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. It is published by Make, the visual automation platform used by 500,000+ organizations to build AI agents and automated workflows.
Every use case below describes the problem it solves, who it is for, the impact it delivers, and the Make scenario you can deploy to implement it. Use cases are organised by team and by AI maturity stage.
Total use cases: 93. Teams represented: 8.
Sales
AI automation use cases for Sales teams. 19 solutions available.
Research prospects on demand
Turn your chat into an AI sales assistant and company research tool that builds a prospect profile from the web, business directories, and data providers, then answers follow up questions with tailored insights.
- Team
- Sales
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Account executives preparing for prospect engagements and sales outreach.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When an account executive sends a company name or domain in the team chat, an AI agent searches web sources, business directories, and data providers, choosing its next source based on what it has already found, and compiles a prospect profile. It replies in the chat with a conversational summary and answers follow up questions in the same thread.
Impact
- Account executives go into every engagement already briefed on the prospect, so outreach goes out sooner and lands with sharper messaging.
Generate sales enablement decks from account and pipeline data
Auto-generate tailored sales decks from pipeline data to cut prep time, ensure consistency, and keep collateral current.
- Team
- Sales
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Sales teams, account executives, solution engineers, and sales enablement teams
The 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.
The 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.
Impact
- Automated deck generation cuts prep time, ensures consistent messaging, and keeps sales collateral current.
Create sales follow-up sequences for every lead stage
Event-driven follow-up sequences deliver personalized outreach faster, improving conversions at every lead stage.
- Team
- Sales
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Sales‑led organizations — especially SDR, BDR, and Account Executive teams
The 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.
The 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.
Impact
- Event-driven follow-up sequences improve speed, personalization, and conversion across every lead stage.
Identify sales signals from job posts
Turn job posts into sales signals to spot real AI and automation buying intent faster.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- RevOps, sales operations, account executives and BDRs
The 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.
The 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.
Impact
- Help sales teams prioritize and qualify opportunities using hiring signals that reveal real transformation intent.
Coach reps from call transcripts
Get AI sales coaching after every call with AI call analysis that surfaces strengths and gaps in the transcript against prospecting best practices and logs insights to a performance tracker.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- 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 coaching across the team.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a sales rep shares a call transcript through the team chat, an AI agent analyzes the conversation against prospecting best practices from internal playbooks and the web, decides which strengths and gaps matter most for that call, sends the rep tailored feedback, and logs the insights to a performance tracker.
Impact
- Every call gets reviewed against prospecting best practices, so reps receive feedback while the conversation is still fresh and improve between calls instead of skipping self review.
Turn PDF order forms into structured data in minutes to accelerate intake and eliminate manual entry.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Any industry where order forms are received
The 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.
The 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.
Impact
- Teams get structured order data from PDFs in minutes, accelerating order intake and reducing manual data entry into downstream systems.
Create and share pre-call research briefs for upcoming meetings
Never walk into meetings cold with AI-generated pre-call briefs delivered automatically before every conversation.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Account executives, sales managers, solution engineers, and BDRs in any sales‑led organisation with recurring customer or prospect calls
The 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.
The 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.
Impact
- Automated pre-call briefs give reps consistent account context, reduce prep time, and improve meeting outcomes.
Draft personalized replies for inbound leads
Set up automated lead response with an AI sales assistant that researches each new lead, drafts a personalized reply, and proposes meeting times from the rep's calendar.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Sales representatives and business development managers who are responsible for responding to inbound leads quickly and personally.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a new inbound lead is created in the CRM, an AI agent researches the prospect and their company on the web, decides which details are relevant, and drafts a personalized email reply. It checks the assigned rep's calendar for upcoming open slots, adds suggested meeting times to the draft, and saves it in the rep's inbox for review.
Impact
- Inbound leads hear back with a relevant reply and a clear next step while their interest is fresh, so fewer opportunities go cold before the first conversation.
Summarize pipeline health for sales managers
Run an AI sales pipeline analysis every morning that rates each open deal red for stagnating or green for active, then emails sales managers one summary for clearer deal tracking.
- Team
- Sales
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Sales managers and revenue operations leads who oversee pipeline health across many open deals and need one daily summary to decide where to coach, intervene, or shift the team's focus.
- Shared scenario
- Make.com shared scenario
The problem
Sales managers have no consistent daily view of pipeline health. Building one means reading through deal notes, tasks, and email threads across the CRM to judge which opportunities are stalling and which are moving. That review rarely happens every day, so stagnating deals go unnoticed while active prospects get buried, and managers step in too late.
The solution
Every morning, an AI agent pulls all open deals from the CRM and reviews each one, deciding which notes, tasks, and email threads to check before rating it red for stagnating, such as no recent progress or a passed close date, or green for high activity, with a short reason. It compiles the ratings into one traffic light report, grouped by status with summary counts at the top, and emails it to the sales managers.
Impact
- Sales managers start each day knowing which deals are stalling and which are moving, so they intervene while deals can still be saved and keep the team focused on live prospects.
Schedule follow ups after sales calls
Turn every call transcript into next steps with an AI sales assistant that assigns the right experts, books follow up calls, shares relevant documentation, and sends automated follow up emails to keep your sales follow up process moving.
- Team
- Sales
- AI maturity stage
- Scale
- Automation type
- Agentic
- Who it is for
- Account executives and sales reps running complex deal cycles, plus the solutions architects who join technical follow ups.
The problem
After sales calls, sales reps lose significant time listening back 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.
The solution
When a sales call ends and its transcript is available in the call recording platform, an AI agent pulls the transcript and identifies the outcomes and next steps. It decides which resources to assign, such as a solutions architect for a technical demo, checks availability and schedules the follow up call with the prospect, searches product documentation and internal channels to answer open questions, then sends a summary to the rep and the prospect.
Impact
- Deals keep moving after every call with next steps owned and the right experts involved, instead of stalling on delayed follow ups and forgotten commitments.
Identify, qualify, and enrich sales leads from social posts and add to your database
Turn social buying signals into enriched, qualified CRM leads automatically—so your sales team can act faster.
- Team
- Sales
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Growth, marketing, demand‑gen, and SDR teams in companies with social‑led or outbound sales motions
The 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.
The 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.
Impact
- Turn social buying signals into qualified, enriched CRM leads your sales team can act on immediately.
Turn sales data into smarter SPIFF recommendations that drive the right behaviors, reduce disputes, and boost revenue.
- Team
- Sales
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Revenue operations, sales leadership, sales enablement teams in any sales‑led organization
The 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.
The solution
Analyzes your sales data to identify and recommend performance incentive initiatives that boost team efficiency and revenue outcomes.
Impact
- 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.
Enrich, score, and route inbound leads by Ideal Customer Profile fit
Turn inbound leads into sales-ready opportunities with AI enrichment, ICP scoring, and instant routing.
- Team
- Sales
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- RevOps, sales operations, account executives, business development representatives (BDRs). Any industry with a sales-led motion, in particular strong for startups
- Blueprint download
- Make.com blueprint JSON
The 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.
The 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.
Impact
- Inbound leads are consistently enriched, scored against your ICP, and routed in seconds to sales, boosting conversion and reducing manual qualification work.
Draft personalized outreach for leads
Run an AI SDR that works through your lead list, researches each company on the web, drafts personalized cold emails grounded in your product knowledge, and switches on cold email automation when you are ready to send.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a set schedule, an AI agent picks up each lead not yet emailed from the lead list, 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 it by email and marks the lead when sending is enabled, and skips any lead missing an email, company, or website with a note explaining why.
Impact
- Reps reach every prospect on their list with researched, on message outreach, so personalization no longer comes at the cost of pipeline volume.
Qualify inbound leads against your ICP
Automate inbound lead qualification by researching each company on the web, weighing it against your ICP with AI lead scoring, and logging a fit score with an evidence based reason.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Sales development reps, account executives, and revenue leaders who triage inbound leads and want to prioritize outreach by fit without doing manual research.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a new lead is submitted through the intake form, an AI 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.
Impact
- Every inbound lead arrives scored and explained against your Ideal Customer Profile, letting reps focus their time on the prospects most likely to convert.
Fill missing details on new leads
Run waterfall enrichment with an agent that picks the right data enrichment tool for each missing detail, moves on when one comes back empty, and writes the results back with their source for complete CRM data enrichment.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Sales operations and revenue operations teams responsible for CRM data quality, and the account executives and sales development reps who work new leads.
- Shared scenario
- Make.com shared scenario
The problem
Leads often land in the CRM with little more than a name and email. Sales operations teams fill the gaps by hand, checking one enrichment tool, then another when the first comes back empty, because no single source covers every lead. Records sit incomplete and reps work them without knowing who they are calling or what the company does.
The solution
An AI agent picks up each lead as it is added to the CRM, works out which details are missing, and draws on a set of enrichment tools to fill them. It chooses which source to query based on the gaps and earlier results, moving on to another tool when one comes back empty. It writes the details it finds back to the lead and notes which source each one came from, flagging any lead it could not complete.
Impact
- New leads arrive as complete as the available sources allow, so reps can work them straight away instead of researching each one by hand.
Diagnose why deals are lost
Run win loss analysis on your recently lost opportunities by researching each account and competitor, uncovering closed lost reasons and patterns across deals, and compiling recommendations into a shareable report.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Sales leaders, revenue operations, and account executives who want to understand why deals are lost and act on competitive and pattern based insights.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a set schedule, an AI agent investigates recently lost opportunities that match the team's defined conditions, 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 finds patterns 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.
Impact
- 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.
Research accounts before outreach
Use a company research tool that runs live web research on each target account, compiles a consistent company research report, and links the document back to the record for sales call preparation.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Sales development reps, account executives, and business development teams who research target accounts before outreach, discovery calls, or meetings.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a set schedule, an AI agent works through the list of companies in the company database, 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 consistently structured brief, creates a document for it, and writes the link back to the company record.
Impact
- Every company on the list arrives with a thorough, consistently formatted research brief, letting reps walk into conversations prepared without doing manual research.
Brief reps before sales calls
Speed up pre call planning with an AI sales assistant that finds the account in your CRM, pulls its record and related opportunities, and answers your questions in a structured brief with cited records.
- Team
- Sales
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Account executives, sales development reps, and sales leaders who need to prepare for account conversations quickly and consistently.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a rep sends an account name and questions in team chat, an AI 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 to the rep in team chat with an executive summary, account snapshot, pipeline overview, and direct answers that cite the records behind them.
Impact
- Every rep walks into a conversation equally prepared with an on demand account brief, without spending time on manual CRM research.
Customer Experience
AI automation use cases for Customer Experience teams. 9 solutions available.
Draft, update, and publish customer support knowledge base articles
Keep support docs accurate and effortless with automated drafting, updates, and publishing for every product change.
- Team
- Customer Experience
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Ideal for SaaS companies, support teams, product teams, and any organisation needing up‑to‑date internal or external documentation
The 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.
The 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.
Impact
- Help support teams keep customer-facing documentation accurate, consistent, and easier to maintain.
Escalate overdue support tickets
Add SLA monitoring and SLA tracking to your support queue, catching tickets left unresolved past your threshold and sending SLA alerts with status, assignee, and priority to the right team.
- Team
- Customer Experience
- AI maturity stage
- Build
- Automation type
- Deterministic
- Who it is for
- Customer support teams and support operations responsible for managing ticket queues and coordinating with internal stakeholders.
The problem
Support leads manually scan the queue for tickets stuck waiting on other teams and chase each one by hand. 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.
The solution
On an hourly schedule, the automation checks the support ticketing system for tickets still unresolved past a defined time threshold, captures the ticket ID, status, assignee, priority, and the team it is waiting on, and posts an alert with those details to that team's chat channel while notifying the assignee directly.
Impact
- Tickets waiting on other teams get unblocked before deadlines pass, so customers stop waiting on stalled requests and service commitments are kept.
Resolve support inquiries from knowledge base
Power your knowledge base chatbot with an AI customer service agent that searches internal documentation, adds web research when the docs fall short, and returns consistent answers without escalation.
- Team
- Customer Experience
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Customer support agents handling inbound inquiries, support managers looking to reduce escalations, and customers who benefit from faster and more consistent responses.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a customer sends a question through the support chatbot, an AI agent determines how to resolve it, querying the internal knowledge base to find relevant documentation, and 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, then replies to the customer in the chat.
Impact
- Customers get dependable answers without waiting on a human, and support staff spend their time on complex cases instead of digging through documentation.
Route support tickets to specialists
Bring help desk automation to your support queue by categorizing each new ticket with AI, applying skills based routing to reach the right specialist, and giving agents AI agent assist with the most relevant knowledge base articles.
- Team
- Customer Experience
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Customer support agents handling tickets and support managers assigning workload.
The problem
Customer support teams receive a high volume of tickets 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.
The 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, routes the ticket to the appropriate team or agent using predefined assignment rules, then uses AI to analyze the ticket against the knowledge base and previous resolutions to suggest the most relevant articles or solutions that match the issue, delivering these suggestions directly to the assigned agent.
Impact
- Customers get answers sooner because each ticket lands with the right specialist and agents resolve more issues on their own instead of escalating.
Capture, categorize, summarize, and tag voice of customer feedback
Unify customer feedback automatically to surface themes, flag urgent issues, and guide smarter product decisions.
- Team
- Customer Experience
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Product managers, product operations, UX researchers, customer success teams — especially in SaaS and product‑led companies handling large volumes of qualitative feedback
The 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.
The 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.
Impact
- 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.
Resolve order requests from chat
Give support teams an AI agent for customer support that reads order requests in team chat, decides whether to look up or update the record in your ecommerce order management system, and posts the result back in the conversation.
- Team
- Customer Experience
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Frontline customer support agents, support team leads, and operations managers in ecommerce businesses who regularly interact with order data as part of customer communications.
- Shared scenario
- Make.com shared scenario
The 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 switching between systems slows response times, increases data entry errors, and pulls agents away from customer conversations.
The solution
An AI agent triggered via the team's chat platform 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 ecommerce backend and delivers results directly in the chat conversation.
Impact
- Support agents stay in the customer conversation instead of hopping between systems, so customers get faster answers and order records stay accurate.
Flag negative customer feedback
Interpret incoming customer feedback, log entries with their sentiment, and notify the customer support team about negative signals.
- Team
- Customer Experience
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Customer support and customer experience leads, support managers, and CX analysts who monitor satisfaction trends and respond to customers reporting poor experiences.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a customer submits the feedback form, an AI agent reads the response and judges whether its sentiment is positive or negative from its tone, specificity, and intent. It logs every submission to a shared spreadsheet with the feedback text, the customer's email, and the sentiment, and when the feedback is negative, it alerts the customer support team in their chat channel so they can reach out to the customer.
Impact
- Unhappy customers hear back from support while the issue is still fresh, instead of their feedback sitting unnoticed in a queue until the relationship sours.
Retrieve customer context in Claude
Pull account, plan, usage, and error logs from internal systems into Claude, giving reps AI agent assist and AI log analysis for faster AI customer support.
- Team
- Customer Experience
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Customer support reps and technical support engineers investigating account, usage, and error issues, and support team leads who need investigations resolved with full customer context.
The problem
Support reps investigating a customer issue have to leave their conversation, log into several internal tools, and look up the customer's account, plan, usage, and recent error logs by hand. Piecing this context together across systems slows the investigation, breaks the rep's focus, and leads to answers based on incomplete information.
The solution
When a support rep asks Claude for more context during an investigation, Claude acts as an AI agent and decides to call the automation as a tool, passing the customer ID and an optional time range. The automation retrieves the account profile and plan details, pulls usage metrics against plan limits, and fetches recent error and execution logs from internal systems, then merges the results into one structured response and returns it to the agent to analyze within the conversation.
Impact
- Customer issues are diagnosed from complete, accurate account and usage context inside the rep's conversation, so customers receive correct answers sooner without reps switching between internal tools.
Answer customer support questions from the knowledge base
Turn scattered docs into trusted AI answers that resolve support questions faster and more consistently.
- Team
- Customer Experience
- AI maturity stage
- Lead
- Automation type
- AI Automation
- Who it is for
- Customer support teams, CX managers and operations teams
The 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.
The 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.
Impact
- Help support and ops teams answer questions faster and more consistently by turning scattered docs into a trusted, searchable Q&A layer
Marketing
AI automation use cases for Marketing teams. 25 solutions available.
Draft articles from content briefs
Scale AI content creation with an AI content writer that researches each brief, captures key insights and angles, and produces a brand aligned article draft ready for editing.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Content writers, content strategists, and editorial teams who turn content briefs into published articles.
- Shared scenario
- Make.com shared scenario
The problem
Content writers manually convert 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 process is slow, inconsistent across writers, and creates a bottleneck that limits how much content the team can produce without growing headcount.
The solution
When a new request is added to the content request board, an AI agent reads the topic and brief, decides which web and internal sources to research, and selects the strongest insights and content angles. It then writes a structured article draft in the brand voice using the style guide, saves it as a new document, and links the draft to the request for editor review.
Impact
- Writers spend their time editing and sharpening drafts instead of starting from a blank page, so the team publishes more content in a consistent voice without adding headcount.
Refine UI copy for brand consistency
Send draft labels, tooltips, and error messages to a chat based UX writing tool that applies an AI style guide and returns clear, consistent copy in your brand voice.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Product marketers, designers, and growth teams who write or review UI copy when shipping product updates.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a team member posts draft product copy such as UI labels, tooltips, helper text, or error messages in the team chat, an AI agent checks it against the style guide and product glossary, looks up how similar strings are worded elsewhere in the product, and rewrites it to be clear, consistent, and in the brand voice. It replies in the thread with the revised copy and a short note on each change.
Impact
- Every screen speaks in the same clear brand voice, so copy reviews move faster and users meet fewer confusing labels, even without a dedicated UX writer.
Generate SEO metadata, keywords, headlines, briefs, and linking for new content
Instantly generate SEO metadata, keywords, headlines, and briefs to launch campaigns faster with consistent, high-quality content.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Digital marketing specialists & managers
The 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.
The 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.
Impact
- Teams generate SEO assets instantly from campaign inputs, enabling faster launches and consistent content quality.
Enrich event registrations with attribution
Capture each new event registration, add company size and industry through contact enrichment, append UTM tracking parameters for source attribution, then create or update the contact in your marketing database tagged for the right nurture sequence.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- Deterministic
- Who it is for
- Demand generation, field marketing, and events teams managing event registrations and follow up campaigns at scale.
The 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.
The 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.
Impact
- Event leads reach nurture campaigns while interest is still high, and every registration is credited to the right campaign source for reliable event ROI reporting.
Compile market research reports
Bring AI market research to your team with an AI research assistant that takes a market, focus, and time frame, searches the web to conduct market research, and delivers a structured research document.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Strategy leads, product managers, business development teams, and marketing analysts who regularly conduct market research.
- Shared scenario
- Make.com shared scenario
The problem
Marketing and strategy 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.
The solution
When a team member submits a research request form with a target market, research focus, and time frame, an AI agent runs web searches, decides which sources are credible and relevant, and keeps searching until the question is covered. It synthesizes the findings into a structured research document saved to the shared drive and sends the link to the requester.
Impact
- Strategic decisions rest on current, sourced market insight delivered on request, instead of waiting on slow manual research that leaves findings outdated.
Flag draft issues before publishing
Run AI proofreading on every new CMS draft, using AI content review and a tone checker to flag typos, brand voice mismatches, and audience language issues, then report suggested fixes to the writer.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Content marketing and editorial teams responsible for drafting, reviewing, and publishing blog content.
The 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.
The 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 report of findings and suggested fixes and sends it to the writer through their usual review channel to apply before publication.
Impact
- Drafts reach publication on brand and free of errors without an editing bottleneck, so the team publishes more content at the same quality bar.
Generate content performance recommendations
Pull engagement metrics from every channel, use AI content analysis to spot high and low performing topics and formats, and deliver AI content strategy recommendations in a scheduled report.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Content marketing and brand teams responsible for creating and optimizing editorial calendars and multichannel content strategies.
The 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 is slow and tedious, and insights often come too late to inform the next content cycle or campaign adjustments.
The 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, uses AI to identify high and low performing patterns and generate 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.
Impact
- Content teams know which topics, formats, and channels earn engagement before planning the next cycle, so campaigns improve on evidence instead of guesswork.
Validate content plans against ICP criteria
Add an AI content reviewer to your content review process that checks each content plan against your ICP criteria and messaging guidelines, then suggests how to adjust messaging, tone, or targeting before production.
- Team
- Marketing
- AI maturity stage
- Build
- Automation type
- Agentic
- Who it is for
- Content marketers, copywriters, and marketing managers responsible for producing blog posts, ad copy, landing pages, social media, and other marketing content.
- Shared scenario
- Make.com shared scenario
The problem
Marketing teams create content across multiple formats without a reliable way to verify alignment with the company's ideal customer profile. Marketers review content plans against ICP documents and personas by hand, or skip the check. This leads to off target messaging, wasted production effort, and content that fails to resonate with the intended audience.
The solution
When a team member submits a content plan in the team chat, an AI agent checks it against a knowledge base of ICP criteria, buyer personas, and messaging guidelines, evaluates how well the plan aligns with ICP parameters, and replies in the chat thread with a summary of the alignment and context specific guidance on how to adjust messaging, tone, or targeting to better match the intended audience.
Impact
- Content goes into production already aligned with the ideal customer profile, so less effort is wasted on off target pieces and messaging resonates with the intended audience.
Build a cross platform social media analytics tool that pulls engagement metrics from every network, applies AI social media analytics to find winning content and posting times, and delivers a consolidated report with recommendations.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Social media managers, brand teams, and content strategists responsible for managing organic social media presence across multiple platforms.
The problem
Social media teams managing multiple platforms manually log into each network to extract engagement metrics, compile data into spreadsheets, and analyze performance patterns. This manual consolidation creates reporting delays, makes comparison across platforms difficult, and prevents teams from quickly identifying which content types and posting strategies drive the best results.
The solution
On a scheduled interval, the automation connects to each social media platform via API, pulls engagement metrics 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.
Impact
- Social media teams see which content and posting strategies work on every platform without waiting on manual reports, so effort shifts toward what drives results.
Get automated marketing reports by pulling spend, impressions, clicks, and conversions from every ad platform on a schedule, standardizing the metrics, and loading them into a PPC dashboard for cross channel reporting.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Deterministic
- Who it is for
- Performance marketing teams and growth teams managing paid advertising across multiple platforms who need unified campaign visibility and reporting.
The problem
Marketing teams running multichannel campaigns manually extract data from different advertising platforms, normalize inconsistent metric formats, and compile 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.
The 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 naming conventions that differ by platform, then writes the normalized data to a centralized dashboard or data warehouse where the marketing team can analyze cross channel performance in real time.
Impact
- Marketing teams see current performance for every channel in a single view, so budget shifts happen faster and cross channel ROI is measured accurately.
Monitor your social channels with an AI social media assistant that answers incoming and repetitive comments in your brand voice, and acts as a social media moderation tool by flagging sensitive comments for human review.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Social media managers and community managers who respond to comments across the brand's social channels.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a new comment is posted on any of the brand's social channels, an AI agent reads it with the post context, checks the brand voice guide and approved answers, and decides how to act. For routine or repeated questions it posts a reply in the brand voice, and for complaints or sensitive comments it holds the reply and sends the comment with a suggested response to the team chat for human review.
Impact
- Followers get timely replies that sound like the brand, while risky comments reach a person before they escalate, protecting brand reputation as comment volume grows.
Repurpose product updates across channels
Automate content creation for every launch with AI content repurposing that turns one product update into a blog post, a social media post, and a product update announcement, each adapted to its channel and brand voice.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The 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 and paste process slows down launches, creates version drift between channels, and makes it nearly impossible to enforce consistent brand voice and messaging at scale.
The solution
When a new product update is sent in through a webhook, an AI agent reads it and writes three assets from it, a blog post, a product update announcement, and a social media post, choosing the emphasis, tone, and format that suit each channel. It saves the drafts in a shared document, then logs each document link and its draft status in the content tracking database for review.
Impact
- Every product update turns into a consistent set of channel drafts ready for review, so launches move forward instead of stalling while teams rewrite the same update for each audience.
Answer event questions in chat
Launch an event chatbot that works as a FAQ chatbot for attendees and staff, searching schedules, speaker bios, logistics, and live updates to answer every question in chat.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The 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 late changes. 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.
The solution
When an attendee or staff member posts a question in the event chat channel, an AI agent interprets it and decides which sources to search, such as the event schedule, speaker bios, logistics documents, and the live updates log. It combines what it finds into an answer, replies in the same thread, and points the person to the event team when no source covers the question.
Impact
- Attendees find answers on their own during the event, so staff stay focused on running it instead of fielding the same questions over and over.
Track competitor positioning shifts
Track competitors across websites, social media, and ad libraries, run AI competitor analysis on their messaging, and deliver a competitive intelligence report with recommended positioning adjustments to marketing leadership.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Product marketing, competitive intelligence, and marketing strategy teams responsible for market positioning and competitive differentiation.
The 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 time intensive and often results in reactive rather than proactive strategic planning.
The 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, uses AI to analyze patterns, identify messaging shifts, compare competitor positioning against the company's current strategy, and generate a strategic brief highlighting key competitive themes, gaps, and recommended positioning adjustments, then delivers the brief to marketing leadership.
Impact
- Marketing leadership spots competitor positioning shifts early and adjusts its own messaging proactively, instead of reacting after competitors have already gained ground.
Repurpose product briefs into launch drafts
Use AI content repurposing to turn a ready product brief into multichannel content for email, blog, social, in app, and press, then send every draft into your content approval workflow at once.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Product marketing and communications teams coordinating product launches across multiple marketing channels.
The problem
When launching a product, marketing teams manually rewrite a single brief into a separate version for each launch channel, such as the announcement email, blog post, social posts, in app message, and press release, adjusting tone, length, and structure for every one. Reformatting and routing each version for approval is tedious and delays coordinated launches.
The solution
When a product brief is marked ready, the automation uses AI to generate a variant for each launch channel the team publishes to, including email, blog, social, in app, and press, with the appropriate tone, length, and structure for that channel, writes each one to its own draft record, and posts an approval request to the reviewer for every channel.
Impact
- Coordinated launches go out on time because every channel version is ready for review together, rather than waiting on one rewrite after another.
Personalize outreach for every customer
Scale AI personalization by pulling each customer's CRM record, behavioral data, and history, then drafting personalized outreach and delivering it through their preferred channel.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Lifecycle, CRM, and demand generation marketers running customer engagement campaigns.
The 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.
The solution
When a customer enters an outreach segment in the CRM, the automation pulls their CRM record, behavioral data, and interaction history, then uses AI to draft personalized messaging that reflects their recent activity. The tailored message is sent through the customer's preferred channel, and the send is logged back to the CRM record.
Impact
- Every customer receives outreach personalized to their data and behavior without marketers writing each message by hand.
Track brand mentions and sentiment
Build your own social listening tool that collects brand mentions from news, social, review sites, and forums, applies AI sentiment analysis to each one, and sends the brand team a prioritized summary for online reputation monitoring.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Brand managers, communications teams, public relations teams, and social media managers responsible for brand reputation and crisis management.
The 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 takes constant effort and often results in delayed responses to critical mentions or sentiment shifts.
The solution
On a scheduled interval, the automation searches news sites, social media platforms, review sites, and industry forums for brand mentions using company name, product names, and relevant keywords, and collects all matching content. It uses AI to classify each mention as positive, negative, or neutral, identify themes, and flag urgent issues. It then generates a summary report categorizing mentions by sentiment and topic with recommendations for response priority and delivers it to the brand team.
Impact
- Reputation risks and negative sentiment shifts are caught while they are still small, so brand teams respond before issues escalate instead of after the damage spreads.
Generate launch briefs from release tickets
Feed technical release tickets to an AI FAQ generator that also drafts internal briefs in plain language, so marketing and support teams are ready on launch day.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The 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 support surprises after launch.
The solution
When a feature ticket moves to released in the issue tracker, an AI agent reads the ticket details and linked specs, decides which assets the release needs, and drafts product one pagers, FAQs, and internal briefs in plain language for non technical teams. It publishes the documents to the team knowledge base and shares the links with marketing, product, and support leads.
Impact
- Every team walks into launch day with the same accurate understanding of what shipped, so launches stay aligned and support is not caught off guard by edge cases.
Use Claude for marketing as an AI marketing assistant and marketing analytics tool that checks each metric's source of truth, queries ad, web analytics, and CRM data with read only access, and returns consistent campaign numbers with source notes.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Performance and growth marketers, channel and campaign managers, marketing operations teams who maintain metric definitions, and marketing leaders reviewing spend, traffic, and pipeline contribution.
The problem
Marketers who want campaign and channel numbers log into each ad platform, web analytics, and the CRM one by one, or wait for a marketing analyst to pull a report. Each platform defines metrics differently and access sits with a few people, so answers arrive slowly and figures conflict between channel reports.
The solution
When a marketer asks their AI assistant a question about campaign or channel performance, the assistant calls the automation on demand, which runs an AI agent. The agent checks the marketing metric catalog to find the source of truth and definition for each requested metric, then runs read only queries against the ad platforms, web analytics platform, and CRM through organization level connections. It normalizes the results and returns the data to the assistant with notes on each number's source and definition.
Impact
- Marketers get consistent campaign and channel numbers from a single source of truth whenever they ask, so budget and optimization decisions rest on agreed figures instead of conflicting platform reports or waiting on analysts.
Track market trends for campaigns
Use AI market research to scan articles and industry sources for a target market, judge which findings matter, and deliver a market trend analysis report to the marketing team on a schedule.
- Team
- Marketing
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- Marketing strategists, brand teams, and content leads who track market trends to inform campaign planning, positioning, and editorial direction.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a scheduled basis, an AI 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 emailed to the marketing team.
Impact
- 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.
Generate on-brand e-commerce product descriptions from product specifications
Turn scattered product specs into on-brand descriptions in minutes and update entire catalogs without copywriting bottlenecks.
- Team
- Marketing
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Digital marketing specialists & managers
The 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.
The 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.
Impact
- Teams turn scattered product specs into on-brand descriptions in minutes, accelerating catalog updates and reducing manual copywriting effort.
Track competitor pricing changes
Run competitor price tracking with an AI agent that reads pricing pages on a schedule, compares plans and tiers with past records, and sends the team a competitive pricing analysis of what changed and why it matters.
- Team
- Marketing
- AI maturity stage
- Scale
- Automation type
- Agentic
- Who it is for
- Product marketers, competitive intelligence analysts, pricing strategists, and sales enablement teams who rely on up to date competitor pricing data.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
Every week, an AI agent visits each competitor pricing page listed in a tracking sheet, interprets the page to identify plans, tiers, and prices, compares them with the last snapshot stored in the sheet, and decides which changes are meaningful. It posts a summary of what changed and why it matters to the team's chat channel, then saves the new snapshot.
Impact
- Marketing teams are notified of competitor pricing changes as they happen, enabling faster responses in positioning, packaging, and sales enablement.
Audit pages against your SEO criteria
Retrieve any live page with an AI SEO audit tool, run a page SEO analysis against your target keywords and criteria, and deliver a prioritized SEO audit report with evidence for every fix.
- Team
- Marketing
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- SEO specialists and content marketers who own organic performance, content editors and writers who implement title, meta and heading fixes, and web and growth teams responsible for landing page quality.
The problem
Marketing teams review pages by hand to check whether titles, meta descriptions, headings, links and images meet their SEO standards and match the keywords they are targeting. Because the review depends on whoever runs it, findings come back inconsistent. Audits get skipped when the team is busy, so issues stay live until someone happens to notice them.
The solution
On a schedule, an AI agent takes each page URL and its target keywords from the tracking spreadsheet along with the team's optimization criteria, then retrieves the live page and judges what it finds against those criteria. It decides which elements warrant scrutiny, weighs each finding by severity, separates confirmed issues from what it could not verify, and flags cases where the keywords conflict with the page's apparent intent. It compiles its evidence and prioritized recommendations into a document and returns the link.
Impact
- Pages are reviewed the same way every time against the team's own standards, so ranking problems are caught and corrected early instead of staying live until someone notices them.
Surface market signals for prioritization
Run AI market research on a set schedule that monitors competitors, tracks market trends, and pulls in user feedback, then delivers one digest so product teams can prioritize with confidence.
- Team
- Marketing
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Product managers and product leads who run discovery and prioritize the roadmap.
- Shared scenario
- Make.com shared scenario
The problem
Product teams waste significant time manually stitching together competitor moves, market signals, and scattered user feedback from inconsistent sources. Without one place to gather and synthesize this information, prioritization slows down, emerging opportunities go unnoticed, and product recommendations become harder to defend with reliable, current data.
The solution
On a set schedule, an AI agent researches competitor activity, market trends, and user feedback from the internal insights hub. It decides which sources to query, including social platforms, news search, and product review sites, synthesizes the signals into a consolidated view of opportunities and threats, and delivers the digest to the product team.
Impact
- Product teams prioritize with current evidence on competitors, market shifts, and user needs, so emerging opportunities are caught early and roadmap recommendations are easier to defend.
Generate timely content for every channel
Run AI content creation and content marketing automation that researches market topics daily, drafts each piece for its format, and publishes it to the right channels.
- Team
- Marketing
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Content marketing, social, and brand teams producing and distributing content across multiple channels.
The 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.
The solution
An AI agent scans industry news feeds and social trends daily for relevant topics, decides which ideas are worth pursuing, and generates content tailored to each format and channel, then publishes each piece to the CMS and social scheduling tool. It chooses its approach based on the topic and audience rather than following one fixed template.
Impact
- The team ships more relevant content across channels without manually researching and drafting every piece.
AI automation use cases for Information Technology teams. 15 solutions available.
Classify and route product feature requests to the right team
Automatically route feature requests to the right team so PMs prioritize faster with cleaner, higher-signal queues.
- Team
- Information Technology
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Ideal for SaaS and product-first companies
The 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.
The 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.
Impact
- Teams get cleaner feature queues, accelerating prioritization while reducing time spent manually triaging low‑value requests.
Guide new users’ onboarding goals into the right success paths
Turn onboarding goals into personalized success paths that guide new users to value faster.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- New Make users across SaaS, operations, marketing, and engineering teams seeking guided onboarding based on their automation goals
The 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.
The 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.
Impact
- Customer success teams turn onboarding goals into tailored success paths, helping new users reach their first meaningful outcome faster.
Route feature requests to owners
Analyze each incoming feature request, assign its category with predefined criteria, log it in your feature request tracking, and notify the owning team so customer feedback management never loses a follow up.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Product managers, product operations teams, and engineering leads who review incoming feature requests.
The problem
Teams receive a steady flow of feature requests across channels, but triage is manual and inconsistent. Product managers read each request and forward it to a team by hand. Items are misrouted, priorities get unclear, and follow ups slip through.
The solution
When a new feature request is submitted through the feedback form, the automation uses AI to read its content, assign a product area and request type using predefined criteria, and identify the owning product team. It then logs the request in the feature request tracker with its category and owner, and posts a summary with a link to the record in that team's chat channel.
Impact
- Every feature request lands with the team that owns it and a clear next step, so customer feedback shapes the roadmap instead of getting lost.
Route vendor outage alerts by app
Use AI to spot vendor status emails in the IT inbox, match third party outage and resolution updates to the affected app, and send the app owner a downtime notification in team chat as part of your incident communication.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- IT operations and service desk teams that monitor vendor notices, the app owners responsible for each business application, and the employees who rely on those apps every day.
The problem
IT teams receive vendor status emails about outages and service problems in a shared inbox, then work out which internal app is affected, look up its owner, and forward the news by hand. Updates sit unread when the team is busy, so app owners hear about vendor outages from colleagues and cannot warn users in time.
The solution
When an email arrives in the IT alerts mailbox, the automation uses AI to check only the sender and subject, and stops unless it is a vendor status update. For vendor emails, it uses AI to read the full message, extract the vendor, affected product, and impact summary, and classify it as an incident or a resolution, skipping any other update. It then looks up the app owner in the app inventory and messages them in team chat with the status and summary.
Impact
- App owners learn about vendor outages as soon as the vendor reports them, so they can warn users and plan workarounds before the service desk fills with tickets.
Schedule postmortems from resolved incidents
Extend your incident response automation to postmortems by assembling a post incident review brief from the alert timeline and key messages, then booking the earliest responder slot within the SLA window.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- 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.
The 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.
The solution
When an incident is marked resolved above a configured severity threshold, the automation pulls participants from on call rosters, the alerting tool, and the service catalog. It uses AI to assemble a postmortem brief from the alert timeline, key messages, and impact data into a templated document. It then checks the responders' calendars, books the earliest slot for the core responders within the SLA window, and sends the invite with the brief linked, posting a confirmation to the incident channel.
Impact
- Postmortems happen while details are still fresh and within the required SLA window, so lessons are captured instead of lost to fading memory and calendar delays.
Build and share DevOps/SRE operational dashboards from existing data sources
Turn existing data into shareable DevOps dashboards that eliminate ad-hoc reporting and keep teams aligned with real-time operational visibility.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Engineering teams, SREs, platform teams, product engineers, and data‑adjacent teams needing fast internal visibility without relying on BI or front‑end support
The 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.
The 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.
Impact
- Engineering and ops teams get up‑to‑date internal dashboards from existing data, reducing ad‑hoc queries and giving everyone a shared operational view.
Flag significant A/B test results
Connect your A/B testing tool, check active experiments for A/B test significance on a schedule, and send the product manager a summary of results and insights once a test reaches its threshold.
- Team
- Information Technology
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Product managers and product analytics/growth teams responsible for running experiments and making rollout decisions.
The 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.
The solution
On a scheduled interval, the automation checks active experiments in the experimentation platform against predefined significance thresholds. When significance is reached, it uses AI to write a summary of the results, the leading variant, and key insights, then sends the summary to the product manager via email or a chosen communication channel.
Impact
- Product managers get timely, actionable experiment summaries the moment significance is reached, enabling faster and more consistent product decisions.
Draft PRDs from one-line product problems in your workspace
Turn one-line product problems into evidence-backed PRDs fast, aligning teams with feedback, analytics, and ready-to-build requirements.
- Team
- Information Technology
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Product managers, product ops, UX teams, and growth teams in SaaS or any product‑led organisation with analytics and feedback systems in place
The 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.
The 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.
Impact
- Product teams turn one‑line problem statements into structured, evidence‑backed PRDs faster, reducing time spent hunting for context and speeding up alignment.
Draft incident postmortems from chat history
Turn each outage into an AI incident report by pulling incident context and chat history into a structured post incident review with timelines, action items, and owners in a shared document.
- Team
- Information Technology
- AI maturity stage
- Scale
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When an incident is marked resolved in the incident management tool, an AI agent reviews the incident record, pulls the related chat threads, and decides which logs and messages matter for the timeline. It drafts a structured postmortem in a shared document covering the timeline, root cause, action items, and owners, then posts the document link in the incident channel for the team to review.
Impact
- Every outage leaves behind a dependable written record, so remediation work gets done and the same failures stop recurring.
Answer Product Managers’ discovery questions from customer feedback
Turn scattered customer feedback into instant discovery answers backed by stronger evidence.
- Team
- Information Technology
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- PMs, Designers, Growth Engineers, and Product leadership. Mostly relevant for product-led companies
The 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.
The 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.
Impact
- Teams answer discovery questions from customer feedback faster, with less manual digging and stronger evidence behind decisions.
Track API changes for engineering
Check every API changelog and spec on a schedule with website change monitoring, summarize new API updates, and alert the responsible engineer and the wider team by email or chat.
- Team
- Information Technology
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Product engineering teams responsible for maintaining and updating API integrations with external vendors.
The 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.
The solution
On a scheduled interval, the automation pulls a database of API integrations and their documentation/changelog links, compares each API spec and changelog with the last stored version, uses AI to summarize what changed, saves the new version, then notifies the responsible engineer and the broader team via email or an instant messaging channel.
Impact
- Engineering teams learn about API changes immediately, reducing downtime risk while freeing time for core product development.
Diagnose triage bugs with Claude Code
Speed up your bug triage process by handing new bugs to Claude Code as an AI code debugger that runs software root cause analysis on your repository and posts the suspected cause and fix to the ticket.
- Team
- Information Technology
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Engineering triage owners and the developers on triage rotation who do first pass bug reproduction and code investigation, plus the engineering leads who depend on scoped tickets.
The problem
Engineering teams pick up each newly reported bug in triage and manually dig through the codebase to reproduce it and work out what is actually broken. This first pass investigation pulls engineers off feature work and leaves tickets sitting in triage until someone has time to look.
The solution
On a schedule, an AI agent picks up new bug tickets in triage from the issue tracker and spins up a Claude Routine cloud environment with the product repository attached. The agent decides which files, recent commits, and execution paths to examine based on what the report describes and what it uncovers as it reads the code. It then comments on the originating ticket with the suspected root cause, the specific files and functions involved, and what needs to change.
Impact
- Bug tickets reach an engineer already carrying a code level diagnosis of what is broken and where, turning triage into a review of findings rather than an investigation from scratch.
Assign backlog tickets to owners
Auto assign tickets on a schedule with an agent that reads each unassigned backlog item, applies your ticket assignment rules to pick the right owner, and moves it to active for cleaner backlog management.
- Team
- Information Technology
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- IT and engineering team leads, project managers, and engineering managers responsible for backlog hygiene, balanced workload, and timely ticket pickup across the team.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a recurring schedule, an AI agent reviews unassigned tickets in the project tracker 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.
Impact
- 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.
Screen incoming messages for security threats
Add AI guardrails that work as an LLM firewall for your agents, screening every message for hidden injections, data leaks, and social engineering, then allowing, blocking, or flagging it to strengthen AI agent security.
- Team
- Information Technology
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Engineering and platform teams deploying AI agents in production, and the developers responsible for keeping those agents safe from malicious or manipulative inputs.
The problem
Teams building AI agents expose them directly to whatever users or upstream systems send, including disguised prompt injection, credential harvesting, and social engineering attempts. Reviewing every message by hand is impractical, and a single missed attack can leak secrets or hijack how the agent behaves.
The solution
When a message arrives at the main agent's incoming webhook or chat channel, an AI agent inspects it and interprets its true intent against a broad range of security threats, from hidden injection attempts to credential exposure and false authority claims. It weighs how risky and ambiguous each request is, then decides whether to let it through, stop it, or escalate it for human review before anything reaches the main agent.
Impact
- Every message is vetted for security threats before it reaches the AI agent, protecting against injection, data leaks, and abuse while letting legitimate requests flow through.
Route access requests to owners
Turn chat into a self service access request system by having AI interpret each request, find the owner of the app or resource in an internal registry, and route the request to them for approval.
- Team
- Information Technology
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The problem
IT teams manually field a constant stream of access requests from employees, identifying the right tool or resource owner and connecting requester and owner for each request. This creates a bottleneck for IT, slows down employees waiting on access, and is impossible to sustain as the organization grows.
The solution
When an employee sends an access request in team chat, an AI agent interprets it, 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 for approval.
Impact
- 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.
People
AI automation use cases for People teams. 11 solutions available.
Summarize interviews into candidate scorecards
Use an AI recruiting assistant to analyze interview recordings or transcripts, turn skills, experience, and motivations into recruiter interview notes, and fill each candidate's interview scorecard in your ATS.
- Team
- People
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Recruiters, recruiting coordinators, and hiring managers who review interviews and complete candidate scorecards.
The problem
Recruiters manually review interview recordings and transcripts, then extract skills, experience, and motivations by hand. Notes are inconsistent, scorecards stay incomplete, and stronger candidates can be missed or evaluated unevenly.
The solution
When an interview recording or transcript is saved to the meeting platform, the automation retrieves it and uses AI to extract the candidate's experience, skills, and motivations into structured recruiter notes. It then matches the candidate in the ATS and fills their interview scorecard with the notes and supporting evidence for each criterion, ready for the hiring team to review.
Impact
- Every candidate is judged on complete, comparable records instead of uneven notes, so strong applicants stand out and the team reaches offers sooner.
Route candidates by resume fit score
Parse each new resume with an AI resume parser, match it to the job requirements, and route candidates by fit score through automated resume screening that updates your ATS with skill tags and summaries.
- Team
- People
- AI maturity stage
- Build
- Automation type
- AI Automation
- Who it is for
- Recruiting teams, talent acquisition teams, HR operations, and hiring managers at companies with high volume hiring needs.
The 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 hires, introduces inconsistent evaluation criteria across recruiters, and prevents teams from responding quickly to strong candidates who may be interviewing with competitors.
The solution
When a new job application arrives in the applicant tracking system, the automation retrieves the resume and job description, then uses AI to extract the candidate's experience, skills, and education and the role's requirements, compare them, produce a fit score with an explanation, and write a short candidate summary. It updates the candidate record with skill tags, score, and summary, then routes by score band, moving strong fits to screening and notifying recruiters, sending borderline fits to manual review, and marking weak fits not qualified.
Impact
- Strong candidates reach recruiters while they are still on the market, and every applicant is judged against the same criteria instead of whichever recruiter happened to read the resume.
Request references for shortlisted candidates
Start automated reference checks as soon as a candidate reaches the reference check stage by pulling their reference contacts from the applicant tracking system and sending each one a personalized reference check form.
- Team
- People
- AI maturity stage
- Accelerate
- Automation type
- Deterministic
- Who it is for
- Recruiting coordinators and recruiters responsible for keeping candidates moving through the pipeline, and hiring managers waiting on completed reference checks.
The problem
Recruiting coordinators manually track when candidates reach the reference check stage, then look up each candidate's reference contacts and send out requests one by one. This manual outreach is easy to delay or forget when several candidates are moving through the pipeline at once, pushing back the start of the reference check process and the eventual hiring decision.
The solution
When a candidate reaches the Reference Check stage in the applicant tracking system, the automation retrieves the candidate's record and pulls the associated reference contact list. It then sends a personalized reference request to each contact through a form tool and logs the request date on the candidate record in the applicant tracking system.
Impact
- Reference outreach begins the moment a candidate reaches the reference check stage, removing the manual step that often delays reference checks and slows down the hiring decision.
Summarize daily team updates for managers
Collect each day's standup responses, log them by team in a central record, and send every manager an AI digest of blockers and recurring themes, so team status updates work like a standup app.
- Team
- People
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- People teams running daily check in programs, people managers who review their direct reports' updates and clear blockers, and department heads tracking progress across several teams.
The problem
People teams and managers collect end of day and standup form responses by hand, copying each one into a shared tracker and sorting it by team. Managers then read every entry themselves to spot blockers and recurring themes. The work is repetitive, responses get missed or filed under the wrong team, and blockers surface too late to act on.
The solution
At the end of each working day, the automation retrieves that day's standup responses from the form tool, matches each respondent to their team and manager using the team directory, and logs every entry in the central update record sorted by team. It then uses AI to summarize each team's entries into a digest of blockers, recurring themes, and items needing follow up, and sends the digest to that team's manager.
Impact
- Every daily update lands in one organized record, and managers start each morning knowing which blockers and themes need their attention instead of reading through every response.
Answer employee policy questions
Give employees an HR chatbot that answers benefits, leave, and policy questions from your current HR documentation, turning routine requests into HR self service and taking the load off your HR help desk.
- Team
- People
- AI maturity stage
- Scale
- Automation type
- Agentic
- Who it is for
- HR managers, People Operations teams, and HR business partners who field employee questions about benefits, leave, and policies.
- Shared scenario
- Make.com shared scenario
The problem
HR teams manually answer 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.
The solution
When an employee asks a question in the team chat, an AI agent searches the HR documentation, including the benefits guide, leave policies, and company handbook, selects the passages that apply, and replies in the thread with an answer and links to the source documents. If it finds no clear answer or the question concerns a personal case, it hands the question to the HR team with the conversation attached.
Impact
- Employees get consistent policy answers whenever they need them, while the HR team spends its time on strategic work instead of repeat questions.
Create personalized AI powered onboarding videos for new hires
Turn new hire data into personalized AI onboarding videos automatically, reducing admin work and accelerating a consistent welcome experience.
- Team
- People
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Operations teams responsible for managing workflows across multiple tools, typically RevOps, Sales Ops, or Customer Operations.
The 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.
The 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.
Impact
- By automating data capture, record updates, and notifications, teams move work forward faster with fewer errors and less administrative overhead.
Draft rejection emails for candidates
Send an automated rejection email when a candidate is marked rejected, using AI to personalize it to their background, drafting it for recruiter review when rejecting candidates after interview stages, and tagging strong fits for the talent pool.
- Team
- People
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Recruiting teams, talent acquisition managers, and HR operations in companies with high volume hiring.
The 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. Manual processes also mean strong candidates who weren't the right fit this time don't get properly tagged for future opportunities.
The solution
When a candidate's status changes to rejected in the ATS, the automation pulls the candidate's profile, stage reached, job description, and rejection reason, then uses AI to write a personalized, empathetic rejection email that references their background. For early stage rejections it sends the email directly; for rejections after interviews it saves a draft for the recruiter to review. It logs the email to the candidate timeline, tags high scoring candidates for the talent pool, and notifies the recruiter.
Impact
- Every rejected candidate gets a timely, personal response instead of silence or a generic note, protecting employer brand and keeping strong applicants open to future roles.
Shortlist candidates from resume submissions
Run AI resume screening on every submitted application, score candidate fit against your hiring rubric, and surface standout applicants to the hiring team with automated candidate screening that logs each assessment.
- Team
- People
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Recruiters, talent acquisition teams, and hiring managers who screen high volumes of inbound applications and want a consistent, criteria driven shortlist.
- Shared scenario
- Make.com shared scenario
The 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 and accept other offers, and there is rarely a documented record of why anyone was passed over.
The solution
When a candidate submits an application in the applicant tracking system, an AI agent retrieves the resume and evaluates it against the hiring rubric 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. Every assessment is recorded for comparison, and standout applicants are surfaced to the hiring team in real time.
Impact
- Every applicant is judged against the same rubric and strong candidates reach the hiring team while still available, with a documented reason behind every decision.
Schedule interviews across candidate availability
Run automated interview scheduling with an AI interview scheduler that checks candidate and interviewer calendars, books the earliest mutual slot with a video link, and updates the recruiting tracker.
- Team
- People
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Recruiting coordinators, talent acquisition teams, and hiring managers who schedule multi attendee interviews across high volume hiring pipelines.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
On a daily schedule, 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.
Impact
- 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.
Answer onboarding questions from new hires
Give new hires an HR chatbot that interprets each question, searches your company knowledge base, and answers in thread with follow ups, working as HR help desk software for policies, processes, and culture.
- Team
- People
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- HR and People Operations teams who field employee inquiries, and new and existing employees seeking quick answers about company policies, benefits, processes, and culture.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When a new hire posts a question in the HR help channel, an AI agent interprets it, searches the company knowledge base for relevant context, and responds with 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.
Impact
- Employees receive immediate accurate answers to their company questions while HR teams reclaim time previously spent on repetitive knowledge requests.
Score interviews from transcript evidence
Give interviewers an AI recruiting assistant that finds the right interview, reads its transcript, and writes interview feedback for each scorecard question with concrete evidence, flagging open questions for review.
- Team
- People
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Interviewers, hiring managers, and recruiters who evaluate candidates and need consistent, evidence backed interview feedback.
- Shared scenario
- Make.com shared scenario
The 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.
The solution
When an interviewer sends the interview date and a candidate detail in chat, an AI agent locates the right interview in the calendar, 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.
Impact
- Interview scorecards are completed with evidence backed assessments shortly after each interview, giving hiring teams consistent, comparable feedback for faster decisions.
Operations
AI automation use cases for Operations teams. 7 solutions available.
Flag supplier delays before stockouts
Add purchase order tracking to your replenishment process by reading each purchase order confirmation from supplier emails, flagging orders that are unconfirmed, short, or late, chasing suppliers, and alerting buyers to orders that will arrive after stock runs out.
- Team
- Operations
- AI maturity stage
- Accelerate
- Automation type
- AI Automation
- Who it is for
- Buyers and procurement teams managing supplier orders, supply and production planners responsible for material availability, and operations managers accountable for stockouts.
The problem
Buyers chase suppliers by hand for order confirmations, read the replies in a shared inbox, and copy confirmed quantities and dates into the ERP. Orders that were never confirmed, or confirmed with a smaller quantity or a later date, go unnoticed until the delivery fails to arrive, causing stockouts, production stoppages, and costly expedited shipping.
The solution
Every morning, the automation pulls open purchase orders from the ERP and searches the purchasing inbox for supplier replies to each one. It uses AI to extract the confirmed quantity and delivery date from the reply or its attachment, records them on the order, and flags orders that are unconfirmed or confirmed short or late. It checks each flagged order's arrival against current stock and average daily usage, emails unconfirmed suppliers a confirmation request, and sends buyers a list of at risk orders, sorted by when stock runs out.
Impact
- Supplier delays surface while there is still time to expedite, switch suppliers, or reschedule production, instead of being discovered when the shelf is already empty.
Update inventory from chat requests
Bring AI inventory management into your team chat so anyone can check stock levels, adjust stock, and create orders in your logistics spreadsheet, with real time inventory tracking and a confirmation for every change.
- Team
- Operations
- AI maturity stage
- Accelerate
- Automation type
- Agentic
- Who it is for
- 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.
- Shared scenario
- Make.com shared scenario
The problem
Operations and logistics teams manage inventory updates and order creation across disconnected spreadsheets and logistics tools, while daily communication happens in chat. The gap between these systems forces manual copying and pasting, leading to stock mismatches, slow order processing, and poor auditability. This makes an accurate, current view of inventory hard to keep.
The solution
When a team member posts a request in the operations chat channel, an AI agent interprets whether they want to check stock, update inventory, or create an order, then reads or writes the matching rows in the logistics spreadsheet. It confirms each change in the thread with the updated stock level or order details, and asks for clarification when a request is ambiguous.
Impact
- Stock levels and orders stay accurate and auditable because updates happen where the team already talks, instead of drifting out of sync across spreadsheets and chat.
Flag data breaches at vendors
Add daily breach checks to third party risk management by scanning news for each vendor that holds your data, using AI to confirm real incidents, and sending data breach alerts to security and vendor owners.
- Team
- Operations
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Security and risk teams who respond to third party incidents, vendor owners and procurement managers responsible for each vendor relationship, and privacy and compliance staff who handle breach notifications.
The problem
Operations teams review vendors once at onboarding and then rely on each vendor to report its own security incidents. When a vendor that holds company or customer data is breached, the news often arrives late or through the press, leaving no time to cut access, assess exposure, or meet notification duties to customers and regulators.
The solution
Every morning, the automation reads the vendor register for vendors marked as holding company or customer data and searches news and breach disclosure sources for each one. It uses AI to judge whether each result reports a breach at that specific vendor. For each confirmed match, it alerts the security team and the vendor's internal owner in team chat with the source link and the data the vendor holds, then logs the incident on the vendor record.
Impact
- The business hears about a breach at a vendor while there is still time to cut access and assess exposure, instead of learning about it from customers or the press.
Flag KPI anomalies for owners
Pull the latest metrics every few minutes, run automated anomaly detection against defined baselines, and send KPI alerts to the right owner by chat and email when a spike or drop appears.
- Team
- Operations
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Data and analytics teams responsible for building reports and monitoring KPIs across business functions.
The 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.
The solution
Every few minutes, the automation pulls the latest KPI metrics from the BI tool and compares each metric against its defined baseline to detect spikes or drops. When an anomaly is found, it uses AI to summarize the likely drivers from related metrics and recent changes, then sends an alert with the metric, its current value, its baseline, and that summary to the relevant KPI owner through the team chat platform and by email.
Impact
- KPI owners hear about critical spikes and drops while there is still time to act, instead of discovering them in the next report review after the damage is done.
Extract invoice details from messages and route for AP approval
Extract invoice details from emails and route approvals automatically to cut manual work, reduce errors, and speed payment cycles.
- Team
- Operations
- AI maturity stage
- Scale
- Automation type
- AI Automation
- Who it is for
- Operations manager
The 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.
The 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.
Impact
- Invoices are automatically captured, structured, and routed to the right approver, shortening payment cycles and reducing manual data entry and errors.
Flag compliance risks in advisory calls
Check every client advisory call transcript for call compliance, judging each exchange against your compliance and legal standards, alerting the team on risky passages, and logging findings for call auditing.
- Team
- Operations
- AI maturity stage
- Lead
- Automation type
- Agentic
- Who it is for
- Operations and compliance leads who oversee client advisory calls, the advisors running those calls, and the teams accountable for privacy and regulatory obligations.
The problem
Operations teams spot check notes and recordings from client advisory calls to confirm that advisors did not misstate terms or mishandle personal data. Reviewing conversations against the company's own compliance and legal standards is slow, so most calls are never checked. Risky statements usually surface only once a customer disputes what they were told.
The solution
When a client advisory call transcript becomes available, an AI agent checks the conversation against the company's compliance and legal standards, pulling whichever standards are relevant to that specific call. It judges for each exchange whether the handling of personal data or the description of regulatory and product terms crosses a line, and how serious it is. The agent then alerts the operations team with the flagged passages and its reasoning, and writes the finding to a compliance record.
Impact
- Privacy missteps and regulatory misstatements in client advisory calls are caught while they can still be corrected with the customer, instead of surfacing later as a dispute or a compliance breach.