactBullets": [ "Customer inquiries are resolved faster and more consistently by an agent that draws from both internal knowledge and the broader web, reducing the manual research burden on support teams and enabling more inquiries to be answered without escalation." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Resolve customer inquiries faster with an AI agent that combines internal knowledge and web research for consistent, escalation-free support.", "destinationUrl": "https://we.make.com/public/shared-scenario/mVv03bgKoeo/knowledge-support-agent", "hasSharedScenario": true }, { "slug": "create-and-update-documents-from-chat", "teamSlug": "productivity", "useCaseSlug": "productivity", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-create-and-update-documents-from-chat", "title": "Create and update documents from chat", "teamName": "Productivity", "useCaseName": "Productivity", "stage": "Build", "problem": "Producing documents takes significant time, creating new files from scratch, reading through existing ones to find or extract specific information, and translating thoughts into written content. Constant context-switching between tools and files breaks focus, slows output, and makes it harder to produce consistent, high-quality work.", "solution": "An AI agent that interprets document requests through chat and dynamically uses tools to create new documents, read and extract content from existing ones, and add or update sections based on user intent. The agent decides which combination of create, read, and edit actions to take depending on the request, the document's current state, and the information it needs to gather or produce.", "what": "An AI agent that interprets document requests through chat and dynamically uses tools to create new documents, read and extract content from existing ones, and add or update sections based on user intent. The agent decides which combination of create, read, and edit actions to take depending on the request, the document's current state, and the information it needs to gather or produce.", "who": "Anyone who regularly works with documents as part of their day-to-day, including managers drafting reports and updates, analysts extracting and synthesizing information, consultants preparing client deliverables, and team leads maintaining internal documentation.", "impactBullets": [ "Document-heavy work becomes faster and less fragmented, freeing people to focus on thinking and decision-making rather than the mechanics of writing, reading, and updating documents." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Chat with an agent to create new documents, read and extract content from existing ones, and update sections on demand.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/38413/YZE5Tj1PExi.jpeg?v=1776171457720", "destinationUrl": "https://we.make.com/public/shared-scenario/YZE5Tj1PExi/document-agent", "hasSharedScenario": true }, { "slug": "triage-inbox-into-a-daily-digest", "teamSlug": "productivity", "useCaseSlug": "productivity", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-triage-inbox-into-a-daily-digest", "title": "Triage inbox into a daily digest", "teamName": "Productivity", "useCaseName": "Productivity", "stage": "Build", "problem": "Busy professionals spend significant time each day sorting through unread emails to understand what's waiting for them. Manually reading, mentally categorizing, and prioritizing every message creates an overwhelming morning routine and makes it hard to focus on what truly needs attention.", "solution": "An AI agent that reviews unread emails on a daily schedule, reasoning about each message's content, sender context, and intent to map it against the user's custom category list. It adapts its categorization to the nuances of each email, weighs what deserves attention, and delivers a personalized digest to an email address of the user's choice.", "what": "An AI agent that reviews unread emails on a daily schedule, reasoning about each message's content, sender context, and intent to map it against the user's custom category list. It adapts its categorization to the nuances of each email, weighs what deserves attention, and delivers a personalized digest to an email address of the user's choice.", "who": "Any professional managing a high volume of daily email who wants to start the day with clarity on what's in their inbox without reading every message.", "impactBullets": [ "Professionals start each day with a clear, categorized summary of their inbox, letting them prioritize attention without having to read every message individually." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Review unread emails each day, classify them against custom categories, and deliver a prioritized digest to a chosen inbox.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/39852/9vI4Yk7OSE8.jpeg?v=1777290672951", "destinationUrl": "https://we.make.com/public/shared-scenario/9vI4Yk7OSE8/email-digest-agent", "hasSharedScenario": true }, { "slug": "draft-articles-from-content-briefs", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-articles-from-content-briefs", "title": "Draft articles from content briefs", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Content writers spend the majority of their working day manually converting content requests into publish-ready articles, researching topics, identifying valuable insights and content angles, and rewriting everything to match the company's brand voice. This end-to-end process is time-consuming, inconsistent across writers, and creates a bottleneck that limits how much content the team can produce without growing headcount.", "solution": "An intelligent content drafting agent that takes a content request or topic as input and autonomously orchestrates the full article creation process, conducting research, capturing key insights and content angles, and generating a structured, brand-aligned article draft that is ready for review and publication. It handles the entire workflow from idea to draft, allowing writers to focus their time on editing and refining rather than starting from scratch.", "what": "An intelligent content drafting agent that takes a content request or topic as input and autonomously orchestrates the full article creation process, conducting research, capturing key insights and content angles, and generating a structured, brand-aligned article draft that is ready for review and publication. It handles the entire workflow from idea to draft, allowing writers to focus their time on editing and refining rather than starting from scratch.", "who": "Content writers, content strategists, and editorial teams who are responsible for producing a high volume of publish-ready articles and need a faster, more consistent way to go from content brief to polished draft without spending hours on research and writing from scratch.", "impactBullets": [ "Reduces the time content writers spend going from brief to publish-ready draft, enabling the team to produce more high-quality content without increasing headcount." ], "setupTime": "6-8 Hours", "automationType": "Agentic", "marketingTagLine": "Turn content briefs into brand-ready article drafts automatically, so your team publishes more high-quality content without adding headcount.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31448/VB0HvYGNcUJ.jpeg?v=1774591688310", "destinationUrl": "https://we.make.com/public/shared-scenario/VB0HvYGNcUJ/content-draft-ai-agent", "hasSharedScenario": true }, { "slug": "refine-ui-copy-for-brand-consistency", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-refine-ui-copy-for-brand-consistency", "title": "Refine UI copy for brand consistency", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "As product and growth teams move fast shipping updates, UI labels, tooltips, and helper text get written by multiple contributors with no consistent process. Copy gradually drifts from the style guide, becomes fragmented across the product, and creates friction in reviews, ultimately confusing users and degrading the overall product experience without a dedicated UX writer to catch and correct it.", "solution": "A UX copy refinement agent that takes raw or draft product copy, UI labels, tooltips, helper text, error messages, and instantly refines it to be clear, consistent, and on-brand. It applies style guide standards automatically, giving product teams expert-level UX writing output without needing a dedicated full-time copywriter on every update.", "what": "A UX copy refinement agent that takes raw or draft product copy, UI labels, tooltips, helper text, error messages, and instantly refines it to be clear, consistent, and on-brand. It applies style guide standards automatically, giving product teams expert-level UX writing output without needing a dedicated full-time copywriter on every update.", "who": "Product managers, designers, and growth teams who regularly write or review UI copy as part of shipping updates and need a fast, reliable way to ensure every piece of in-product text meets brand and UX writing standards.", "impactBullets": [ "Consistent on-brand copy", "Faster review cycles", "Eliminated style drift", "Scalable UX writing" ], "setupTime": "6-8 Hours", "automationType": "Agentic", "marketingTagLine": "Keep every product word on-brand with instant UX copy refinement that speeds reviews and eliminates style drift.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31325/VSoHWOyR9s8.jpeg?v=1774596852645", "destinationUrl": "https://we.make.com/public/shared-scenario/VSoHWOyR9s8/ux-writing-agent", "hasSharedScenario": true }, { "slug": "generate-seo-metadata-keywords-headlines-briefs-and-linking-for-new-content", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-generate-seo-metadata-keywords-headlines-briefs-and-linking-for-new-content", "title": "Generate SEO metadata, keywords, headlines, briefs, and linking for new content", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "SEO prep work is manual and inconsistent, requiring repeated research and formatting for every campaign. Meta tags, keywords, briefs, and interlinking get rebuilt each time, slowing launches and creating rework across teams.", "solution": "Workflow that helps the marketing team to pre-generate multiple content items such as: Meta tags, Keywords, Campaign headlines, SEO briefs, Content interlinking. It standardizes these assets in advance so campaigns can move faster, with less manual SEO prep work needed from the team. ", "what": "Workflow that helps the marketing team to pre-generate multiple content items such as: Meta tags, Keywords, Campaign headlines, SEO briefs, Content interlinking. It standardizes these assets in advance so campaigns can move faster, with less manual SEO prep work needed from the team. ", "who": "Digital marketing specialists & managers", "impactBullets": [ "Teams generate SEO assets instantly from campaign inputs, enabling faster launches and consistent content quality." ], "setupTime": "1-3 hours", "automationType": "AI Automation", "marketingTagLine": "Instantly generate SEO metadata, keywords, headlines, and briefs to launch campaigns faster with consistent, high-quality content.", "videoUrl": "https://drive.google.com/file/d/1YlRksYz9cfB6-jTMjbn8YbtQb2DCez20/preview", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "enrich-event-registrations-with-attribution", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-enrich-event-registrations-with-attribution", "title": "Enrich event registrations with attribution", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Marketing teams running webinars, conferences, or virtual events manually export registration lists from landing page tools, look up additional contact information, add campaign tracking fields, and import everything into their marketing database. This creates significant delays between registration and follow-up, increases risk of data quality issues, and makes accurate campaign attribution difficult.", "solution": "When a new registration is submitted via the event landing page, the automation captures the contact details, enriches the record by looking up additional firmographic data such as company size and industry from data enrichment services, appends campaign source and UTM parameters for attribution tracking, then creates or updates the contact record in the marketing database with all enriched fields and tags them for the appropriate nurture sequence.", "what": "When a new registration is submitted via the event landing page, the automation captures the contact details, enriches the record by looking up additional firmographic data such as company size and industry from data enrichment services, appends campaign source and UTM parameters for attribution tracking, then creates or updates the contact record in the marketing database with all enriched fields and tags them for the appropriate nurture sequence.", "who": "Demand generation, field marketing, and events teams managing event registrations and follow-up campaigns at scale.", "impactBullets": [ "Event registrations are instantly enriched and synced to marketing systems with proper attribution, enabling immediate follow-up and more accurate campaign performance measurement." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Turn event registrations into enriched, attributed leads instantly for faster follow-up and more accurate campaign measurement.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "research-markets-into-structured-reports", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-research-markets-into-structured-reports", "title": "Research markets into structured reports", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Teams manually search the web for market information, sift through multiple sources, and compile findings into documents. This process is repetitive and time consuming, delaying strategic decisions that depend on up to date market insights.", "solution": "An AI agent that accepts a target market, research focus, and time frame, then conducts web searches, evaluates and synthesizes information across sources, and delivers a structured research document tailored to the original question.", "what": "An AI agent that accepts a target market, research focus, and time frame, then conducts web searches, evaluates and synthesizes information across sources, and delivers a structured research document tailored to the original question.", "who": "Strategy leads, product managers, business development teams, and any employees who regularly conduct market research and need a streamlined process.", "impactBullets": [ "Teams receive comprehensive, structured market research documents without spending hours on manual searching and information compilation." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Accept a research focus and time frame, search the web for relevant market information, and deliver a structured research document.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/38748/V9pqDdp6FRk.jpeg?v=1776344862762", "destinationUrl": "https://we.make.com/public/shared-scenario/V9pqDdp6FRk/market-research-agent", "hasSharedScenario": true }, { "slug": "flag-draft-issues-before-publishing", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-flag-draft-issues-before-publishing", "title": "Flag draft issues before publishing", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Marketing teams spend significant time manually reviewing blog drafts to catch typos and ensure the writing matches brand tone and target-audience style, especially when content volume and article length are high, creating bottlenecks before publishing.", "solution": "When a new draft is created in the CMS, the automation pulls the article text, uses AI to review it for typos, brand-tone mismatches, and ICP/audience language issues, then generates a clear report of findings and suggested fixes for the writer to apply before publication.", "what": "When a new draft is created in the CMS, the automation pulls the article text, uses AI to review it for typos, brand-tone mismatches, and ICP/audience language issues, then generates a clear report of findings and suggested fixes for the writer to apply before publication.", "who": "Content marketing and editorial teams responsible for drafting, reviewing, and publishing blog content.", "impactBullets": [ "The team publishes higher-quality content faster by automatically flagging typos and brand/ICP tone issues as soon as drafts are created." ], "setupTime": "3-5 hours", "automationType": "AI Automation", "marketingTagLine": "Publish higher-quality content faster with AI-powered draft reviews for typos, brand voice, and audience alignment.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "generate-content-performance-recommendations", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-generate-content-performance-recommendations", "title": "Generate content performance recommendations", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Content marketing teams publish across multiple channels but lack the time to systematically analyze which topics, formats, and messaging drive the strongest engagement. Manually reviewing performance data and identifying patterns takes hours, and insights often come too late to inform the next content cycle or campaign adjustments.", "solution": "On a scheduled basis, the automation pulls content performance metrics from publishing platforms and analytics tools, aggregates engagement data by content type, topic, and channel, sends the dataset to an AI agent that identifies high and low-performing patterns and generates specific recommendations such as optimal posting times, top-performing content formats, and underperforming topic areas, then compiles these insights into a report delivered to the marketing team with clear next steps.", "what": "On a scheduled basis, the automation pulls content performance metrics from publishing platforms and analytics tools, aggregates engagement data by content type, topic, and channel, sends the dataset to an AI agent that identifies high and low-performing patterns and generates specific recommendations such as optimal posting times, top-performing content formats, and underperforming topic areas, then compiles these insights into a report delivered to the marketing team with clear next steps.", "who": "Content marketing and brand teams responsible for creating and optimizing editorial calendars and multi-channel content strategies.", "impactBullets": [ "Marketing teams receive data-driven content optimization recommendations automatically, enabling faster iteration and better-performing campaigns based on actual engagement patterns." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Turn content performance data into actionable recommendations for faster optimization and higher-engagement campaigns.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "validate-content-plans-against-icp-criteria", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-validate-content-plans-against-icp-criteria", "title": "Validate content plans against ICP criteria", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Build", "problem": "Marketing teams create content across multiple formats without a reliable way to verify alignment with the company's ideal customer profile. This leads to off-target messaging, wasted production effort, and content that fails to resonate with the intended audience.", "solution": "An AI agent connected to the team's chat platform and a knowledge base containing ICP criteria, buyer personas, and messaging guidelines. The agent reviews content plans submitted by the team, evaluates alignment against ICP parameters, and provides context-specific guidance on how to adjust messaging, tone, or targeting to better match the intended audience.", "what": "An AI agent connected to the team's chat platform and a knowledge base containing ICP criteria, buyer personas, and messaging guidelines. The agent reviews content plans submitted by the team, evaluates alignment against ICP parameters, and provides context-specific guidance on how to adjust messaging, tone, or targeting to better match the intended audience.", "who": "Content marketers, copywriters, and marketing managers responsible for producing blog posts, ad copy, landing pages, social media, and other marketing content.", "impactBullets": [ "Marketing teams can validate content alignment with ICP criteria before production, reducing off-target content and ensuring consistent audience-focused messaging." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Review content plans against ICP criteria, evaluate audience alignment, and provide tailored guidance to adjust messaging before production.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/37462/FziPXzXLBXY.jpeg?v=1775561674330", "destinationUrl": "https://we.make.com/public/shared-scenario/FziPXzXLBXY/icp-content-alignment-agent", "hasSharedScenario": true }, { "slug": "classify-and-route-product-feature-requests-to-the-right-team", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-classify-and-route-product-feature-requests-to-the-right-team", "title": "Classify and route product feature requests to the right team", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Build", "problem": "Feature requests pile up in a shared ideas board and require manual triage. PMs review irrelevant items, routing is inconsistent, and high-signal requests get delayed, reducing the speed of discovery and prioritization.", "solution": "This is an AI workflow that automates the classification and routing of product feature requests. The system scans a product ideas board, identifies the relevant team or product area for each feature request, and sends a notification via an instant messenger tool to the appropriate team. This process aims to streamline product discovery and prioritization by ensuring that Product Managers (PMs) only receive requests relevant to their area.", "what": "This is an AI workflow that automates the classification and routing of product feature requests. The system scans a product ideas board, identifies the relevant team or product area for each feature request, and sends a notification via an instant messenger tool to the appropriate team. This process aims to streamline product discovery and prioritization by ensuring that Product Managers (PMs) only receive requests relevant to their area.", "who": "Ideal for SaaS and product-first companies", "impactBullets": [ "Teams get cleaner feature queues, accelerating prioritization while reducing time spent manually triaging low‑value requests." ], "setupTime": "2 hours", "automationType": "AI Automation", "marketingTagLine": "Automatically route feature requests to the right team so PMs prioritize faster with cleaner, higher-signal queues.", "videoUrl": "https://drive.google.com/file/d/10lfm4FHFH0h2RtVVsVKX7gQ4vV-e564R/preview", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "summarize-interviews-into-candidate-scorecards", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-summarize-interviews-into-candidate-scorecards", "title": "Summarize interviews into candidate scorecards", "teamName": "People", "useCaseName": "People", "stage": "Build", "problem": "Recruiters spend hours reviewing interview recordings and transcripts, then manually extracting skills, experience, and motivations. Notes are inconsistent, scorecards stay incomplete, and stronger candidates can be missed or evaluated unevenly.", "solution": "Analyzes interview recordings or transcripts to pull out key details like experience, skills, and motivations, then creates recruiter notes that fill the candidate’s scorecard in the ATS.", "what": "Analyzes interview recordings or transcripts to pull out key details like experience, skills, and motivations, then creates recruiter notes that fill the candidate’s scorecard in the ATS.", "who": "Recruiters, talent acquisition teams, HR teams in any industry", "impactBullets": [ "Automated interview insight extraction creates consistent, evidence-based scorecards, reduces recruiter admin time, and helps strong candidates stand out." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn interview transcripts into consistent ATS scorecards that save recruiter time and spotlight top candidates.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "route-candidates-by-resume-fit-score", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-route-candidates-by-resume-fit-score", "title": "Route candidates by resume fit score", "teamName": "People", "useCaseName": "People", "stage": "Build", "problem": "HR and recruiting teams manually review each incoming resume to extract candidate information, compare qualifications against job requirements, and determine which applicants should advance to the next stage. This creates significant bottlenecks in high-volume hiring, delays time-to-hire, introduces inconsistent evaluation criteria across recruiters, and prevents teams from responding quickly to strong candidates who may be interviewing with competitors.", "solution": "When a new job application arrives via webhook or scheduled polling, the automation retrieves the resume file and job description from the ATS, sends the resume to an AI parsing module that extracts structured data including name, contact information, work history, education, skills, and certifications, sends the job description to AI for requirement extraction, performs semantic matching that understands skill relationships and context beyond simple keyword matching to compare the candidate profile against requirements, generates a fit score from 0-100 with an explanation, creates a 3-4 sentence candidate summary highlighting key strengths and alignment, updates the ATS with extracted skills tags, fit score, and AI-generated summary, then automatically routes candidates based on their score: candidates scoring 75 or above are moved to the screening stage with notifications sent to recruiters, scores between 50-74 are marked for manual review, and scores below 50 are classified as not qualified. All actions and reasoning are logged to the ATS timeline for transparency.", "what": "When a new job application arrives via webhook or scheduled polling, the automation retrieves the resume file and job description from the ATS, sends the resume to an AI parsing module that extracts structured data including name, contact information, work history, education, skills, and certifications, sends the job description to AI for requirement extraction, performs semantic matching that understands skill relationships and context beyond simple keyword matching to compare the candidate profile against requirements, generates a fit score from 0-100 with an explanation, creates a 3-4 sentence candidate summary highlighting key strengths and alignment, updates the ATS with extracted skills tags, fit score, and AI-generated summary, then automatically routes candidates based on their score: candidates scoring 75 or above are moved to the screening stage with notifications sent to recruiters, scores between 50-74 are marked for manual review, and scores below 50 are classified as not qualified. All actions and reasoning are logged to the ATS timeline for transparency.", "who": "Recruiting teams, talent acquisition teams, HR operations, and hiring managers at companies with high-volume hiring needs across any industry.", "impactBullets": [ "Recruiters save significant time on initial screening, reduce time-to-hire, and improve hiring quality through consistent, bias-reduced candidate evaluation that scales effortlessly across thousands of applications." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Score, summarize, and route resumes automatically to help recruiters hire faster with consistent, high-quality candidate screening.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "identify-sales-signals-from-job-posts", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-identify-sales-signals-from-job-posts", "title": "Identify sales signals from job posts", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Sales and RevOps teams rely on shallow CRM fields and self-reported intent, missing hiring signals that reveal projects in AI, automation, or tech transformation. Manually reviewing current and past job posts per account is already part of sales research, but it doesn’t scale across a growing pipeline.", "solution": "Uncover hidden hiring signals by analyzing a company's job postings. Input a company URL from your CRM or lead form, and the system automatically pulls recent and historical job listings to identify AI, automation, and tech transformation initiatives. ", "what": "Uncover hidden hiring signals by analyzing a company's job postings. Input a company URL from your CRM or lead form, and the system automatically pulls recent and historical job listings to identify AI, automation, and tech transformation initiatives. ", "who": "RevOps, sales operations, account executives and BDRs", "impactBullets": [ "Help sales teams prioritize and qualify opportunities using hiring signals that reveal real transformation intent." ], "setupTime": "6–8 hours", "automationType": "AI Automation", "marketingTagLine": "Turn job posts into sales signals to spot real AI and automation buying intent faster.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "coach-reps-from-call-transcripts", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-coach-reps-from-call-transcripts", "title": "Coach reps from call transcripts", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Sales reps manually listen back to call recordings and review transcripts to identify what they did well and where they fell short against prospecting best practices. This review is repetitive and time-consuming, so it often gets skipped, leaving reps without the feedback they need to hit their targets.", "solution": "An AI agent that sales reps trigger after a call by sharing the transcript, then adapts its analysis to each conversation, surfacing strengths and gaps, logging insights to a performance tracker, and on request synthesizing trends over a chosen period while referencing gold-standard prospecting guidance from internal documents or the web.", "what": "An AI agent that sales reps trigger after a call by sharing the transcript, then adapts its analysis to each conversation, surfacing strengths and gaps, logging insights to a performance tracker, and on request synthesizing trends over a chosen period while referencing gold-standard prospecting guidance from internal documents or the web.", "who": "Sales reps who want ongoing self-coaching on their prospecting calls, plus sales managers and enablement leads looking to understand patterns in rep performance and guide team-wide coaching.", "impactBullets": [ "Sales reps receive personalized, actionable feedback on every call and can see how their performance is trending against prospecting best practices without dedicating hours to self-review." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Analyze call transcripts, log strengths and gaps to a performance tracker, and benchmark rep trends against prospecting best practices.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/38553/Je9BiroxeMv.jpeg?v=1776245727188", "destinationUrl": "https://we.make.com/public/shared-scenario/Je9BiroxeMv/sales-coach-agent", "hasSharedScenario": true }, { "slug": "extract-and-structure-pdf-order-forms-for-faster-order-intake", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-extract-and-structure-pdf-order-forms-for-faster-order-intake", "title": "Extract and structure PDF order forms for faster order intake", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Order forms arrive as PDFs via multiple channels and must be manually opened, interpreted, and keyed into downstream systems, creating bottlenecks in license creation, inconsistent field interpretation, and poor visibility into processing status.", "solution": "The Order Form Extractor automates the processing of PDF order forms from Google Drive by utilizing AI to extract and structure data necessary for license creation. This workflow involves waiting for an order form, processing the PDF using OpenAI's assistant, and then formatting the extracted data before moving and tracking the files.", "what": "The Order Form Extractor automates the processing of PDF order forms from Google Drive by utilizing AI to extract and structure data necessary for license creation. This workflow involves waiting for an order form, processing the PDF using OpenAI's assistant, and then formatting the extracted data before moving and tracking the files.", "who": "Any industry where order forms are received", "impactBullets": [ "Teams get structured order data from PDFs in minutes, accelerating order intake and reducing manual data entry into downstream systems." ], "setupTime": "1-3 hours", "automationType": "AI Automation", "marketingTagLine": "Turn PDF order forms into structured data in minutes to accelerate intake and eliminate manual entry.", "videoUrl": "https://drive.google.com/file/d/1L1nKHk1i79gy0VQLtztRrXVdBpFHQmwx/preview", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "create-and-share-pre-call-research-briefs-for-upcoming-meetings", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-create-and-share-pre-call-research-briefs-for-upcoming-meetings", "title": "Create and share pre-call research briefs for upcoming meetings", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Reps join calls without up-to-date account context because research is scattered across CRM records, web sources, and past notes. This creates inconsistent prep, missed signals, and time lost before meetings.", "solution": "This workflow scans your upcoming calendar events, pulls contact and account details from your CRM and leverages scraping or enrichment tools with AI to generate pre-call prep then posts to an instant messenger channel before each meeting.", "what": "This workflow scans your upcoming calendar events, pulls contact and account details from your CRM and leverages scraping or enrichment tools with AI to generate pre-call prep then posts to an instant messenger channel before each meeting.", "who": "Account executives, sales managers, solution engineers, and BDRs in any sales‑led organisation with recurring customer or prospect calls", "impactBullets": [ "Automated pre-call briefs give reps consistent account context, reduce prep time, and improve meeting outcomes." ], "setupTime": "3–6 hours", "automationType": "AI Automation", "marketingTagLine": "Never walk into meetings cold with AI-generated pre-call briefs delivered automatically before every conversation.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-personalized-replies-for-inbound-leads", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-personalized-replies-for-inbound-leads", "title": "Draft personalized replies for inbound leads", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Sales teams lose high-value leads because researching prospects, drafting personalized replies, and coordinating meeting availability are all handled manually. Follow-ups slip through the cracks as inbox volume grows, and the time it takes to research a lead, craft a tailored message, and propose suitable meeting times means opportunities go cold before the first conversation even happens.", "solution": "A lead response agent that automatically researches new leads, drafts personalized, context-aware email replies, and checks the sales rep's 7-day calendar availability to propose meeting times, all without manual effort. It ensures every new lead receives a timely, tailored response with a clear next step, eliminating the delays that cause high-value opportunities to slip away.", "what": "A lead response agent that automatically researches new leads, drafts personalized, context-aware email replies, and checks the sales rep's 7-day calendar availability to propose meeting times, all without manual effort. It ensures every new lead receives a timely, tailored response with a clear next step, eliminating the delays that cause high-value opportunities to slip away.", "who": "Sales representatives and business development managers who are responsible for responding to inbound leads quickly and personally, but are slowed down by the manual effort of researching prospects, writing tailored outreach, and coordinating availability across a busy calendar.", "impactBullets": [ "Ensures every new lead receives a timely, personalized response with proposed meeting times automatically, eliminating manual research and drafting delays that cause high-value opportunities to go cold." ], "setupTime": "4-8 hours", "automationType": "Agentic", "marketingTagLine": "Turn every inbound lead into a booked conversation with instant, personalized replies and automated meeting scheduling.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31068/X8ZDOgyQ9Mj.jpeg?v=1774014715331", "destinationUrl": "https://we.make.com/public/shared-scenario/X8ZDOgyQ9Mj/market-research-analyst", "hasSharedScenario": true }, { "slug": "flag-stalled-deals-in-your-pipeline", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-flag-stalled-deals-in-your-pipeline", "title": "Flag stalled deals in your pipeline", "teamName": "Sales", "useCaseName": "Sales", "stage": "Accelerate", "problem": "Sales teams managing large volumes of open deals have no consistent, daily view of pipeline health. Stagnating opportunities go unnoticed for days or weeks while high-activity prospects get buried across scattered notes, tasks, and inbox threads — making it nearly impossible for sales managers to prioritize effectively, intervene early, and keep revenue on track.", "solution": "A pipeline health agent that runs daily, analyzes all open deals, and automatically generates a structured traffic light report, flagging stagnating opportunities in red, highlighting high-activity prospects in green, and surfacing everything that needs attention directly in the sales team's inbox. It gives sales managers and reps a clear, consistent view of pipeline health every morning without any manual pulling or reporting.", "what": "A pipeline health agent that runs daily, analyzes all open deals, and automatically generates a structured traffic light report, flagging stagnating opportunities in red, highlighting high-activity prospects in green, and surfacing everything that needs attention directly in the sales team's inbox. It gives sales managers and reps a clear, consistent view of pipeline health every morning without any manual pulling or reporting.", "who": "Sales managers and revenue operations leads who are responsible for monitoring pipeline health across a large number of open deals and need a reliable, daily signal to prioritize follow-ups, identify at-risk opportunities, and keep their team focused on the right prospects.", "impactBullets": [ "Gives the sales team a clear, automated daily view of pipeline health — ensuring stagnating deals are caught early and high-priority prospects never get buried." ], "setupTime": "6-8 Hours", "automationType": "Agentic", "marketingTagLine": "Start every morning with a clear pipeline view—catch stalled deals early and keep high-priority opportunities moving.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/32133/Gh5s84tdJaY.jpeg?v=1774008114090", "destinationUrl": "https://we.make.com/public/shared-scenario/Gh5s84tdJaY/deal-pipeline-agent", "hasSharedScenario": true }, { "slug": "route-tickets-to-right-specialists", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-route-tickets-to-right-specialists", "title": "Route tickets to right specialists", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Accelerate", "problem": "Customer support teams receive hundreds of tickets daily across multiple channels. Support managers manually review and assign tickets to the right specialist based on issue type, product area, and complexity. New support agents spend significant time searching knowledge base articles or escalating issues that could be resolved with existing documentation. This creates delayed response times and inefficient resource allocation.", "solution": "When a new ticket arrives from any channel, the automation captures the ticket content and customer context, uses AI to categorize the issue type and complexity level based on the content analysis, routes the ticket to the appropriate team or agent according to predefined assignment rules based on the AI classification, then uses AI to analyze the ticket against the knowledge base and previous resolutions to suggest the three most relevant articles or solutions that match the issue, delivering these suggestions directly to the assigned agent to enable faster resolution without manual searching.", "what": "When a new ticket arrives from any channel, the automation captures the ticket content and customer context, uses AI to categorize the issue type and complexity level based on the content analysis, routes the ticket to the appropriate team or agent according to predefined assignment rules based on the AI classification, then uses AI to analyze the ticket against the knowledge base and previous resolutions to suggest the three most relevant articles or solutions that match the issue, delivering these suggestions directly to the assigned agent to enable faster resolution without manual searching.", "who": "Customer support agents handling tickets, support managers assigning workload, customers waiting for responses, and product teams who created knowledge base content.", "impactBullets": [ "Tickets are routed to the correct specialist immediately with relevant knowledge base articles automatically suggested, enabling faster response times and empowering agents to resolve more issues independently without escalation." ], "setupTime": "4-6 Hours", "automationType": "AI Automation", "marketingTagLine": "Route every ticket instantly and equip agents with AI-suggested solutions for faster resolutions and fewer escalations.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "capture-categorize-summarize-and-tag-voice-of-customer-feedback", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-capture-categorize-summarize-and-tag-voice-of-customer-feedback", "title": "Capture, categorize, summarize, and tag voice of customer feedback", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Accelerate", "problem": "User feedback comes in via forms, surveys, tickets, and emails, forcing teams to manually consolidate and interpret it; categories are inconsistent, themes are missed, and urgent issues are slow to surface.", "solution": "Captures qualitative user feedback from forms, surveys, support tickets, and emails into a unified database. Automatically categorizes, summarizes, and tags each entry by theme, sentiment, and urgency.", "what": "Captures qualitative user feedback from forms, surveys, support tickets, and emails into a unified database. Automatically categorizes, summarizes, and tags each entry by theme, sentiment, and urgency.", "who": "Product managers, product operations, UX researchers, customer success teams — especially in SaaS and product‑led companies handling large volumes of qualitative feedback", "impactBullets": [ "Customer success and product teams get a single, structured view of customer feedback, making it easier to spot themes, surface urgent issues, and feed the roadmap." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Unify customer feedback automatically to surface themes, flag urgent issues, and guide smarter product decisions.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "resolve-order-requests-from-chat", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-resolve-order-requests-from-chat", "title": "Resolve order requests from chat", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Accelerate", "problem": "Customer support teams constantly switch between their chat platform and the order management system to look up order details, update records, and handle customer requests. This context-switching slows response times, increases data entry errors, and pulls agents away from customer conversations.", "solution": "An AI agent embedded in the team's chat platform that interprets natural language requests from support agents and decides whether to retrieve order information or update records, such as shipping details, cancellations, or fulfillment statuses, then executes the action against the e-commerce backend and delivers results directly in the chat conversation.", "what": "An AI agent embedded in the team's chat platform that interprets natural language requests from support agents and decides whether to retrieve order information or update records, such as shipping details, cancellations, or fulfillment statuses, then executes the action against the e-commerce backend and delivers results directly in the chat conversation.", "who": "Frontline customer support agents, support team leads, and operations managers in ecommerce businesses who regularly interact with order data as part of customer communications.", "impactBullets": [ "Support agents can retrieve and update order information directly from their chat platform without switching to the order management system, resulting in faster customer response times and fewer manual data entry errors." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Interpret order-related requests from the team chat, determine the appropriate retrieval or update action, and deliver results directly in the conversation.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/36102/e3pY83TaIak.jpeg?v=1774597139157", "destinationUrl": "https://we.make.com/public/shared-scenario/e3pY83TaIak/customer-order-management-agent", "hasSharedScenario": true }, { "slug": "flag-negative-feedback-for-follow-up", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-flag-negative-feedback-for-follow-up", "title": "Flag negative feedback for follow-up", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Accelerate", "problem": "Customer support teams collect feedback through forms across the customer journey but reviewing each submission to determine whether it signals satisfaction or a deeper experience issue takes meaningful time. Negative signals often go unnoticed in submission queues, delaying the team's ability to reach out to customers whose experience needs attention.", "solution": "When customer feedback arrives, an agent interprets the message and assesses its sentiment by reasoning about tone, specificity, and underlying intent. It stores every submission alongside its sentiment classification in a shared spreadsheet and alerts the customer support team in their chat channel when the feedback signals a negative experience that warrants follow up.", "what": "When customer feedback arrives, an agent interprets the message and assesses its sentiment by reasoning about tone, specificity, and underlying intent. It stores every submission alongside its sentiment classification in a shared spreadsheet and alerts the customer support team in their chat channel when the feedback signals a negative experience that warrants follow up.", "who": "Customer support and customer experience leads, support managers, and CX analysts who monitor satisfaction trends and respond to customers reporting poor experiences.", "impactBullets": [ "Customer feedback is interpreted as it arrives, captured with sentiment context, and surfaced to the support team whenever it points to a negative experience that calls for follow up." ], "setupTime": "1-2 Hours", "automationType": "Agentic", "marketingTagLine": "Interpret incoming customer feedback, log entries with their sentiment, and notify the customer support team about negative signals.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/40670/j2l0MkWfl6M.jpeg?v=1777966940178", "destinationUrl": "https://we.make.com/public/shared-scenario/j2l0MkWfl6M/customer-feeedback-agent", "hasSharedScenario": true }, { "slug": "analyze-social-performance-across-platforms", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-analyze-social-performance-across-platforms", "title": "Analyze social performance across platforms", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Social media teams managing multiple platforms spend hours each week manually logging into each network to extract engagement metrics, compile data into spreadsheets, and analyze performance patterns. This manual consolidation creates reporting delays, makes cross-platform comparison difficult, and prevents teams from quickly identifying which content types and posting strategies drive the best results.", "solution": "On a scheduled interval, the automation connects to each social media platform via API, pulls engagement metrics including impressions, reach, likes, comments, shares, and clicks for all posts, normalizes the data to account for platform-specific metric definitions, aggregates the information by content type, posting time, and campaign theme, then uses AI to analyze patterns and identify high-performing content characteristics, optimal posting times, and underperforming content areas. The automation generates a consolidated performance report with specific insights and recommendations, then delivers it to the social media team.", "what": "On a scheduled interval, the automation connects to each social media platform via API, pulls engagement metrics including impressions, reach, likes, comments, shares, and clicks for all posts, normalizes the data to account for platform-specific metric definitions, aggregates the information by content type, posting time, and campaign theme, then uses AI to analyze patterns and identify high-performing content characteristics, optimal posting times, and underperforming content areas. The automation generates a consolidated performance report with specific insights and recommendations, then delivers it to the social media team.", "who": "Social media managers, brand teams, and content strategists responsible for managing organic social media presence across multiple platforms.", "impactBullets": [ "Social media teams receive unified performance analytics with actionable insights automatically, enabling data-driven content decisions and eliminating hours of manual reporting work." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Unify social performance data automatically to reveal winning content, optimize posting strategy, and eliminate hours of manual reporting.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "sync-ad-performance-to-one-dashboard", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-sync-ad-performance-to-one-dashboard", "title": "Sync ad performance to one dashboard", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Marketing teams running multi-channel campaigns waste hours each week manually extracting data from different advertising platforms, normalizing inconsistent metric formats, and compiling everything into a unified dashboard. This creates reporting delays, increases risk of human error in data entry, and prevents real-time visibility into campaign performance across channels.", "solution": "On a scheduled interval, the automation connects to each advertising platform via API, pulls campaign performance data including spend, impressions, clicks, and conversions, transforms the metrics into a standardized format to handle platform-specific naming conventions, then writes the normalized data to a centralized dashboard or data warehouse where the marketing team can analyze cross-channel performance in real time.", "what": "On a scheduled interval, the automation connects to each advertising platform via API, pulls campaign performance data including spend, impressions, clicks, and conversions, transforms the metrics into a standardized format to handle platform-specific naming conventions, then writes the normalized data to a centralized dashboard or data warehouse where the marketing team can analyze cross-channel performance in real time.", "who": "Performance marketing teams and growth teams managing paid advertising across multiple platforms who need unified campaign visibility and reporting.", "impactBullets": [ "Marketing teams get real-time, unified campaign performance data without manual effort, enabling faster optimization decisions and more accurate cross-channel ROI analysis." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Unify ad platform reporting automatically for real-time insights, faster optimizations, and more accurate cross-channel ROI.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-on-brand-replies-to-comments", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-on-brand-replies-to-comments", "title": "Draft on-brand replies to comments", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "As social media comment volumes grow, community management teams struggle to respond consistently and on time across all channels. Replies drift off-brand as multiple team members jump in, repeated questions pile up without standardized answers, and critical or reputationally risky comments get buried in the noise, damaging audience trust and brand perception at scale.", "solution": "An social comment management agent that continuously monitors social media channels, drafts brand-aligned responses to incoming comments, and automatically handles high-volume, repetitive interactions without manual effort. It flags only the most critical or sensitive comments for human review, ensuring the team stays in control where it matters most while never leaving a follower without a response.", "what": "An social comment management agent that continuously monitors social media channels, drafts brand-aligned responses to incoming comments, and automatically handles high-volume, repetitive interactions without manual effort. It flags only the most critical or sensitive comments for human review, ensuring the team stays in control where it matters most while never leaving a follower without a response.", "who": "Social media managers and community managers who are responsible for maintaining an active, on-brand presence across social channels and need a scalable way to manage high comment volumes without sacrificing response quality or consistency.", "impactBullets": [ "Ensures every social comment receives a timely, brand-aligned response automatically, while surfacing only the most critical interactions for human review, protecting brand reputation at scale." ], "setupTime": "4-8 hours", "automationType": "Agentic", "marketingTagLine": "Respond to every social comment instantly with on-brand automation, while escalating only high-risk interactions for human review.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31072/06K2pr0znwp.jpeg?v=1774015337764", "destinationUrl": "https://we.make.com/public/shared-scenario/06K2pr0znwp/social-media-comment-responder", "hasSharedScenario": true }, { "slug": "repurpose-product-updates-across-channels", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-repurpose-product-updates-across-channels", "title": "Repurpose product updates across channels", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Product and marketing teams waste significant time manually reformatting and rewriting a single product update for each publishing channel, blog, social media, and internal announcements. This copy-paste driven process slows down launches, creates version drift between channels, and makes it nearly impossible to enforce consistent brand voice and messaging at scale.", "solution": "An intelligent multi-channel content publishing agent that takes a single product update as input and simultaneously generates a fully formatted blog post, social media update, and product announcement, each adapted to the appropriate tone and format for its channel. It publishes across all platforms automatically, ensuring brand consistency with zero manual drafting or reformatting required.", "what": "An intelligent multi-channel content publishing agent that takes a single product update as input and simultaneously generates a fully formatted blog post, social media update, and product announcement, each adapted to the appropriate tone and format for its channel. It publishes across all platforms automatically, ensuring brand consistency with zero manual drafting or reformatting required.", "who": "Product marketers and content managers who are responsible for communicating product updates across multiple channels and need to move fast at launch without sacrificing brand consistency or quality.", "impactBullets": [ "Enables the product marketing team to go from a single product update to fully published, brand-consistent content across all channels in minutes, accelerating launches and eliminating manual reformatting." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Turn one product update into fully published, brand-consistent content across every channel in minutes.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31612/BKGNirN9fBh.jpeg?v=1774015091444", "destinationUrl": "https://we.make.com/public/shared-scenario/BKGNirN9fBh/content-marketing-agent", "hasSharedScenario": true }, { "slug": "answer-event-questions-in-chat", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-answer-event-questions-in-chat", "title": "Answer event questions in chat", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Event teams struggle to manage attendee and staff inquiries during live events, with critical information like schedules, speaker details, and logistics scattered across docs, spreadsheets, and last-minute updates. Staff are constantly distracted by repetitive questions that pull focus away from running the event smoothly, while attendees are left frustrated digging for answers on their own.", "solution": "An intelligent event FAQ agent that ingests event data, schedules, speaker bios, logistics, and real-time updates, and makes it instantly queryable via chat. Attendees and organizers can ask natural language questions and receive immediate, accurate answers without needing to search through documents or wait for a staff member to respond.", "what": "An intelligent event FAQ agent that ingests event data, schedules, speaker bios, logistics, and real-time updates, and makes it instantly queryable via chat. Attendees and organizers can ask natural language questions and receive immediate, accurate answers without needing to search through documents or wait for a staff member to respond.", "who": "Event managers, coordinators, and operations staff who are responsible for running smooth events and managing high volumes of repetitive attendee and staff inquiries in real time.", "impactBullets": [ "Eliminates repetitive event inquiries by giving attendees and staff instant access to accurate event information via chat, freeing the team to focus on running the event." ], "setupTime": "6-8 Hours", "automationType": "Agentic", "marketingTagLine": "Instant event answers via chat, so staff stay focused and attendees always know what’s happening.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31107/Zfajs7Kefom.jpeg?v=1774596995146", "destinationUrl": "https://we.make.com/public/shared-scenario/Zfajs7Kefom/event-query-agen", "hasSharedScenario": true }, { "slug": "track-competitor-positioning-shifts", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-competitor-positioning-shifts", "title": "Track competitor positioning shifts", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Product marketing and strategy teams need to monitor competitor messaging, campaign themes, and positioning shifts to inform their own strategies, but manually tracking competitor content across websites, social media, and advertising channels is extremely time-intensive and often results in reactive rather than proactive strategic planning.", "solution": "On a scheduled interval, the automation collects competitor content from specified sources including websites, social media posts, and ad libraries, extracts key messaging and positioning themes from the collected content, sends the compiled data to an AI agent that analyzes patterns, identifies messaging shifts, compares competitor positioning against the company's current strategy, and generates a strategic brief highlighting key competitive themes, gaps, and recommended positioning adjustments, then delivers the brief to marketing leadership.", "what": "On a scheduled interval, the automation collects competitor content from specified sources including websites, social media posts, and ad libraries, extracts key messaging and positioning themes from the collected content, sends the compiled data to an AI agent that analyzes patterns, identifies messaging shifts, compares competitor positioning against the company's current strategy, and generates a strategic brief highlighting key competitive themes, gaps, and recommended positioning adjustments, then delivers the brief to marketing leadership.", "who": "Product marketing, competitive intelligence, and marketing strategy teams responsible for market positioning and competitive differentiation.", "impactBullets": [ "Marketing leadership receives regular competitive intelligence briefs with strategic recommendations, enabling proactive positioning decisions and faster response to market shifts." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Turn competitor signals into strategic briefs that sharpen positioning and help marketing leaders stay ahead of market shifts.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "repurpose-a-product-brief-for-every-channel", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-repurpose-a-product-brief-for-every-channel", "title": "Repurpose a product brief for every channel", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "When launching a product, marketing teams manually rewrite a single brief into versions for each channel, adjusting tone, length, and structure for every one. Reformatting and routing each version for approval is tedious and delays coordinated launches.", "solution": "Starting from a single product brief, the automation reformats the message for each channel with the appropriate tone, length, and structure, then routes each version through the required approval flow. Approved content is prepared for publishing on every channel.", "what": "Starting from a single product brief, the automation reformats the message for each channel with the appropriate tone, length, and structure, then routes each version through the required approval flow. Approved content is prepared for publishing on every channel.", "who": "Product marketing and communications teams coordinating multi channel product launches.", "impactBullets": [ "A single product brief becomes consistent, approved launch communications for every channel without manual reformatting." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Take a single product brief, reformat it for each channel, and route every version through approval.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "track-competitor-messaging-and-positioning-shifts", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-competitor-messaging-and-positioning-shifts", "title": "Track competitor messaging and positioning shifts", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Marketing teams track competitors by manually visiting websites, social channels, and other sources to spot messaging and positioning changes. This monitoring is inconsistent and time consuming, so important shifts are noticed late or missed entirely.", "solution": "On a set schedule, the automation collects competitor content from sources the team defines and sends it to an AI model that analyzes messaging patterns and identifies positioning shifts. The result is delivered to the team as a strategic brief.", "what": "On a set schedule, the automation collects competitor content from sources the team defines and sends it to an AI model that analyzes messaging patterns and identifies positioning shifts. The result is delivered to the team as a strategic brief.", "who": "Product marketing, strategy, and competitive intelligence teams tracking the market landscape.", "impactBullets": [ "The team receives a regular strategic brief on competitor positioning without manually monitoring every source." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Collect competitor content on a schedule, analyze messaging patterns, and deliver a strategic brief on positioning shifts.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "personalize-outreach-for-every-customer", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-personalize-outreach-for-every-customer", "title": "Personalize outreach for every customer", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Marketing teams send the same generic outreach to broad segments because tailoring messages to each customer by hand is not feasible. Generic campaigns convert poorly and fail to reflect what the team already knows about each contact.", "solution": "When a customer qualifies for outreach, the automation pulls their CRM record, behavioral data, and history, then sends that context to an AI model that drafts personalized messaging. The tailored message is delivered through the customer's preferred channel.", "what": "When a customer qualifies for outreach, the automation pulls their CRM record, behavioral data, and history, then sends that context to an AI model that drafts personalized messaging. The tailored message is delivered through the customer's preferred channel.", "who": "Lifecycle, CRM, and demand generation marketers running customer engagement campaigns.", "impactBullets": [ "Every customer receives outreach personalized to their data and behavior without marketers writing each message by hand." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Pull each customer's CRM and behavioral data, generate personalized messaging, and deliver it through the right channel.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "track-brand-mentions-and-sentiment", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-brand-mentions-and-sentiment", "title": "Track brand mentions and sentiment", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Brand and communications teams need to track how their company is mentioned across news sites, social media, forums, and review platforms to identify reputation risks and opportunities, but manually searching multiple sources daily is extremely time-intensive and often results in delayed responses to critical mentions or sentiment shifts.", "solution": "On a scheduled interval, the automation searches specified sources including news sites, social media platforms, review sites, and industry forums for brand mentions using company name, product names, and relevant keywords, collects all matching content with metadata like source, author, and timestamp, sends the compiled mentions to an AI agent that analyzes sentiment for each mention classifying it as positive, negative, or neutral, identifies themes and topics being discussed, flags urgent issues requiring immediate attention such as PR crises or viral negative sentiment, then generates a summary report categorizing mentions by sentiment and topic with recommendations for response priority and delivers it to the brand team.", "what": "On a scheduled interval, the automation searches specified sources including news sites, social media platforms, review sites, and industry forums for brand mentions using company name, product names, and relevant keywords, collects all matching content with metadata like source, author, and timestamp, sends the compiled mentions to an AI agent that analyzes sentiment for each mention classifying it as positive, negative, or neutral, identifies themes and topics being discussed, flags urgent issues requiring immediate attention such as PR crises or viral negative sentiment, then generates a summary report categorizing mentions by sentiment and topic with recommendations for response priority and delivers it to the brand team.", "who": "Brand managers, communications teams, public relations teams, and social media managers responsible for brand reputation and crisis management.", "impactBullets": [ "Brand teams receive automated daily summaries of brand mentions with sentiment analysis and prioritized alerts, enabling faster responses to reputation threats and better visibility into public perception." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Track brand mentions everywhere with AI sentiment analysis and priority alerts to respond faster and protect your reputation.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "translate-release-tickets-into-launch-briefs", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-translate-release-tickets-into-launch-briefs", "title": "Translate release tickets into launch briefs", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Release knowledge stays locked inside technical tickets that only engineers can easily interpret, leaving product, marketing, and support teams to hunt for updates, misalign on scope, and miss critical edge cases. Without a standardized way to translate technical details into accessible language, launch preparation becomes inconsistent across departments, causing delays, rework, and post-launch support surprises.", "solution": "An release translation agent that reads technical ticket data and automatically generates clear, structured product one-pagers, FAQs, and internal briefs tailored for non-technical teams. It bridges the gap between engineering and the rest of the business, ensuring every department, from marketing to customer support, has exactly what they need to be ready on launch day.", "what": "An release translation agent that reads technical ticket data and automatically generates clear, structured product one-pagers, FAQs, and internal briefs tailored for non-technical teams. It bridges the gap between engineering and the rest of the business, ensuring every department, from marketing to customer support, has exactly what they need to be ready on launch day.", "who": "Product marketers, and cross-functional launch leads who are responsible for aligning multiple departments ahead of a release and need to communicate technical changes clearly without relying on engineers to manually translate every ticket.", "impactBullets": [ "Automatically transforms technical release tickets into clear, department-ready documentation, ensuring every team is aligned and prepared for launch day without manual translation or follow-up." ], "setupTime": "8-10 Hours", "automationType": "Agentic", "marketingTagLine": "Turn technical release tickets into launch-ready briefs so every team stays aligned, informed, and prepared on launch day.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31045/OjWdBPiPtle.jpeg?v=1774591410300", "destinationUrl": "https://we.make.com/public/shared-scenario/OjWdBPiPtle/product-release-automation-agent", "hasSharedScenario": true }, { "slug": "validate-email-lists-for-deliverability", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-validate-email-lists-for-deliverability", "title": "Validate email lists for deliverability", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Email marketing teams accumulate invalid email addresses and inactive subscribers over time, leading to poor deliverability rates, increased sending costs, damage to sender reputation, and inaccurate campaign performance metrics. Manually identifying and removing these contacts by reviewing engagement data and validating email addresses is extremely time-intensive and often done inconsistently or too infrequently.", "solution": "On a scheduled interval, the automation pulls the complete email subscriber list from the marketing database, validates each email address for technical deliverability using email verification APIs to identify invalid, risky, or disposable addresses, analyzes engagement history to identify contacts with zero opens or clicks over the past six months, segments contacts into categories including invalid addresses, inactive subscribers, and engaged contacts, then automatically suppresses or removes invalid and inactive contacts from active campaign lists while documenting the reason for removal and sends a summary report to the marketing team with list hygiene metrics and recommended actions.", "what": "On a scheduled interval, the automation pulls the complete email subscriber list from the marketing database, validates each email address for technical deliverability using email verification APIs to identify invalid, risky, or disposable addresses, analyzes engagement history to identify contacts with zero opens or clicks over the past six months, segments contacts into categories including invalid addresses, inactive subscribers, and engaged contacts, then automatically suppresses or removes invalid and inactive contacts from active campaign lists while documenting the reason for removal and sends a summary report to the marketing team with list hygiene metrics and recommended actions.", "who": "Email marketing managers, marketing operations teams, and demand generation teams responsible for email campaign performance and database health.", "impactBullets": [ "Email lists stay clean with validated addresses and engaged subscribers, improving deliverability rates, reducing sending costs, and protecting sender reputation while maintaining accurate campaign metrics." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Automatically remove invalid and inactive subscribers to boost deliverability, cut sending costs, and protect your sender reputation.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "track-market-trends-for-campaigns", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-market-trends-for-campaigns", "title": "Track market trends for campaigns", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Accelerate", "problem": "Marketing teams manually research market trends by reading articles and reviewing industry reports across many sources. Synthesizing everything into a coherent narrative is time consuming and pulls strategists away from higher value planning work. Insights often arrive too late to influence campaign decisions or content priorities.", "solution": "On a scheduled basis, the agent receives a target market and uses web search tools to investigate articles and industry sources, adapting its research path based on what it surfaces. It judges the relevance and credibility of findings, decides which threads warrant deeper investigation, and synthesizes the discovered insights into a structured trend report delivered to the team.", "what": "On a scheduled basis, the agent receives a target market and uses web search tools to investigate articles and industry sources, adapting its research path based on what it surfaces. It judges the relevance and credibility of findings, decides which threads warrant deeper investigation, and synthesizes the discovered insights into a structured trend report delivered to the team.", "who": "Marketing strategists, brand teams, and content leads who track market trends to inform campaign planning, positioning, and editorial direction.", "impactBullets": [ "Marketing teams receive a complete trend report for each tracked market on a regular cadence, freeing strategists from manual research and surfacing relevant signals before competitors act." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Research a target market across the web, evaluate the most relevant trends, and deliver a synthesized report to the marketing team.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/41566/V59z5M0XZ2t.jpeg?v=1778820019725", "destinationUrl": "https://we.make.com/public/shared-scenario/V59z5M0XZ2t/trends-spotter-agent", "hasSharedScenario": true }, { "slug": "guide-new-users-onboarding-goals-into-the-right-success-paths", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-guide-new-users-onboarding-goals-into-the-right-success-paths", "title": "Guide new users’ onboarding goals into the right success paths", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Accelerate", "problem": "New users struggle to translate their automation goals into the right starting point at signup, so they see irrelevant examples, delay time‑to‑first‑workflow, and often drop off before getting value.", "solution": "Captures new users’ automation goals during signup, scores their relevancy, and directs them to the most relevant use-case resources, templates, or scenarios. The onboarding adapts by matching user intent to personalized content.", "what": "Captures new users’ automation goals during signup, scores their relevancy, and directs them to the most relevant use-case resources, templates, or scenarios. The onboarding adapts by matching user intent to personalized content.", "who": "New Make users across SaaS, operations, marketing, and engineering teams seeking guided onboarding based on their automation goals", "impactBullets": [ "Customer success teams turn onboarding goals into tailored success paths, helping new users reach their first meaningful outcome faster." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn onboarding goals into personalized success paths that guide new users to value faster.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "route-feature-requests-to-owners", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-route-feature-requests-to-owners", "title": "Route feature requests to owners", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Accelerate", "problem": "Teams receive a steady flow of requests and feature ideas across channels, but triage is manual and inconsistent. Items are misrouted, priorities get unclear, and follow-ups slip through.", "solution": "Automatically sorts incoming requests, features, or projects by analyzing their content and context, then assigns them to the right category using predefined criteria. Once classified, the system can trigger follow-up actions, such as notifying the relevant team, updating a database, or starting a review process, ensuring nothing gets missed and workflows stay organized.", "what": "Automatically sorts incoming requests, features, or projects by analyzing their content and context, then assigns them to the right category using predefined criteria. Once classified, the system can trigger follow-up actions, such as notifying the relevant team, updating a database, or starting a review process, ensuring nothing gets missed and workflows stay organized.", "who": "Product teams, engineering teams, support teams, PMs, product ops, and any organisation managing large volumes of requests, tickets, or feature data", "impactBullets": [ "Requests and ideas are consistently classified and routed, reducing manual triage effort while ensuring the right work lands in the right queue with clear next steps." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Automatically classify and route feature requests, spot duplicates, and alert teams when demand signals it’s time to act.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "schedule-postmortems-from-resolved-incidents", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-schedule-postmortems-from-resolved-incidents", "title": "Schedule postmortems from resolved incidents", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Accelerate", "problem": "After an incident is resolved, engineering teams must manually compile alert timelines, communication logs, and responder details into a postmortem document while separately coordinating calendars to schedule the review. This delays postmortems, causes details to be lost as memory fades, and often pushes reviews outside the required SLA window.", "solution": "When an incident is marked resolved above a configured severity threshold, the automation pulls the participant list from on-call rosters, the alerting tool, and the service catalog. It assembles a structured postmortem brief from the alert timeline, key communication messages, and impact data into a templated document. It then finds available time slots for the core responder group and posts a scheduling poll to the incident channel for confirmation before sending the invite with the brief linked.", "what": "When an incident is marked resolved above a configured severity threshold, the automation pulls the participant list from on-call rosters, the alerting tool, and the service catalog. It assembles a structured postmortem brief from the alert timeline, key communication messages, and impact data into a templated document. It then finds available time slots for the core responder group and posts a scheduling poll to the incident channel for confirmation before sending the invite with the brief linked.", "who": "SRE, platform engineering, and DevOps teams responsible for running incident response processes and ensuring postmortem reviews happen consistently within SLA at organizations with moderate to high incident volume.", "impactBullets": [ "Postmortem preparation that previously took hours of manual coordination happens automatically within minutes of incident resolution, ensuring reviews occur within SLA with a comprehensive, pre-populated brief that makes the meeting immediately productive." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Auto-schedule SLA-ready postmortems with pre-built incident briefs, turning resolution into productive reviews within minutes.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "build-and-share-devops-sre-operational-dashboards-from-existing-data-sources", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-build-and-share-devops-sre-operational-dashboards-from-existing-data-sources", "title": "Build and share DevOps/SRE operational dashboards from existing data sources", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Accelerate", "problem": "Engineering teams spend time pulling metrics from multiple systems, writing ad‑hoc queries, and exporting spreadsheets. Dashboards live in external tools, go stale, and are hard to share consistently across departments.", "solution": "Creates custom internal dashboards by connecting to your existing data sources, allowing teams to quickly visualize key metrics without relying on external tools. This workflow lets you build and share tailored reports, making it easy to track and communicate important information across departments.", "what": "Creates custom internal dashboards by connecting to your existing data sources, allowing teams to quickly visualize key metrics without relying on external tools. This workflow lets you build and share tailored reports, making it easy to track and communicate important information across departments.", "who": "Engineering teams, SREs, platform teams, product engineers, and data‑adjacent teams needing fast internal visibility without relying on BI or front‑end support", "impactBullets": [ "Engineering and ops teams get up‑to‑date internal dashboards from existing data, reducing ad‑hoc queries and giving everyone a shared operational view." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn existing data into shareable DevOps dashboards that eliminate ad-hoc reporting and keep teams aligned with real-time operational visibility.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "flag-significant-a-b-test-results", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-flag-significant-a-b-test-results", "title": "Flag significant A/B test results", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Accelerate", "problem": "Product teams running A/B tests often miss or receive experiment outcomes too late because significance checks and result sharing are manual. This leads to delayed decisions, inconsistent product direction, and stakeholders acting on outdated information.", "solution": "On a scheduled interval, the automation checks active experiments against predefined significance thresholds. When significance is reached, it generates a concise summary of results and insights, then notifies the product manager via email or a chosen communication channel so they can act immediately.", "what": "On a scheduled interval, the automation checks active experiments against predefined significance thresholds. When significance is reached, it generates a concise summary of results and insights, then notifies the product manager via email or a chosen communication channel so they can act immediately.", "who": "Product managers and product analytics/growth teams responsible for running experiments and making rollout decisions.", "impactBullets": [ "Product managers get timely, actionable experiment summaries the moment significance is reached, enabling faster and more consistent product decisions." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Automatically detect statistically significant A/B test results and deliver instant summaries so product teams make faster, confident decisions.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "analyze-sentiment-in-reference-checks", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-analyze-sentiment-in-reference-checks", "title": "Analyze sentiment in reference checks", "teamName": "People", "useCaseName": "People", "stage": "Accelerate", "problem": "Reference checking is time-consuming, often delayed until final stages causing offer delays, and produces inconsistent insights depending on who conducts the call and what questions are asked. Manual reference checks rely heavily on subjective interpretation, and recruiters struggle to detect subtle red flags or hesitation in reference responses. Many references are never completed due to scheduling challenges, and the insights gathered aren't systematically documented for hiring manager review.", "solution": "When a candidate reaches Reference Check stage in the ATS (webhook trigger or manual form submission), the automation retrieves candidate data and reference contact information, then sends automated reference survey emails via form tools. Every 6 hours, it checks for completed responses. When a response is detected, the automation retrieves the survey text and sends it to AI for sentiment analysis with a prompt to identify overall sentiment score (0-100), key strengths, concerns or hesitations, enthusiasm level, and red flags. The AI generates structured analysis for each reference. The automation compiles all reference analyses into a unified summary report and calculates an aggregate sentiment score. If any reference scores below 40, it sends an immediate alert to the recruiter. Finally, it generates a formatted PDF report with sentiment scores, key themes, direct quotes, and AI recommendations, emails it to the hiring manager and recruiter, updates the ATS with the reference summary and sentiment scores, and moves the candidate to Final Decision stage.", "what": "When a candidate reaches Reference Check stage in the ATS (webhook trigger or manual form submission), the automation retrieves candidate data and reference contact information, then sends automated reference survey emails via form tools. Every 6 hours, it checks for completed responses. When a response is detected, the automation retrieves the survey text and sends it to AI for sentiment analysis with a prompt to identify overall sentiment score (0-100), key strengths, concerns or hesitations, enthusiasm level, and red flags. The AI generates structured analysis for each reference. The automation compiles all reference analyses into a unified summary report and calculates an aggregate sentiment score. If any reference scores below 40, it sends an immediate alert to the recruiter. Finally, it generates a formatted PDF report with sentiment scores, key themes, direct quotes, and AI recommendations, emails it to the hiring manager and recruiter, updates the ATS with the reference summary and sentiment scores, and moves the candidate to Final Decision stage.", "who": "Hiring managers making final hiring decisions, recruiting coordinators managing reference check logistics, HR compliance teams needing documented reference records, and candidates who benefit from faster hiring decisions.", "impactBullets": [ "Reference check time is significantly reduced while AI detects subtle sentiment patterns humans miss, accelerating offer decisions with better documentation and red flag detection." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Speed up reference checks with AI-powered sentiment analysis to uncover red flags faster and support confident hiring decisions.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "extract-receipt-data-from-email", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-extract-receipt-data-from-email", "title": "Extract receipt data from email", "teamName": "Operations", "useCaseName": "Operations", "stage": "Accelerate", "problem": "Finance teams manually extract data from receipts submitted by employees and vendors, copying line items, totals, dates, and tax details into spreadsheets or accounting systems. The work is repetitive, prone to transcription errors, and creates a backlog that delays expense visibility and reimbursements.", "solution": "When a receipt arrives by email, the agent interprets the document, identifies which fields are relevant based on the format and content it encounters, and extracts the values that matter for the team's records. It records the structured data in a central sheet, adapting how it handles each submission based on what the document contains.", "what": "When a receipt arrives by email, the agent interprets the document, identifies which fields are relevant based on the format and content it encounters, and extracts the values that matter for the team's records. It records the structured data in a central sheet, adapting how it handles each submission based on what the document contains.", "who": "Accounting and finance teams who process employee and vendor receipts, controllers tracking expenses across the organization, and bookkeepers maintaining accurate expense records.", "impactBullets": [ "Receipt data is captured into a central sheet without manual transcription, giving the finance team a clean, organized view they can act on for reporting, reimbursement, and reconciliation." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Read incoming receipts from email, extract the relevant expense data, and record it in a central sheet for the finance team.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/41569/AzkBlsgLZMg.jpeg?v=1778821120035", "destinationUrl": "https://we.make.com/public/shared-scenario/AzkBlsgLZMg/receipt-extractor-agent", "hasSharedScenario": true }, { "slug": "update-inventory-from-chat-requests", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-update-inventory-from-chat-requests", "title": "Update inventory from chat requests", "teamName": "Operations", "useCaseName": "Operations", "stage": "Accelerate", "problem": "Operations and logistics teams manage inventory updates and order creation across disconnected spreadsheets and logistics tools, while day-to-day communication happens in chat. The gap between these systems forces manual copy-pasting, leading to stock mismatches, slow order processing, and poor auditability — making it nearly impossible to maintain an accurate, real-time view of inventory at scale.", "solution": "An intelligent inventory management agent that connects directly to logistics spreadsheets and allows team members to query stock levels, update inventory, and create orders through natural language chat commands. It bridges the gap between where the team communicates and where inventory data lives, enabling real-time logistics management without ever leaving the chat interface.", "what": "An intelligent inventory management agent that connects directly to logistics spreadsheets and allows team members to query stock levels, update inventory, and create orders through natural language chat commands. It bridges the gap between where the team communicates and where inventory data lives, enabling real-time logistics management without ever leaving the chat interface.", "who": "Operations managers, logistics coordinators, and supply chain teams who need to manage inventory and orders in real time but are slowed down by switching between chat, spreadsheets, and separate logistics tools throughout the day.", "impactBullets": [ "Eliminates the manual gap between team communication and inventory management, enabling real-time stock updates and order creation directly from chat with full auditability." ], "setupTime": "4-8 hours", "automationType": "Agentic", "marketingTagLine": "Manage stock and create orders in chat with real-time inventory updates and full auditability.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31075/8LArtHDDzgq.jpeg?v=1774591240286", "destinationUrl": "https://we.make.com/public/shared-scenario/8LArtHDDzgq/inventory-and-order-management-agent", "hasSharedScenario": true }, { "slug": "extract-invoice-data-from-email-attachments", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-extract-invoice-data-from-email-attachments", "title": "Extract invoice data from email attachments", "teamName": "Operations", "useCaseName": "Operations", "stage": "Accelerate", "problem": "Finance teams manually extract data from invoices submitted by contractors and vendors, copying line items, totals, due dates, and tax details into spreadsheets or accounting systems. The work is repetitive, prone to transcription errors, and creates a backlog that delays payment scheduling and supplier reconciliation.", "solution": "When an invoice arrives by email, the agent interprets the document, identifies which fields are relevant based on the format and content it encounters, and extracts the values that matter for the team's records. It records the structured data in a central sheet, adapting how it handles each submission based on what the invoice contains.", "what": "When an invoice arrives by email, the agent interprets the document, identifies which fields are relevant based on the format and content it encounters, and extracts the values that matter for the team's records. It records the structured data in a central sheet, adapting how it handles each submission based on what the invoice contains.", "who": "Accounts payable specialists and finance teams who process contractor and vendor invoices, controllers monitoring outgoing payments, and bookkeepers maintaining accurate supplier records.", "impactBullets": [ "Invoice data is captured into a central sheet without manual transcription, giving the finance team a clean, organized view they can act on for payment scheduling, approvals, and reconciliation." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Read incoming invoices from email, extract the relevant data, and record it in a central sheet for the finance team.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/41572/aBUYAoU508w.jpeg?v=1778822180190", "destinationUrl": "https://we.make.com/public/shared-scenario/aBUYAoU508w/invoice-extractor-agent", "hasSharedScenario": true }, { "slug": "schedule-follow-ups-from-sales-calls", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-schedule-follow-ups-from-sales-calls", "title": "Schedule follow-ups from sales calls", "teamName": "Sales", "useCaseName": "Sales", "stage": "Scale", "problem": "After sales calls, sales reps lose significant time re-listening to recordings, manually extracting decision criteria and next steps, and coordinating follow-up actions across disconnected systems. As deal volume grows, this manual process causes delayed follow-ups, missed action items, and inconsistent handoffs between sales and technical teams,creating friction at the most critical stages of the deal cycle.", "solution": "A post-call agent that extracts the transcript from a sales call and autonomously executes all follow-up actions based on the outcomes. It identifies next steps and assigns the right resources — such as a solutions architect for a technical demo or proof of concept — finds availability and schedules follow-up calls with the prospect, surfaces relevant product documentation to share, and performs a knowledge search across internal documentation and communication channels to resolve any outstanding questions. It then consolidates all outputs into a concise summary notification delivered to both the sales rep and the prospect, ensuring nothing falls through the cracks.", "what": "A post-call agent that extracts the transcript from a sales call and autonomously executes all follow-up actions based on the outcomes. It identifies next steps and assigns the right resources — such as a solutions architect for a technical demo or proof of concept — finds availability and schedules follow-up calls with the prospect, surfaces relevant product documentation to share, and performs a knowledge search across internal documentation and communication channels to resolve any outstanding questions. It then consolidates all outputs into a concise summary notification delivered to both the sales rep and the prospect, ensuring nothing falls through the cracks.", "who": "Sales reps who are responsible for managing complex deal cycles and need a reliable, automated way to execute post-call follow-ups consistently and quickly without re-listening to recordings or manually coordinating across teams and systems.", "impactBullets": [ "Eliminates manual post-call effort by automatically extracting outcomes, assigning resources, scheduling follow-ups, and notifying both the sales rep and prospect — ensuring every deal moves forward without delay or missed actions." ], "setupTime": "4-6 hours", "automationType": "Agentic", "marketingTagLine": "Turn sales calls into instant action with automated follow-ups, smart scheduling, and zero missed next steps.", "videoUrl": "https://www.loom.com/embed/b9c7173d91c24b7a97a4ae8a40f3e56c", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "identify-qualify-and-enrich-sales-leads-from-social-posts-and-add-to-your-database", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-identify-qualify-and-enrich-sales-leads-from-social-posts-and-add-to-your-database", "title": "Identify, qualify, and enrich sales leads from social posts and add to your database", "teamName": "Sales", "useCaseName": "Sales", "stage": "Scale", "problem": "Social conversations generate constant buying signals, but collecting them manually is slow and inconsistent. Teams miss relevant posts, lack enough author context to qualify intent, and struggle to turn activity into CRM-ready leads.", "solution": "Monitors social posts for specific keywords using scraping APIs, extracts post and author data, uses AI to assess relevance and intent, enriches profiles with company info, and automatically adds qualified leads to your CRM or database app.", "what": "Monitors social posts for specific keywords using scraping APIs, extracts post and author data, uses AI to assess relevance and intent, enriches profiles with company info, and automatically adds qualified leads to your CRM or database app.", "who": "Growth, marketing, demand‑gen, and SDR teams in companies with social‑led or outbound sales motions", "impactBullets": [ "Turn social buying signals into qualified, enriched CRM leads your sales team can act on immediately." ], "setupTime": "3–6 hours", "automationType": "AI Automation", "marketingTagLine": "Turn social buying signals into enriched, qualified CRM leads automatically—so your sales team can act faster.", "videoUrl": "https://www.loom.com/embed/b9c7173d91c24b7a97a4ae8a40f3e56c", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "analyze-revops-performance-and-get-recommendations", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-analyze-revops-performance-and-get-recommendations", "title": "Analyze RevOps performance and get recommendations", "teamName": "Sales", "useCaseName": "Sales", "stage": "Scale", "problem": "Sales incentives are often set with limited evidence, so SPIFFs miss the behaviors that truly drive pipeline and revenue. Teams spend time guessing payouts, resolving disputes, and piecing together impact from disconnected data sources.", "solution": "Analyzes your sales data to identify and recommend performance incentive initiatives that boost team efficiency and revenue outcomes.", "what": "Analyzes your sales data to identify and recommend performance incentive initiatives that boost team efficiency and revenue outcomes.", "who": "Revenue operations, sales leadership, sales enablement teams in any sales‑led organization", "impactBullets": [ "Help RevOps and sales leaders design SPIFFs that reliably drive the right behaviors and improve revenue outcomes, while reducing time spent on disputes and manual analysis." ], "setupTime": "6–8 hours", "automationType": "AI Automation", "marketingTagLine": "Turn sales data into smarter SPIFF recommendations that drive the right behaviors, reduce disputes, and boost revenue.", "videoUrl": "https://www.loom.com/embed/b9c7173d91c24b7a97a4ae8a40f3e56c", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "enrich-score-and-route-inbound-leads-by-ideal-customer-profile-fit", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-enrich-score-and-route-inbound-leads-by-ideal-customer-profile-fit", "title": "Enrich, score, and route inbound leads by Ideal Customer Profile fit", "teamName": "Sales", "useCaseName": "Sales", "stage": "Scale", "problem": "Inbound leads arrive from multiple campaigns and channels with fragmented company data, forcing marketing and RevOps to manually clean, qualify, and hand off records, which slows speed‑to‑lead and floods funnels with low‑fit accounts.", "solution": "Leverages Make AI tools to automate the lead qualification process by connecting your form builder, database and instant message app. Uses AI to enrich new leads with company data, scores them against your Ideal Customer Profile (ICP), and instantly routes high-scoring leads to your sales team while sending lower-scoring leads to a nurturing campaign.", "what": "Leverages Make AI tools to automate the lead qualification process by connecting your form builder, database and instant message app. Uses AI to enrich new leads with company data, scores them against your Ideal Customer Profile (ICP), and instantly routes high-scoring leads to your sales team while sending lower-scoring leads to a nurturing campaign.", "who": "RevOps, sales operations, account executives, business development representatives (BDRs). Any industry with a sales-led motion, in particular strong for startups", "impactBullets": [ "Inbound leads are consistently enriched, scored against your ICP, and routed in seconds to sales, boosting conversion and reducing manual qualification work." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn inbound leads into sales-ready opportunities with AI enrichment, ICP scoring, and instant routing.", "videoUrl": "https://drive.google.com/file/d/10-mxKCCNH0AM4F51uuMlacEyJOLFKeLE/preview", "destinationUrl": "https://www.make.com/en/login", "blueprintUrl": "https://playbook.make.com/blueprints/multi-channel-lead-processing-blueprint.json", "hasSharedScenario": false }, { "slug": "escalate-urgent-feedback-from-chat", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-escalate-urgent-feedback-from-chat", "title": "Escalate urgent feedback from chat", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Scale", "problem": "Customer support and success teams struggle to interpret and act on customer feedback arriving through messaging apps in real time. Sentiment shifts go unnoticed, follow-up questions are inconsistent across agents, urgent issues are slow to reach a human, and satisfied customers are rarely prompted to leave reviews,resulting in missed relationship-building opportunities and a fragmented, reactive customer experience.", "solution": "A conversational feedback agent that engages customers naturally through messaging apps, actively listens to feedback, interprets sentiment in real time, and asks timely, contextual follow-up questions. It automatically escalates urgent issues to a human agent when needed, and identifies satisfied customers to invite them to share a review, delivering a personalized, always-on engagement experience that strengthens customer relationships at scale.", "what": "A conversational feedback agent that engages customers naturally through messaging apps, actively listens to feedback, interprets sentiment in real time, and asks timely, contextual follow-up questions. It automatically escalates urgent issues to a human agent when needed, and identifies satisfied customers to invite them to share a review, delivering a personalized, always-on engagement experience that strengthens customer relationships at scale.", "who": "Customer success and support teams who are responsible for maintaining strong customer relationships through messaging channels and need a scalable way to engage personally, catch issues early, and turn positive experiences into public reviews without increasing headcount.", "impactBullets": [ "Transforms passive messaging feedback into active, personalized customer conversations — catching issues early, routing urgent cases instantly, and turning satisfied customers into brand advocates automatically." ], "setupTime": "8-12 hours", "automationType": "Agentic", "marketingTagLine": "Turn messaging feedback into real-time conversations that resolve urgent issues fast and convert happy customers into loyal advocates.", "videoUrl": "https://drive.google.com/file/d/1kwnLq5xilFkZAP71zGCTCpNwl7lkoYdx/preview", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "publish-content-from-one-input", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-publish-content-from-one-input", "title": "Publish content from one input", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Scale", "problem": "Marketing and content teams spend significant time manually researching topics, drafting content across multiple formats, and publishing to different platforms, often repeating the same steps for every piece. This fragmented workflow slows output, creates bottlenecks, and pulls team members away from strategy and creative direction.", "solution": "An AI-powered content orchestration agent that accepts flexible, variable inputs, from a rough topic idea to a detailed content brief, and autonomously determines the right sequence of actions to execute. It handles research, content generation, and publishing across specified platforms, adapting its workflow based on the context and intent of the input. It can be triggered directly by a user or invoked by other agents as part of a larger automation chain.", "what": "An AI-powered content orchestration agent that accepts flexible, variable inputs, from a rough topic idea to a detailed content brief, and autonomously determines the right sequence of actions to execute. It handles research, content generation, and publishing across specified platforms, adapting its workflow based on the context and intent of the input. It can be triggered directly by a user or invoked by other agents as part of a larger automation chain.", "who": "Content marketers, social media managers, and growth teams who need to produce and distribute content at scale without manually managing every step of the pipeline.", "impactBullets": [ "Reduces content production from hours of manual coordination to a single input, letting the team focus on strategy while the agent handles research, creation, and distribution autonomously." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Turn one input into researched, created, and published content automatically, freeing your team to focus on strategy.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "refine-blog-content-for-search-rankings", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-refine-blog-content-for-search-rankings", "title": "Refine blog content for search rankings", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Scale", "problem": "Marketing and content teams want to improve search rankings but can't justify the cost of an SEO agency. Blog updates happen inconsistently, keyword research is ad hoc and undisciplined, and when optimizations are made they often compromise brand voice, hurt readability, or fail to reflect actual search intent, resulting in content that neither ranks well nor resonates with readers.", "solution": "An SEO optimization agent that analyzes existing blog content in two structured steps, first auditing the content for keyword gaps, search intent alignment, and ranking opportunities, then refining the copy to improve SEO performance while preserving brand voice and readability. It delivers optimized, publish-ready blog content without the cost of an agency or the risk of content that feels over-optimized.", "what": "An SEO optimization agent that analyzes existing blog content in two structured steps, first auditing the content for keyword gaps, search intent alignment, and ranking opportunities, then refining the copy to improve SEO performance while preserving brand voice and readability. It delivers optimized, publish-ready blog content without the cost of an agency or the risk of content that feels over-optimized.", "who": "Content marketers and SEO leads who are responsible for improving organic search performance but need a scalable, consistent way to optimize blog content without sacrificing brand voice or relying on expensive external agencies.", "impactBullets": [ "Enables the marketing team to consistently optimize blog content for search rankings without compromising brand voice or readability, replacing ad hoc efforts with a repeatable, agency-quality process." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Agency-quality SEO optimization that boosts rankings while preserving brand voice, readability, and search intent.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31761/cStC5JxTTWX.jpeg?v=1774023471301", "destinationUrl": "https://we.make.com/public/shared-scenario/cStC5JxTTWX/seo-and-keyword-optimization-agent", "hasSharedScenario": true }, { "slug": "score-and-route-inbound-leads-to-sales", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-score-and-route-inbound-leads-to-sales", "title": "Score and route inbound leads to sales", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Scale", "problem": "Marketing teams collect leads from forms and landing pages, then manually judge which ones are worth pursuing and hand them to the right salesperson. This delays follow up and means strong leads sit unworked while weak ones consume attention.", "solution": "When a form or landing page submission arrives, the automation captures the lead, sends its details to an AI model that scores it against qualification criteria, and routes qualified leads to the CRM with enriched records. Sales receives prioritized, ready to work leads without manual triage.", "what": "When a form or landing page submission arrives, the automation captures the lead, sends its details to an AI model that scores it against qualification criteria, and routes qualified leads to the CRM with enriched records. Sales receives prioritized, ready to work leads without manual triage.", "who": "Demand generation and sales development teams managing inbound lead capture and qualification.", "impactBullets": [ "Inbound leads are scored and routed to the right owner the moment they arrive, with no manual triage." ], "setupTime": "2-4 Hours", "automationType": "AI Automation", "marketingTagLine": "Capture form submissions, score leads against qualification criteria, and route qualified records to the CRM for sales.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "generate-on-brand-e-commerce-product-descriptions-from-product-specifications", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-generate-on-brand-e-commerce-product-descriptions-from-product-specifications", "title": "Generate on-brand e-commerce product descriptions from product specifications", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Scale", "problem": "Product specs live in scattered sheets and systems, and marketers manually turn them into on‑brand product descriptions for each channel, leading to slow updates, style drift, and inconsistent coverage across the catalog.", "solution": "Generate product descriptions for hundreds of products in minutes - no need for copywriters. Drop your product specs and have AI handle the rest. The workflow creates consistent, on-brand copy at scale so you can launch or update large catalogs without bottlenecks.", "what": "Generate product descriptions for hundreds of products in minutes - no need for copywriters. Drop your product specs and have AI handle the rest. The workflow creates consistent, on-brand copy at scale so you can launch or update large catalogs without bottlenecks.", "who": "Digital marketing specialists & managers", "impactBullets": [ "Teams turn scattered product specs into on-brand descriptions in minutes, accelerating catalog updates and reducing manual copywriting effort." ], "setupTime": "1-2 hour", "automationType": "AI Automation", "marketingTagLine": "Turn scattered product specs into on-brand descriptions in minutes and update entire catalogs without copywriting bottlenecks.", "videoUrl": "https://www.loom.com/embed/a5e06230574a419a9eb472efa497cfc4", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "track-competitor-pricing-changes", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-competitor-pricing-changes", "title": "Track competitor pricing changes", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Scale", "problem": "Product marketing teams manually check competitor pricing pages on a recurring basis to spot changes in plans, features, or pricing tiers. This is repetitive, error-prone, and changes are often caught too late to inform positioning, packaging, or sales enablement decisions.", "solution": "An AI agent that monitors competitor pricing pages on a regular schedule, interprets page content to identify pricing structures, detects meaningful changes compared to previously tracked data, and delivers a contextualized summary of what changed and why it matters to the team via their chat platform.", "what": "An AI agent that monitors competitor pricing pages on a regular schedule, interprets page content to identify pricing structures, detects meaningful changes compared to previously tracked data, and delivers a contextualized summary of what changed and why it matters to the team via their chat platform.", "who": "Product marketers, competitive intelligence analysts, pricing strategists, and sales enablement teams who rely on up-to-date competitor pricing data.", "impactBullets": [ "Marketing teams are notified of competitor pricing changes as they happen, enabling faster responses in positioning, packaging, and sales enablement." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Monitor competitor pricing pages on a schedule, detect changes in plans or tiers, and deliver contextualized summaries to the team via chat.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/37817/MfH10jPDuMO.jpeg?v=1775663007335", "destinationUrl": "https://we.make.com/public/shared-scenario/MfH10jPDuMO/competitor-price-monitor-agent", "hasSharedScenario": true }, { "slug": "draft-prds-from-one-line-product-problems-in-your-workspace", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-prds-from-one-line-product-problems-in-your-workspace", "title": "Draft PRDs from one-line product problems in your workspace", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Scale", "problem": "PMs start planning with a vague sentence, then lose time hunting across scattered feedback and analytics. PRDs become inconsistent, miss evidence, and slow alignment on hypotheses, stories, and acceptance criteria.", "solution": "Takes a one-sentence problem (e.g., \"Users are dropping off during step 3 of onboarding\") from a PM, searches a central feedback database for related user complaints, pulls relevant product analytics, and generates a structured PRD draft. It includes user stories, hypotheses, acceptance criteria, and evidence from feedback and analytics, delivered via an instant messenger, note-taking, or project management app.", "what": "Takes a one-sentence problem (e.g., \"Users are dropping off during step 3 of onboarding\") from a PM, searches a central feedback database for related user complaints, pulls relevant product analytics, and generates a structured PRD draft. It includes user stories, hypotheses, acceptance criteria, and evidence from feedback and analytics, delivered via an instant messenger, note-taking, or project management app.", "who": "Product managers, product ops, UX teams, and growth teams in SaaS or any product‑led organisation with analytics and feedback systems in place", "impactBullets": [ "Product teams turn one‑line problem statements into structured, evidence‑backed PRDs faster, reducing time spent hunting for context and speeding up alignment." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn one-line product problems into evidence-backed PRDs fast, aligning teams with feedback, analytics, and ready-to-build requirements.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-incident-postmortems-from-chat-history", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-incident-postmortems-from-chat-history", "title": "Draft incident postmortems from chat history", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Scale", "problem": "After a technical incident, engineering and DevOps teams are left piecing together what happened from scattered tickets, system logs, and chat threads. Writing a postmortem is a manual, time-consuming process that produces inconsistent reports, causing follow-up actions to slip through the cracks, ownership to go unassigned, and technical timelines to be poorly documented or lost entirely.", "solution": "An intelligent incident postmortem agent that reviews technical incident context, pulls relevant chat history, and automatically generates a comprehensive, structured postmortem report in a shared document. It captures key timelines, action items, and owners in a consistent format, turning every incident into a documented learning opportunity without any manual write-up required.", "what": "An intelligent incident postmortem agent that reviews technical incident context, pulls relevant chat history, and automatically generates a comprehensive, structured postmortem report in a shared document. It captures key timelines, action items, and owners in a consistent format, turning every incident into a documented learning opportunity without any manual write-up required.", "who": "Engineers, DevOps leads, and site reliability engineers who are responsible for documenting incidents, running postmortems, and ensuring follow-up actions are tracked and assigned after an outage or technical failure.", "impactBullets": [ "Transforms every technical incident into a structured, actionable postmortem automatically, eliminating manual write-ups and ensuring no follow-up action or owner is ever lost." ], "setupTime": "8 hours", "automationType": "Agentic", "marketingTagLine": "Automatically turn every incident into a structured postmortem with clear timelines, owners, and action items—no manual write-up needed.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31077/e4Tq5NLhMVb.jpeg?v=1774024227121", "destinationUrl": "https://we.make.com/public/shared-scenario/e4Tq5NLhMVb/incident-postmortem-report-agent", "hasSharedScenario": true }, { "slug": "answer-product-managers-discovery-questions-from-customer-feedback", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-answer-product-managers-discovery-questions-from-customer-feedback", "title": "Answer Product Managers’ discovery questions from customer feedback", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Scale", "problem": "Customer feedback lives across tickets, surveys, interview notes, and analytics, forcing PMs and adjacent teams to manually search, tag, and piece together signals, which slows discovery and leads to decisions based on incomplete evidence.", "solution": "The Insights Hub is a RAG-based application that centralises customer feedback and enables PMs, Designers, and Growth Engineers to access insights from customer feedback in a conversational way. It dramatically reduces the time spent manually searching through tickets, surveys, and interviews.", "what": "The Insights Hub is a RAG-based application that centralises customer feedback and enables PMs, Designers, and Growth Engineers to access insights from customer feedback in a conversational way. It dramatically reduces the time spent manually searching through tickets, surveys, and interviews.", "who": "PMs, Designers, Growth Engineers, and Product leadership. Mostly relevant for product-led companies", "impactBullets": [ "Teams answer discovery questions from customer feedback faster, with less manual digging and stronger evidence behind decisions." ], "setupTime": "3-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn scattered customer feedback into instant discovery answers backed by stronger evidence.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "track-api-changes-for-engineering", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-track-api-changes-for-engineering", "title": "Track API changes for engineering", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Scale", "problem": "Engineering teams relying on multiple third-party APIs struggle to keep up with frequent spec and changelog updates, and missing a change can break product integrations. Manually monitoring vendor documentation takes significant time and pulls engineers away from development work.", "solution": "On a scheduled interval, the automation pulls a database of API integrations and their documentation/changelog links, checks each API spec and changelog for updates, detects and summarizes what changed, then notifies the responsible engineer and the broader team via email or an instant messaging channel.", "what": "On a scheduled interval, the automation pulls a database of API integrations and their documentation/changelog links, checks each API spec and changelog for updates, detects and summarizes what changed, then notifies the responsible engineer and the broader team via email or an instant messaging channel.", "who": "Product engineering teams responsible for maintaining and updating API integrations with external vendors.", "impactBullets": [ "Engineering teams learn about API changes immediately, reducing downtime risk while freeing time for core product development." ], "setupTime": "4-6 hours", "automationType": "AI Automation", "marketingTagLine": "Stay ahead of API changes with automatic monitoring, concise summaries, and instant alerts that reduce integration risk and save engineering time.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "answer-employee-policy-questions", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-answer-employee-policy-questions", "title": "Answer employee policy questions", "teamName": "People", "useCaseName": "People", "stage": "Scale", "problem": "HR teams spend hours every day answering the same repetitive questions about benefits, leave policies, and company handbook guidelines across multiple channels. Responses vary depending on who answers, policy updates frequently go uncommunicated, and employees are left waiting for information that should be instantly accessible, pulling HR away from higher-value strategic work that actually moves the business forward.", "solution": "An HR knowledge agent that instantly answers employee questions about benefits, leave policies, and company handbook guidelines with accurate, consistent responses drawn directly from up-to-date HR documentation. It handles the full volume of routine HR inquiries automatically, freeing the HR team to focus on higher-value work while ensuring every employee gets the right answer immediately.", "what": "An HR knowledge agent that instantly answers employee questions about benefits, leave policies, and company handbook guidelines with accurate, consistent responses drawn directly from up-to-date HR documentation. It handles the full volume of routine HR inquiries automatically, freeing the HR team to focus on higher-value work while ensuring every employee gets the right answer immediately.", "who": "HR managers and People Operations teams who are responsible for supporting employees across the business but are overwhelmed by the volume of repetitive, routine inquiries that consume time better spent on strategic HR initiatives.", "impactBullets": [ "Frees the HR team from repetitive routine inquiries by giving employees instant, accurate answers to benefits, leave, and policy questions — at any time, without human intervention." ], "setupTime": "6-8 Hours", "automationType": "Agentic", "marketingTagLine": "Instant HR answers for every employee, freeing your team from repetitive questions and keeping policy guidance accurate and consistent.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31738/TDnVDCR5DUF.jpeg?v=1774591098272", "destinationUrl": "https://we.make.com/public/shared-scenario/TDnVDCR5DUF/hr-slack-assistant", "hasSharedScenario": true }, { "slug": "create-personalized-ai-powered-onboarding-videos-for-new-hires", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-create-personalized-ai-powered-onboarding-videos-for-new-hires", "title": "Create personalized AI powered onboarding videos for new hires", "teamName": "People", "useCaseName": "People", "stage": "Scale", "problem": "Teams spend too much time manually collecting, copying, and updating information across tools, which causes delays, inconsistent data, and missed follow-ups when work falls through the cracks.", "solution": "The automation captures new incoming items (such as form submissions, emails, or tool events), transforms and validates the data, then automatically creates or updates the relevant records in connected apps and notifies the right stakeholders to take action.", "what": "The automation captures new incoming items (such as form submissions, emails, or tool events), transforms and validates the data, then automatically creates or updates the relevant records in connected apps and notifies the right stakeholders to take action.", "who": "Operations teams responsible for managing workflows across multiple tools, typically RevOps, Sales Ops, or Customer Operations.", "impactBullets": [ "By automating data capture, record updates, and notifications, teams move work forward faster with fewer errors and less administrative overhead." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn new hire data into personalized AI onboarding videos automatically, reducing admin work and accelerating a consistent welcome experience.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-rejection-emails-for-candidates", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-rejection-emails-for-candidates", "title": "Draft rejection emails for candidates", "teamName": "People", "useCaseName": "People", "stage": "Scale", "problem": "Recruiters manually write rejection emails for candidates, trying to personalize each message while maintaining empathy and protecting employer brand. This emotionally draining task often results in generic, delayed, or inconsistent communication that damages candidate experience. Poor communication directly impacts talent pool quality and future hiring pipeline. Manual processes also mean strong candidates who weren't the right fit this time don't get properly tagged for future opportunities.", "solution": "When a candidate status changes to rejected in the ATS, the automation captures candidate data including name, email, role applied for, stage reached, date applied, and skills summary, along with the job description and rejection reason if provided. This information is sent to an AI agent with instructions to generate a professional, empathetic rejection email that references the candidate's specific background, explains the decision when appropriate, and offers constructive feedback for advanced-stage candidates. The system uses a router to decide the next action: for early-stage rejections during resume review or initial screening, it auto-sends the email and notifies the recruiter; for late-stage rejections after interviews, it creates a draft in the recruiter's email client and sends a review notification. If the candidate achieves a fit score of 70 or higher, they are automatically tagged as a strong talent pool candidate for future opportunities. The email is logged to the ATS candidate timeline, and the recruiter receives a summary notification with candidate name, role, and confirmation that the rejection communication was sent or drafted.", "what": "When a candidate status changes to rejected in the ATS, the automation captures candidate data including name, email, role applied for, stage reached, date applied, and skills summary, along with the job description and rejection reason if provided. This information is sent to an AI agent with instructions to generate a professional, empathetic rejection email that references the candidate's specific background, explains the decision when appropriate, and offers constructive feedback for advanced-stage candidates. The system uses a router to decide the next action: for early-stage rejections during resume review or initial screening, it auto-sends the email and notifies the recruiter; for late-stage rejections after interviews, it creates a draft in the recruiter's email client and sends a review notification. If the candidate achieves a fit score of 70 or higher, they are automatically tagged as a strong talent pool candidate for future opportunities. The email is logged to the ATS candidate timeline, and the recruiter receives a summary notification with candidate name, role, and confirmation that the rejection communication was sent or drafted.", "who": "Recruiting teams, talent acquisition managers, and HR operations in companies with high-volume hiring who need to maintain quality candidate communication at scale while protecting employer brand and building talent pools.", "impactBullets": [ "Recruiters save significant time on rejection communications while every candidate receives a personalized, empathetic response quickly, improving re-application rates and automatically building talent pools of strong candidates for future opportunities." ], "setupTime": "4-8 Hours", "automationType": "AI Automation", "marketingTagLine": "Automate empathetic rejection emails to save recruiter time, protect candidate experience, and grow stronger future talent pools.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "flag-kpi-anomalies-for-owners", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-flag-kpi-anomalies-for-owners", "title": "Flag KPI anomalies for owners", "teamName": "Operations", "useCaseName": "Operations", "stage": "Scale", "problem": "Data teams have to manually monitor business KPI reports for sudden spikes or drops, often checking multiple times per day. This makes anomaly detection slow and inconsistent, increasing the risk that critical metric changes are noticed too late to prevent business issues.", "solution": "Every few minutes the automation pulls the latest KPI metrics, compares them against defined baselines to detect spikes or drops (anomalies), and when an anomaly is found it sends alerts to the relevant KPI owner via team communication channels and email, enabling the data team to proactively notify stakeholders.", "what": "Every few minutes the automation pulls the latest KPI metrics, compares them against defined baselines to detect spikes or drops (anomalies), and when an anomaly is found it sends alerts to the relevant KPI owner via team communication channels and email, enabling the data team to proactively notify stakeholders.", "who": "Data and analytics teams responsible for building reports and monitoring KPIs across business functions.", "impactBullets": [ "Automated anomaly detection helps teams react to critical KPI changes sooner, reducing the likelihood that spikes or drops turn into larger business problems." ], "setupTime": "4-6 hours", "automationType": "AI Automation", "marketingTagLine": "Detect KPI anomalies instantly and alert owners fast to prevent critical metric shifts from becoming bigger business problems.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "extract-contract-data-on-request", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-extract-contract-data-on-request", "title": "Extract contract data on request", "teamName": "Operations", "useCaseName": "Operations", "stage": "Scale", "problem": "Legal teams manually read through contracts to locate specific data points and answer questions from stakeholders. This is repetitive, time-consuming, and pulls team members away from higher-value legal work.", "solution": "An AI agent connected to the team's chat platform that receives natural language requests specifying a contract and the type of data needed. The agent reads the contract, extracts the relevant information, and summarizes findings in the conversation, handling follow-up questions with context from the same document.", "what": "An AI agent connected to the team's chat platform that receives natural language requests specifying a contract and the type of data needed. The agent reads the contract, extracts the relevant information, and summarizes findings in the conversation, handling follow-up questions with context from the same document.", "who": "Legal counsel, paralegals, contract managers, and operations staff who regularly need to extract or reference specific data from contracts.", "impactBullets": [ "Team members get answers from contracts directly in their chat platform without manually reading through documents, freeing up legal staff for higher-value work." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Receive contract queries via chat, read and extract the requested data from the document, and summarize findings with support for follow-up questions.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/37592/V0FPaGd9cez.jpeg?v=1775564310161", "destinationUrl": "https://we.make.com/public/shared-scenario/V0FPaGd9cez/contract-data-extractor-agent", "hasSharedScenario": true }, { "slug": "extract-invoice-details-from-messages-and-route-for-ap-approval", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-extract-invoice-details-from-messages-and-route-for-ap-approval", "title": "Extract invoice details from messages and route for AP approval", "teamName": "Operations", "useCaseName": "Operations", "stage": "Scale", "problem": "Invoice documents arrive across email threads and formats, forcing finance to download attachments, rekey fields, and chase budget owners for approvals. This slows payment cycles, increases errors, and reduces visibility.", "solution": "Analyze incoming emails, extract invoices, and route for approval. This reduces manual data entry and back-and-forth between finance and budget owners by automatically capturing key invoice details and sending them to the right approver.", "what": "Analyze incoming emails, extract invoices, and route for approval. This reduces manual data entry and back-and-forth between finance and budget owners by automatically capturing key invoice details and sending them to the right approver.", "who": "Operations manager", "impactBullets": [ "Invoices are automatically captured, structured, and routed to the right approver, shortening payment cycles and reducing manual data entry and errors." ], "setupTime": "2-4 hours", "automationType": "AI Automation", "marketingTagLine": "Extract invoice details from emails and route approvals automatically to cut manual work, reduce errors, and speed payment cycles.", "videoUrl": "https://www.loom.com/embed/f0381f489bbe4a00a246f93d24130a50", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "draft-personalized-outreach-for-leads", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-personalized-outreach-for-leads", "title": "Draft personalized outreach for leads", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Sales reps manually research each lead's company, dig through positioning and proof points, and write a tailored email one prospect at a time. This is slow, inconsistent across reps, and forces a tradeoff between personalization and the volume needed to keep the pipeline full.", "solution": "Working through a lead list, the agent picks up each contact that has not yet been emailed, runs live web research on their company, and pulls product positioning and proof points from uploaded knowledge files to draft a tailored subject and message guided by the rep's pitch direction. It writes the draft back to the lead record, sends through email and marks the lead when sending is enabled, and skips any lead missing an email, company, or website with a clear note explaining why.", "what": "Working through a lead list, the agent picks up each contact that has not yet been emailed, runs live web research on their company, and pulls product positioning and proof points from uploaded knowledge files to draft a tailored subject and message guided by the rep's pitch direction. It writes the draft back to the lead record, sends through email and marks the lead when sending is enabled, and skips any lead missing an email, company, or website with a clear note explaining why.", "who": "Sales development reps, account executives, and founders running outbound prospecting who need personalized emails for an entire lead list without writing each one by hand.", "impactBullets": [ "Every lead arrives with a researched, on message outreach email ready to review or already sent, letting reps reach more prospects without sacrificing personalization." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Research each lead's company, draft a tailored pitch grounded in your product knowledge, write it back to the lead record, and send when enabled.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/43303/IIqFEgnTO3E.jpeg?v=1780906911481", "destinationUrl": "https://we.make.com/public/shared-scenario/IIqFEgnTO3E/personalized-outreach-agent", "hasSharedScenario": true }, { "slug": "surface-market-signals-for-prioritization", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-surface-market-signals-for-prioritization", "title": "Surface market signals for prioritization", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Product teams waste significant time manually stitching together competitor moves, market signals, and scattered user feedback from inconsistent sources. Without a centralized, automated way to gather and synthesize this information, prioritization slows down, emerging opportunities go unnoticed, and product recommendations become harder to defend with reliable, up-to-date data.", "solution": "A product discovery agent that accelerates the discovery phase for product managers by automatically analyzing competitor activity, tracking market trends, and surfacing user feedback from an internal insights hub. Equipped with seven specialized tools, including social platform analysis, news search, and product review scraping, it synthesizes signals from across the market into a consolidated, actionable view that makes prioritization faster and recommendations easier to defend.", "what": "A product discovery agent that accelerates the discovery phase for product managers by automatically analyzing competitor activity, tracking market trends, and surfacing user feedback from an internal insights hub. Equipped with seven specialized tools, including social platform analysis, news search, and product review scraping, it synthesizes signals from across the market into a consolidated, actionable view that makes prioritization faster and recommendations easier to defend.", "who": "Product managers who are responsible for driving the product discovery process and need a faster, more reliable way to gather competitive intelligence, market signals, and user feedback without spending hours on manual research across inconsistent sources.", "impactBullets": [ "Accelerates the product discovery phase by automatically consolidating competitor activity, market trends, and user feedback into a single actionable view, enabling faster, more defensible product prioritization decisions." ], "setupTime": "4-8 hours", "automationType": "Agentic", "marketingTagLine": "Turn competitor moves, market trends, and user feedback into faster, defensible product prioritization.", "videoUrl": "https://drive.google.com/file/d/1jshLrAix0_GrIhmMADLk_RKJiE6N3U9O/preview", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/31336/D0750GLRJmP.jpeg?v=1774015153445", "destinationUrl": "https://we.make.com/public/shared-scenario/D0750GLRJmP/news-industry-insights-agent", "hasSharedScenario": true }, { "slug": "qualify-inbound-leads-against-your-icp", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-qualify-inbound-leads-against-your-icp", "title": "Qualify inbound leads against your ICP", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Sales reps manually research each inbound lead, visiting company websites and searching the web to judge how well the company fits the Ideal Customer Profile. This is slow and subjective, so strong leads sit unqualified while reps spend time on prospects that were never a good match.", "solution": "When a new lead is submitted through the intake form, the agent researches the company using live web search and weighs what it finds against the Ideal Customer Profile and disqualifiers pulled from uploaded knowledge files. It judges fit for each lead in context, then logs an ICP match score and a short evidence based reason to a spreadsheet, giving reps a ranked, qualified view of every lead.", "what": "When a new lead is submitted through the intake form, the agent researches the company using live web search and weighs what it finds against the Ideal Customer Profile and disqualifiers pulled from uploaded knowledge files. It judges fit for each lead in context, then logs an ICP match score and a short evidence based reason to a spreadsheet, giving reps a ranked, qualified view of every lead.", "who": "Sales development reps, account executives, and revenue leaders who triage inbound leads and want to prioritize outreach by fit without doing manual research.", "impactBullets": [ "Every inbound lead arrives scored and explained against your Ideal Customer Profile, letting reps focus their time on the prospects most likely to convert." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Research each inbound lead's company, score its fit against your Ideal Customer Profile, and log the score with a supporting reason.", "destinationUrl": "https://we.make.com/public/shared-scenario/LEsubqrg0cA/lead-qualification-agent", "hasSharedScenario": true }, { "slug": "diagnose-why-deals-are-lost", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-diagnose-why-deals-are-lost", "title": "Diagnose why deals are lost", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Sales teams rarely have time to dig into why deals are lost, so closed lost opportunities pile up in the CRM without review. Pulling the records, researching each company, and spotting common threads across deals is slow manual work, which means recurring loss patterns and competitive threats go unnoticed.", "solution": "The agent investigates recently lost opportunities based on the conditions you define, retrieving each opportunity and its related account from the CRM. It runs live web research to add company and competitor context, decides what is worth digging into for each deal, and looks for patterns across them by industry, size, source, loss reason, and competitor. It then compiles an executive summary, per deal deep dives, cross deal patterns, and recommended next steps into a shareable document.", "what": "The agent investigates recently lost opportunities based on the conditions you define, retrieving each opportunity and its related account from the CRM. It runs live web research to add company and competitor context, decides what is worth digging into for each deal, and looks for patterns across them by industry, size, source, loss reason, and competitor. It then compiles an executive summary, per deal deep dives, cross deal patterns, and recommended next steps into a shareable document.", "who": "Sales leaders, revenue operations, and account executives who want to understand why deals are lost and act on competitive and pattern based insights.", "impactBullets": [ "Lost deals are continuously analyzed and turned into clear patterns and recommendations, helping the sales team understand why they lose and improve future win rates." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Pull lost opportunities and their accounts, research each company and competitor, find patterns across deals, and compile findings with recommendations into a shareable document.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/44439/hmzKaiVqaH6.jpeg?v=1782117105249", "destinationUrl": "https://we.make.com/public/shared-scenario/hmzKaiVqaH6/lost-deals-analysis-agent", "hasSharedScenario": true }, { "slug": "research-accounts-before-outreach", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-research-accounts-before-outreach", "title": "Research accounts before outreach", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Sales reps manually research every target company before outreach or meetings, searching the web for an overview, products, business model, recent news, and competitors, then writing it all up. This is slow and repetitive, and the depth and formatting vary from rep to rep, so reps often go into conversations underprepared.", "solution": "Working through a list of companies, the agent picks up any that have not been researched yet and runs live web research to gather the specific details the team asks for, judging what is relevant for each company as it goes. It compiles the findings into a clean, consistently structured brief, creates a document for it, and writes the link back to the company record so every account has a ready brief.", "what": "Working through a list of companies, the agent picks up any that have not been researched yet and runs live web research to gather the specific details the team asks for, judging what is relevant for each company as it goes. It compiles the findings into a clean, consistently structured brief, creates a document for it, and writes the link back to the company record so every account has a ready brief.", "who": "Sales development reps, account executives, and business development teams who research target accounts before outreach, discovery calls, or meetings.", "impactBullets": [ "Every company on the list arrives with a thorough, consistently formatted research brief, letting reps walk into conversations prepared without doing manual research." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Research each company on your list, compile the details you ask for into a structured brief, create a document, and link it back to the company record.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/43310/DyHa6qYrnhj.jpeg?v=1780911351476", "destinationUrl": "https://we.make.com/public/shared-scenario/DyHa6qYrnhj/company-research-agent", "hasSharedScenario": true }, { "slug": "brief-reps-before-sales-calls", "teamSlug": "sales", "useCaseSlug": "sales", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-brief-reps-before-sales-calls", "title": "Brief reps before sales calls", "teamName": "Sales", "useCaseName": "Sales", "stage": "Lead", "problem": "Before a call, reps manually dig through the CRM to piece together an account's history, open pipeline, and recent deals, then try to answer their own questions about stage, use cases, and competitors. This research eats into selling time, and reps walk into conversations with uneven levels of preparation.", "solution": "Given an account name and a set of questions, the agent locates the account in the CRM, pulls its record along with related open and recently closed opportunities, and decides what else it needs to retrieve to answer each question. Working read only, it reasons across the records and returns a structured brief with an executive summary, account snapshot, pipeline overview, and direct answers that cite the records behind them.", "what": "Given an account name and a set of questions, the agent locates the account in the CRM, pulls its record along with related open and recently closed opportunities, and decides what else it needs to retrieve to answer each question. Working read only, it reasons across the records and returns a structured brief with an executive summary, account snapshot, pipeline overview, and direct answers that cite the records behind them.", "who": "Account executives, sales development reps, and sales leaders who need to prepare for account conversations quickly and consistently.", "impactBullets": [ "Every rep walks into a conversation equally prepared with an on demand account brief, without spending time on manual CRM research." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Find an account in the CRM, pull its record and related opportunities, and answer your questions in a structured, cited brief.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/44445/cyZKxf8oPuQ.jpeg?v=1782116980761", "destinationUrl": "https://we.make.com/public/shared-scenario/cyZKxf8oPuQ/account-researcher-agent", "hasSharedScenario": true }, { "slug": "answer-customer-support-questions-from-the-knowledge-base", "teamSlug": "customer-experience", "useCaseSlug": "customer-experience", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-answer-customer-support-questions-from-the-knowledge-base", "title": "Answer customer support questions from the knowledge base", "teamName": "Customer Experience", "useCaseName": "Customer Experience", "stage": "Lead", "problem": "Teams store policies, FAQs, and SOPs across docs and wikis, so employees and customers ask repetitive questions. Search is inconsistent, answers drift, and support and ops spend time chasing sources.", "solution": "Indexes your internal knowledge (docs, FAQs, SOPs) to deliver fast, accurate self-service answers. It processes content, handles queries through AI, finds relevant context, and returns responses via chat widget, instant messenger, or API.", "what": "Indexes your internal knowledge (docs, FAQs, SOPs) to deliver fast, accurate self-service answers. It processes content, handles queries through AI, finds relevant context, and returns responses via chat widget, instant messenger, or API.", "who": "Customer support teams, CX managers and operations teams", "impactBullets": [ "Help support and ops teams answer questions faster and more consistently by turning scattered docs into a trusted, searchable Q&A layer" ], "setupTime": "2–4 hours", "automationType": "AI Automation", "marketingTagLine": "Turn scattered docs into trusted AI answers that resolve support questions faster and more consistently.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false }, { "slug": "fix-page-issues-to-grow-organic-visibility", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-fix-page-issues-to-grow-organic-visibility", "title": "Fix page issues to grow organic visibility", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Lead", "problem": "Marketing teams manually audit web pages, track keyword positions, update meta tags, and check for technical issues across a growing site. This work is repetitive and never finished, so important fixes slip and pages lose visibility in search and generative engines.", "solution": "An AI agent reviews pages and search performance, interprets what is causing visibility gaps, and decides which action to take for each page, whether updating metadata, flagging a technical problem, or recommending content changes. It adapts its checks to the specific issues it finds and reports the fixes it applied or proposed.", "what": "An AI agent reviews pages and search performance, interprets what is causing visibility gaps, and decides which action to take for each page, whether updating metadata, flagging a technical problem, or recommending content changes. It adapts its checks to the specific issues it finds and reports the fixes it applied or proposed.", "who": "SEO specialists, content marketers, and growth teams responsible for organic search and discoverability across large websites.", "impactBullets": [ "Pages stay continuously optimized for search and generative engines without analysts manually auditing every issue." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Audit pages, diagnose visibility gaps, and apply or recommend the right SEO fixes for each page.", "hasSharedScenario": false }, { "slug": "draft-on-brand-replies-to-social-comments", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-draft-on-brand-replies-to-social-comments", "title": "Draft on-brand replies to social comments", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Lead", "problem": "Marketing teams monitor comments across many social channels and try to respond in the brand voice while keeping up with volume. Reading every comment, judging its intent, and drafting appropriate replies by hand does not scale and leads to slow or missed responses.", "solution": "An AI agent monitors comments across social channels, evaluates the context and sentiment of each one, and drafts suggested responses in the brand voice for the team to review and send. It decides how to handle each comment based on what it interprets rather than following a single reply template.", "what": "An AI agent monitors comments across social channels, evaluates the context and sentiment of each one, and drafts suggested responses in the brand voice for the team to review and send. It decides how to handle each comment based on what it interprets rather than following a single reply template.", "who": "Social media managers and community teams handling engagement across multiple platforms.", "impactBullets": [ "The team responds to social comments quickly and in a consistent voice without reading and drafting every reply manually." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Monitor social comments, assess context and sentiment, and suggest on brand responses for the team to send.", "hasSharedScenario": false }, { "slug": "generate-on-topic-content-for-every-channel", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-generate-on-topic-content-for-every-channel", "title": "Generate on-topic content for every channel", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Lead", "problem": "Marketing teams repeat the same cycle for every piece of content, researching topics, drafting across formats, and publishing to each channel. Doing this by hand limits how much quality content the team can ship and slows its response to market shifts.", "solution": "An AI agent monitors the market for relevant topics, decides which ideas are worth pursuing, and generates content tailored to each format and channel before publishing where specified. It chooses its approach based on the topic and audience rather than following one fixed template.", "what": "An AI agent monitors the market for relevant topics, decides which ideas are worth pursuing, and generates content tailored to each format and channel before publishing where specified. It chooses its approach based on the topic and audience rather than following one fixed template.", "who": "Content marketing, social, and brand teams producing and distributing content across multiple channels.", "impactBullets": [ "The team ships more relevant content across channels without manually researching and drafting every piece." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Monitor the market for topics, generate content for each format, and publish across the right channels.", "hasSharedScenario": false }, { "slug": "rewrite-copy-to-a-consistent-brand-voice", "teamSlug": "marketing", "useCaseSlug": "marketing", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-rewrite-copy-to-a-consistent-brand-voice", "title": "Rewrite copy to a consistent brand voice", "teamName": "Marketing", "useCaseName": "Marketing", "stage": "Lead", "problem": "Marketing teams receive copy from many contributors, each with a different tone and style, and must manually rewrite it to match brand guidelines before anything ships. Reviewing every input for voice consistency is slow and easy to get wrong across many campaigns and channels.", "solution": "An AI agent takes scattered inputs and rewrites them into clear, consistent, on brand copy, judging where tone drifts and how to correct it for each piece. It adapts its edits to the channel and context so messaging stays aligned before publication.", "what": "An AI agent takes scattered inputs and rewrites them into clear, consistent, on brand copy, judging where tone drifts and how to correct it for each piece. It adapts its edits to the channel and context so messaging stays aligned before publication.", "who": "Brand, content, and communications teams responsible for tone consistency across campaigns and channels.", "impactBullets": [ "Published copy stays consistent and on brand regardless of who wrote the original input." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Take scattered copy inputs, correct tone and style, and produce consistent on brand content before publication.", "hasSharedScenario": false }, { "slug": "triage-backlog-tickets-to-owners", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-triage-backlog-tickets-to-owners", "title": "Triage backlog tickets to owners", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Lead", "problem": "Engineering and IT teams accumulate unassigned tickets in the backlog because team leads must manually scan submissions, interpret context, and decide on the right owner before work can begin. This creates delays in ticket pickup, uneven workload distribution across the team, and a backlog that never feels clean or actionable.", "solution": "On a recurring schedule, an agent reviews unassigned tickets in the backlog, interprets the context of each one, and weighs it against team assignment criteria to identify the most appropriate owner. It assigns each ticket to that team member and moves its status from backlog to active, keeping the queue continuously triaged without requiring manual oversight.", "what": "On a recurring schedule, an agent reviews unassigned tickets in the backlog, interprets the context of each one, and weighs it against team assignment criteria to identify the most appropriate owner. It assigns each ticket to that team member and moves its status from backlog to active, keeping the queue continuously triaged without requiring manual oversight.", "who": "IT and engineering team leads, project managers, and engineering managers responsible for backlog hygiene, balanced workload, and timely ticket pickup across the team.", "impactBullets": [ "The ticket backlog is continuously triaged and routed to the right owners without manual scanning, so work is picked up faster and distributed more evenly across the team." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Review unassigned tickets on a schedule, assign each to the most appropriate owner, and update the ticket status to active.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/37018/BxAm6kZQw7v.jpeg?v=1775025980528", "destinationUrl": "https://we.make.com/public/shared-scenario/BxAm6kZQw7v/ticket-triage-agent", "hasSharedScenario": true }, { "slug": "route-access-requests-to-owners", "teamSlug": "information-technology", "useCaseSlug": "information-technology", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-route-access-requests-to-owners", "title": "Route access requests to owners", "teamName": "Information Technology", "useCaseName": "Information Technology", "stage": "Lead", "problem": "IT teams manually field a constant stream of access requests from employees, identifying the right tool or resource owner and connecting the two parties for each request. This creates a bottleneck for IT, slows down employees waiting on access, and is impossible to sustain as the organization grows.", "solution": "An AI agent that interprets access requests sent through chat, identifies the specific app or resource being requested even when described informally, and locates the appropriate owner from an internal registry. The agent then routes the request to that owner with the relevant context, adapting its interpretation and matching approach to the wording, scope, and ambiguity of each unique request.", "what": "An AI agent that interprets access requests sent through chat, identifies the specific app or resource being requested even when described informally, and locates the appropriate owner from an internal registry. The agent then routes the request to that owner with the relevant context, adapting its interpretation and matching approach to the wording, scope, and ambiguity of each unique request.", "who": "IT teams handling employee access provisioning, tool and resource owners responsible for approving access, and employees who need fast access to the apps required to do their work.", "impactBullets": [ "Access requests are interpreted, routed to the right owner, and resolved through chat, removing the IT team as a manual middleman and getting employees to the resources they need faster." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Interpret employee access requests from chat, identify the relevant resource and its owner, and route the request to the owner for approval.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/40571/c5jo8Yv7sZM.jpeg?v=1777885527224", "destinationUrl": "https://we.make.com/public/shared-scenario/c5jo8Yv7sZM/access-request-agent", "hasSharedScenario": true }, { "slug": "shortlist-candidates-from-resume-submissions", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-shortlist-candidates-from-resume-submissions", "title": "Shortlist candidates from resume submissions", "teamName": "People", "useCaseName": "People", "stage": "Lead", "problem": "Recruiting teams read every inbound resume by hand, judging each one against the role's criteria before deciding who advances. When applications pile up, screening becomes slow and the bar drifts from reviewer to reviewer and day to day. Strong candidates get buried in the queue and accept other offers, and there is rarely a documented record of why anyone was passed over.", "solution": "The agent picks up each submitted application, retrieves the attached resume, and evaluates it against the hiring rubric and criteria held in its knowledge base. It reasons about how the candidate's experience maps to the role, produces an evidence based fit score with a summary, key strengths, and key gaps, and decides whether the candidate clears the bar for the team's attention. Every assessment is recorded for comparison, and standout applicants are surfaced to the hiring team in real time.", "what": "The agent picks up each submitted application, retrieves the attached resume, and evaluates it against the hiring rubric and criteria held in its knowledge base. It reasons about how the candidate's experience maps to the role, produces an evidence based fit score with a summary, key strengths, and key gaps, and decides whether the candidate clears the bar for the team's attention. Every assessment is recorded for comparison, and standout applicants are surfaced to the hiring team in real time.", "who": "Recruiters, talent acquisition teams, and hiring managers who screen high volumes of inbound applications and want a consistent, criteria driven shortlist.", "impactBullets": [ "Every applicant is evaluated against the same rubric and the strongest fits are surfaced to the hiring team in real time, replacing hours of manual resume reading with a consistent, defensible shortlist." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Retrieve each submitted resume, assess it against the hiring rubric, score candidate fit, and surface standout applicants to the hiring team.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/42825/XMT7FkrRc0I.jpeg?v=1780290773329", "destinationUrl": "https://we.make.com/public/shared-scenario/XMT7FkrRc0I/resume-analysis-agent", "hasSharedScenario": true }, { "slug": "schedule-interviews-across-candidate-availability", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-schedule-interviews-across-candidate-availability", "title": "Schedule interviews across candidate availability", "teamName": "People", "useCaseName": "People", "stage": "Lead", "problem": "Recruiting coordinators manually gather availability from candidates and interviewers, then cross reference everyone's calendars to find a slot that works for the whole panel. The constant back and forth is slow and prone to conflicts, and strong candidates often drop off before a time can be locked in.", "solution": "An AI agent works through candidates who still need an interview, interpreting each one's required attendees and reading their calendar availability to determine the best mutual meeting time within business hours. It then books the meeting with a video link, invites every attendee, and updates the tracking record so the pipeline keeps moving without manual coordination.", "what": "An AI agent works through candidates who still need an interview, interpreting each one's required attendees and reading their calendar availability to determine the best mutual meeting time within business hours. It then books the meeting with a video link, invites every attendee, and updates the tracking record so the pipeline keeps moving without manual coordination.", "who": "Recruiting coordinators, talent acquisition teams, and hiring managers who schedule multi attendee interviews across high volume hiring pipelines.", "impactBullets": [ "Interviews are booked at the earliest time that works for everyone without manual coordination, keeping candidates engaged and freeing recruiters to focus on sourcing and candidate experience." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Find mutual availability across candidates and interviewers, book the meeting with a video link, and update the recruiting tracker.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/42823/QHdNyBCI6qP.jpeg?v=1780289539032", "destinationUrl": "https://we.make.com/public/shared-scenario/QHdNyBCI6qP/interview-scheduler-agent", "hasSharedScenario": true }, { "slug": "answer-onboarding-questions-from-new-hires", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-answer-onboarding-questions-from-new-hires", "title": "Answer onboarding questions from new hires", "teamName": "People", "useCaseName": "People", "stage": "Lead", "problem": "HR teams manually field recurring questions from employees about company policies, processes, and culture, pulling information from scattered documents and systems to craft responses. As headcount grows, this repetitive question handling becomes a constant interruption that pulls HR away from strategic work and slows down employees waiting for answers.", "solution": "An AI agent that interprets each employee question, searches the company knowledge base for relevant context, and responds with accurate answers tailored to the inquiry. The agent adapts its approach for each conversation, deciding which sources to consult, how to handle ambiguous requests, and how to address follow up questions within the same thread.", "what": "An AI agent that interprets each employee question, searches the company knowledge base for relevant context, and responds with accurate answers tailored to the inquiry. The agent adapts its approach for each conversation, deciding which sources to consult, how to handle ambiguous requests, and how to address follow up questions within the same thread.", "who": "HR and People Operations teams who field employee inquiries, and new and existing employees seeking quick answers about company policies, benefits, processes, and culture.", "impactBullets": [ "Employees receive immediate accurate answers to their company questions while HR teams reclaim time previously spent on repetitive knowledge requests." ], "setupTime": "4-8 Hours", "automationType": "Agentic", "marketingTagLine": "Interpret employee questions, search the company knowledge base, and deliver tailored answers with support for follow up inquiries.", "scenarioImageUrl": "https://cdn.make.com/scenario-sharing/we-make-com/thumbnails/39221/z99aH8HLoL2.jpeg?v=1776775807111", "destinationUrl": "https://we.make.com/public/shared-scenario/z99aH8HLoL2/hr-onboarding-qn-a-agent", "hasSharedScenario": true }, { "slug": "score-interviews-with-transcript-evidence", "teamSlug": "people", "useCaseSlug": "people", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-score-interviews-with-transcript-evidence", "title": "Score interviews with transcript evidence", "teamName": "People", "useCaseName": "People", "stage": "Lead", "problem": "Interviewers manually complete scorecards after each interview by recalling the conversation and writing supporting evidence for every competency. Feedback often gets delayed or skipped, and the depth and rigor of assessments vary from one interviewer to the next. This slows hiring decisions and makes candidates harder to compare fairly.", "solution": "An AI agent that locates the right interview from a date and a candidate detail, interprets the linked meeting transcript, and decides how to answer each scorecard question with concrete supporting evidence. It adapts to the structure of each scorecard template and flags assumptions or open questions where the transcript is inconclusive, producing a completed scorecard ready for review.", "what": "An AI agent that locates the right interview from a date and a candidate detail, interprets the linked meeting transcript, and decides how to answer each scorecard question with concrete supporting evidence. It adapts to the structure of each scorecard template and flags assumptions or open questions where the transcript is inconclusive, producing a completed scorecard ready for review.", "who": "Interviewers, hiring managers, and recruiters who evaluate candidates and need consistent, evidence backed interview feedback.", "impactBullets": [ "Interview scorecards are completed with evidence backed assessments shortly after each interview, giving hiring teams consistent, comparable feedback for faster decisions." ], "setupTime": "2-4 Hours", "automationType": "Agentic", "marketingTagLine": "Find the right interview, read its transcript, and complete the scorecard with evidence for each competency.", "destinationUrl": "https://we.make.com/public/shared-scenario/0LC6wPCCZUm/interview-scorecard-agent", "hasSharedScenario": true }, { "slug": "validate-expense-reports-against-policy", "teamSlug": "operations", "useCaseSlug": "operations", "canonicalUrl": "https://playbook.make.com/dashboard#use-case-validate-expense-reports-against-policy", "title": "Validate expense reports against policy", "teamName": "Operations", "useCaseName": "Operations", "stage": "Lead", "problem": "Finance teams manually review every expense report to verify receipts match transactions, check policy compliance on spending limits and eligible categories, and catch duplicate submissions or missing documentation. This creates bottlenecks during busy periods and results in inconsistent policy enforcement and delayed reimbursements.", "solution": "An AI agent that analyzes expense reports by interpreting receipt images, cross-referencing company policies to determine compliance, and making context-dependent decisions about approval based on variables like expense type, amount thresholds, historical patterns, and policy edge cases—adapting its validation approach to each unique submission rather than following fixed rules.", "what": "An AI agent that analyzes expense reports by interpreting receipt images, cross-referencing company policies to determine compliance, and making context-dependent decisions about approval based on variables like expense type, amount thresholds, historical patterns, and policy edge cases—adapting its validation approach to each unique submission rather than following fixed rules.", "who": "Finance and accounting teams who process expense reports, finance managers and controllers enforcing spending policies, and employees who want faster reimbursements with fewer corrections.", "impactBullets": [ "Expense reports are validated instantly against policy rules, significantly reducing manual review time, enabling faster reimbursement for compliant submissions, and improving compliance consistency across the organization." ], "setupTime": "4-6 hours", "automationType": "Agentic", "marketingTagLine": "Instantly validate expense reports with AI to speed reimbursements, reduce manual review, and enforce policy consistently.", "destinationUrl": "https://www.make.com/en/login", "hasSharedScenario": false } ] }