The CEO of Zapier recently announced that they have more AI agents working for them than human employees. Now that sounds exciting and terrifying but the truth is most of the “agents” he listed out are really just simple automations (with some AI sprinkled in).
He is, after all, promoting his own company, which he uses to build these automations.
In this guide, I will show you how to build those same automations on Make.com. It’s designed for business owners, no-code enthusiasts, and consultants looking to automate real-world tasks in marketing, sales, and operations.
The first thing you need to do is create a Make.com account. Sign up here to get one month free on the Pro plan.
I’ve split this guide up into sections for Marketing, Sales, HR, Product, and Customer Support. The following automations are beginner-friendly and the best way to learn is to follow the instructions and build it yourself.
If you’re looking to build more complex AI Agents, I have a full guide here. I also have a free email course which you can sign up for below.
High-Impact Use Cases for Marketing Teams
Write a blog post, summarize it for LinkedIn, create social variations, send campaign results to the team, draft the newsletter…and repeat. Every. Single. Week.
You’re drowning in content demands and half of it is grunt work that AI can now handle. Enter: Make.com + AI.
This combo turns your messy marketing checklist into an elegant flowchart. You write once, and AI helps you remix, repurpose, and report across all your channels.
Here’s what you can automate today:
- Turn blog posts into LinkedIn content
- Repurpose content into tweets, emails, or IG captions
- Summarize campaign performance into Slack reports
- Generate social variations for A/B testing
- Create email copy from feature releases
- Summarize webinars or podcasts for newsletters
Let’s build a few of these together.
Project 1: Blog to LinkedIn Auto-Post
Scenario: You’ve just published a blog post. Instead of opening LinkedIn and crafting a summary from scratch, this automation turns your post into a social-ready snippet instantly.
How it works: Make.com watches your RSS feed for new content. When a new blog post is detected, it sends the blog’s title and content to Claude or OpenAI with a carefully constructed prompt. The AI replies with a LinkedIn-ready post featuring a hook and CTA. This is then routed to Buffer for scheduling or Slack for internal review. All content can be logged to Google Sheets or Airtable for records and team collaboration.
Step-by-Step Walkthrough:
- Trigger on New Blog Post (RSS Module)
- Drag the RSS module into your Make.com scenario.
- Enter your blog’s RSS feed URL (e.g., https://yourblog.com/rss).
- Set the module to check for new posts every X minutes.
- Ensure the output includes title, link, and content/excerpt.
- AI Content Creation (OpenAI Module)
- Add the OpenAI module (ChatGPT model) or Claude by Anthropic.
- Create a prompt like:”You are a copywriter creating LinkedIn posts for a B2B audience. Write a short, engaging post that summarizes this blog. Include a 1-line hook, 1–2 insights, and end with a call-to-action. Blog title: {{title}}, Excerpt: {{content}}.”
- Choose GPT-4o for Claude Sonnet 4 (I prefer Claude).
- Output should be plain text.
- Routing Options (Router Node)
- You can either insert a Router node after the OpenAI output or do this in a sequence (like my setup above).
- Route A: Manual Review
- Add Slack module.
- Post the AI-generated copy to #marketing-content.
- Include buttons for “Approve” or “Revise” via Slack reactions or separate review workflows.
- Route B: Auto Schedule
- Add Buffer or LinkedIn module.
- Schedule the post directly.
- Add time delay if needed before posting (e.g., delay by 30 min to allow override).
- Log It (Google Sheets or Airtable)
- Add a Google Sheets or Airtable module.
- Create a row with blog title, link, and generated post.
- Optional: Include timestamp and user who approved the content.
Optional Enhancements:
- Add a “fallback content” path if AI fails or times out.
- Use a Make “Text Parser” to clean up or trim content to fit platform character limits.
- Add UTM parameters to links using a “Set Variable” step before publishing.
Why this helps: This flow cuts down repetitive work, ensures content consistency, and keeps your distribution engine running on autopilot with human review only when needed.
Project 2: AI Campaign Performance Digest
Scenario: When I started my career in marketing, over a decade ago, before AI, I would manually compile Google Ads campaign reports every Monday morning. Today, AI does it for you, and shares a clean summary to Slack every morning.
How it works: Make.com runs a scheduled workflow each morning. It pulls campaign data from Google Ads, sends it to GPT-4o with a prompt designed to extract insights, and then posts a summary digest to a Slack channel.
Step-by-Step Walkthrough:
- Trigger on Schedule:
- Use the “Scheduler” module in Make.
- Set the time to run daily at 8:00 AM (or whatever cadence fits your reporting cycle).
- Fetch Campaign Data (Google Ads Module):
- Add a Google Ads module.
- Authenticate with your account and select the appropriate campaign.
- Configure it to retrieve key metrics like impressions, clicks, CTR, cost, conversions, and ROAS.
- Ensure the output is formatted clearly to be used in the next step.
- Summarize Metrics (OpenAI Module):
- Add an OpenAI (ChatGPT) module.
- Use a system + user prompt combo to ensure structured output: System: “You are a digital marketing analyst summarizing ad performance.” User: “Summarize the following Google Ads metrics in 3 concise bullet points. Highlight performance trends, wins, and concerns. Metrics: {{output from Google Ads module}}”
- Choose GPT-4o for better language quality and reliability.
- Post to Slack (Slack Module):
- Add the Slack module and connect your workspace.
- Send the AI summary to your marketing channel (e.g., #ads-daily).
- Format the message cleanly using markdown, and optionally include a link to the Google Ads dashboard for deeper inspection.
- Log for Reference (Optional):
- Add a Google Sheets or Airtable module to log the raw metrics + AI summary.
- Include a date stamp and campaign ID for tracking trends over time.
Optional Enhancements:
- Add a fallback message if AI output is blank or token limits are exceeded.
- Use a router to conditionally summarize different campaign types differently (e.g., brand vs. performance).
- Include comparison to previous day or week by pulling two data sets and calculating diffs before sending to GPT.
Why this helps: It delivers a high-signal snapshot of ad performance daily without wasting your time, and keeps everyone on the same page.
Project 3: AI Content Research Assistant
Scenario: You’re planning a new blog post, campaign, or social series and need quick, high-quality content research. Instead of spending hours googling stats, quotes, and trending ideas, let an AI-powered automation do the heavy lifting.
How it works: You input a topic into Airtable (or another database), which triggers a workflow in Make.com. The AI uses that topic to generate:
- A list of content angles
- Related stats or facts
- Popular subtopics or related trends
- Potential hooks or titles
Everything gets logged into a Google Sheet or Notion database for review and use.
Step-by-Step:
- Trigger: Airtable Record Created
- Use the Airtable “Watch Records” module.
- Set it to monitor a “Content Ideas” table.
- Capture fields like: Topic, Target Audience, Tone (optional).
- AI Research Prompt (OpenAI Module):
- Add OpenAI ChatGPT-4 module.
- Prompt:”You are a content strategist researching ideas for a blog post or campaign. Given the topic ‘{{Topic}}’ and the audience ‘{{Audience}}’, generate:
- 3 content angles
- 3 surprising stats or insights with real examples
- 3 hook ideas or headline starters. Format clearly with numbered sections.”
- Parse and Organize (Text Parser or Set Variables):
- If needed, extract each section into separate fields using Text Parser or Set Variable modules.
- Log to Google Sheets or Notion:
- Add a new row with:
- Topic
- Audience
- Generated angles
- Hooks/headlines
- Suggested stats
- Add a new row with:
- Optional Enhancements:
- Add a Slack notification: “New content research ready for review!”
- Add a filter so only topics marked as “High Priority” trigger AI research.
Why this helps: You eliminate blank-page paralysis and get rich, contextual research for any content initiative without wasting your team’s time or creativity on preliminary digging.
The Sales Bottleneck: Manual Follow-Ups & Cold Data
Sales teams waste hours every week:
- Manually sorting through low-quality leads
- Writing cold emails from scratch
- Logging CRM updates by hand
- Missing follow-ups because of clunky tools
With Make.com and AI, you can automate the entire pre-sale pipeline—from qualification to enrichment to personalized outreach—while still keeping it human where it counts.
Project 1: AI Lead Qualification & Outreach Workflow
Scenario: Automatically qualify new leads and kick off personalized outreach. Imagine you have a web form or marketing funnel capturing leads. Instead of manually sifting through them, we’ll build a Make.com workflow that uses AI to evaluate each lead’s potential and respond accordingly. High-quality leads will get a custom email (drafted by AI) and be logged in a CRM, while unqualified ones might get a polite decline or be deprioritized.
How it works: Whenever a new lead comes in (with details like name, company, message, etc.), the workflow triggers. It sends the lead info to an AI (GPT-4o) to determine if the lead is “Qualified” or “Not Qualified,” along with reasoning. Based on the AI’s decision, Make branches into different actions.
Step-by-Step:
- Trigger on New Lead:
- Use a Webhook module if your lead form sends a webhook
- Or use a Google Sheets module if leads are collected there
- Or integrate with your CRM (HubSpot, Pipedrive, etc.)
- AI Qualification (OpenAI):
- Add OpenAI (ChatGPT) module
- Prompt:System: “You are a sales assistant that qualifies leads for our business.” User: “Lead details: Name: {{name}}, Company: {{company}}, Message: {{message}}. Based on this, decide if this lead is Qualified or Not Qualified for our services, and provide a brief reason. Respond in the format: Qualified/Not Qualified – Reason.”
- Branching Logic (Router or IF):
- Use an IF or Router module to check if the response contains “Qualified.”
- Route accordingly:
- Qualified → Follow-up path
- Not Qualified → Logging or polite response
- Qualified Lead Path:
- Generate Email: Use another OpenAI module to draft a personalized email:Prompt: “Write a friendly email to this lead introducing our services. Use this info: {{lead data + qualification reasoning}}.”
- Send Email: Use Gmail or SMTP module to send the AI-generated message.
- Log Lead: Add/update lead in your CRM or Google Sheet.
- Unqualified Lead Path:
- Polite Decline (Optional): Use GPT to generate a kind “not the right fit” email.
- Internal Log: Mark the lead in CRM or Sheet as disqualified.
- Test the Workflow:
- Use test leads to verify AI outputs and routing logic.
- Ensure prompt format is consistent for accurate branching.
Bonus Ideas:
- Human-in-the-loop Review: Send AI-drafted email to Slack for approval before sending.
- Scoring instead of binary: Ask AI to score Hot, Warm, Cold.
- Enrichment before AI: Use Clearbit or Apollo API to add job title, company size, industry.
Why this helps: Your sales team only sees high-quality leads and can follow up instantly with personalized, AI-written messages.
Project 2: AI-Powered CRM Enrichment & Follow-Up
Scenario: Automate enrichment for CRM records and schedule follow-ups based on lead type.
How it works: Whenever a new contact is added to your CRM (or manually tagged), the workflow enriches the contact (e.g. via Clearbit), uses AI to suggest next actions, and schedules a follow-up reminder.
Step-by-Step:
- Trigger: Watch for a new CRM contact (e.g., HubSpot “New Contact” trigger).
- Enrichment: Call Clearbit or similar API to retrieve job title, company data.
- AI Recommendation: Use OpenAI:Prompt: “Based on this lead info, suggest next sales action and urgency level. Respond with a 1-sentence summary.”
- Create Task: Add to Trello/Asana/Google Calendar or CRM task board.
- Notify Salesperson: Slack message or email summary with AI’s next step.
Why this helps: Keeps your CRM smart and your reps focused on the right next step.
Project 3: AI Deal Progress Updates to Stakeholders
Scenario: Keep internal stakeholders updated as deals progress, without constant emails or meetings.
How it works:
When a deal stage changes in your CRM, AI summarizes deal context and posts an update to a Slack channel or email digest.
Step-by-Step:
- Trigger: Watch for deal stage change (e.g. from “Demo” to “Negotiation”).
- Pull Context: Use previous notes or contact data.
- AI Summary: Prompt:“Summarize this deal update with name, stage, client concern, and next step. Make it brief but informative.”
- Send Digest: Post to Slack #deals or email manager/team.
Why this helps: Reduces status meetings while keeping everyone aligned.
Automation For Product Teams
Product managers juggle user feedback, bug reports, feature requests, competitor research, roadmap planning, internal prioritization, and stakeholder updates, all at once. It’s chaos. And most of it is repetitive, noisy, and hard to scale.
With Make.com and AI, you can:
- Digest qualitative feedback in minutes
- Summarize feature requests by theme
- Classify bugs and assign owners
- Monitor competitor news
- Auto-generate user stories and release notes
Let’s walk through a few real workflows.
Project 1: Feature Request Summarizer & Classifier
Scenario: Users submit feature requests through a form, support tool, or product portal. Instead of manually reviewing each one, this automation summarizes and categorizes requests using AI, then logs them in your product management system.
How it works: A new request triggers the workflow. AI (via GPT-4o) reads and classifies the submission (e.g., UX, performance, integrations), writes a short summary, and sends the data to Airtable or Notion for prioritization.
Step-by-Step:
- Trigger: Form Submission or Inbox Monitoring
- Use the “Webhook” module if collecting feedback via a form (e.g., Typeform, Tally).
- Or use the “Gmail” or “Intercom” module to watch for new support emails or messages.
- Capture key fields: name, email (optional), feature request text, and source.
- AI Summarization and Categorization (OpenAI Module):
- Add the OpenAI module.
- Use the following prompt:”You are a product manager assistant. Summarize the following user feature request in 1–2 sentences. Then categorize it as one of: UX/UI, Performance, Integrations, New Feature, Other. Respond with: Summary: … / Category: …”
- Process Output (Text Parser, Set Variable):
- If needed, parse out “Summary:” and “Category:” into separate fields.
- Log to Product Tracker (Airtable/Notion/Google Sheets):
- Add a module to write the summary, category, and source to your product request tracker.
- Optional: Add a timestamp and auto-assign priority if source = “VIP” or “internal.”
- Bonus Enhancements:
- Add Slack notifications to alert the product team when a new high-priority request is submitted.
- Use a Router node to auto-tag requests into different buckets (e.g., roadmap now/later/backlog).
Why this helps: Instead of skimming dozens of tickets, PMs see a categorized, summarized list ready to evaluate in minutes.
Project 2: Bug Report Classifier and Assignment
Scenario: Your support team logs bugs from users. Instead of having a PM manually triage and assign each one, this workflow uses AI to determine severity and auto-assigns to the right team or Slack channel.
How it works: When a new bug report is added to your tracking system (e.g., Airtable, Google Sheet, or Intercom), the workflow triggers. GPT-4o reads the bug report, labels it by severity, recommends the team, and routes the report to a Jira board or Slack for resolution.
Step-by-Step:
- Trigger: New Bug Logged
- Use “Airtable – Watch Records” or “Google Sheets – Watch Rows.”
- Trigger on a new row in your “Bugs” table with fields: Description, Environment, App Version, Submitter.
- AI Classification (OpenAI Module):
- Add the OpenAI module.
- Prompt:”You are a technical triage assistant. Read this bug description and assign: a) Severity: Low, Medium, High b) Team: Frontend, Backend, Infra, QA Description: {{bug_text}} Respond: Severity: … / Team: …”
- Parse Output:
- Use a Text Parser or Set Variable module to extract the fields.
- Routing & Assignment (Router + Slack/Jira):
- Use a Router module to route based on team.
- For each branch:
- Slack: Send bug summary to respective team channel
- Jira: Create issue with pre-filled metadata
- Log Final Record:
- Update Airtable/Sheet with AI’s classification, routing action, and date.
Why this helps: Triage happens instantly, teams are alerted without delay, and engineering isn’t bogged down by unclear, unprioritized issues.
Project 3: Competitor Research Digest
Scenario: Your product team wants to monitor competitor news (feature launches, pricing changes, new positioning) but no one has time to check their blogs or Twitter every day. Let automation do it for you.
How it works: Make.com monitors competitor blogs or news feeds using RSS. New content is piped into GPT-4o, which extracts relevant summaries and logs them to Notion or shares them in Slack.
Step-by-Step:
- Trigger: RSS Feed Monitoring
- Use the “RSS Watch Items” module.
- Add feeds from competitor blogs (e.g., /news, /blog/rss).
- Trigger the scenario when new items appear.
- AI Summary (OpenAI Module):
- Add the OpenAI module.
- Prompt:”You are a product strategist summarizing competitor updates. Summarize the following blog post in 2–3 sentences. Focus on new features, strategic changes, and pricing or positioning shifts.” Input: {{rss_content}}
- Routing and Output:
- Slack: Send formatted summary with post link to #product-intel
- Notion: Append to a Competitive Insights database (Title, Summary, Source URL, Date)
- Optional Enhancements:
- Add a keyword filter (e.g., only send if post mentions “AI,” “pricing,” “feature,” etc.)
- Use sentiment analysis to mark as positive/negative/neutral (another AI call)
Why this helps: Keeps product and strategy teams aware of external moves without manual research, freeing time for response planning or differentiation work.
Project 4: Generate User Stories from Feedback
Scenario: You’ve collected raw user feedback from forms, surveys, support tickets, or customer interviews. Now you need to turn that messy, unstructured input into clear, actionable user stories. Let AI write the first draft for your backlog.
How it works: Whenever feedback is marked as actionable or tagged with “feature request,” Make.com sends it to GPT-4o. The AI rewrites it in proper user story format and logs it to your dev tracker (Notion, Airtable, Trello, Jira, etc.).
Step-by-Step:
- Trigger: Tagged Feedback Entry
- Use “Watch Records” (Airtable) or “Watch Database Items” (Notion).
- Set a filter: Only run if field ‘Type’ = “Feature Request.”
- Prompt AI to Generate User Story (OpenAI Module):
- Prompt:”You are a product manager preparing backlog items. Turn this raw feedback into a user story using this format: ‘As a [user role], I want to [goal/action], so that [benefit].’ Feedback: {{feedback_text}}”
- Post-processing (Optional):
- Add a sentiment analysis module (e.g., another AI call) to assess urgency.
- Use Router to assign story to the correct product squad based on keyword/topic.
- Log Story:
- Notion: Add to product backlog database
- Airtable: Insert as a new story row
- Jira/Trello: Create new ticket with AI-generated description
- Notify Stakeholders (Optional):
- Slack alert to product owner: “New story added from feedback: {{story}}”
Why this helps: Turns raw, unstructured user data into clean, consistent backlog items—without product managers rewriting every ticket themselves.
HR Teams: Automate Onboarding and Employee Insights
HR teams are buried under repetitive, time-consuming tasks:
- Answering the same policy questions again and again
- Sorting resumes manually
- Drafting internal emails and updates
AI automations free up time for strategic people ops work while giving employees faster responses and a better experience.
Project 1: AI-Powered HR Slack Assistant
Scenario: Employees constantly ask HR about leave policies, benefits, or internal procedures. This workflow creates an AI-powered Slack bot that answers common questions instantly.
How it works: Employees post questions in a designated Slack channel. Make.com captures the question, sends it to GPT-4 (with your handbook or policies as context), and posts the AI-generated answer back in the thread.
Step-by-Step:
- Trigger:
- Use Slack’s “Watch Messages in Channel” module
- Monitor #ask-hr or a similar channel
- AI Response (OpenAI):
- Prompt: “You are an HR assistant. Use the following context from our handbook to answer questions. If you don’t know the answer, say so. Question: {{message_text}}.”
- Provide a static section of company policy or use a database API to insert context
- Respond in Thread:
- Post the AI-generated answer as a reply to the original Slack message
- Fallback Handling:
- If AI is unsure, route to a human HR rep with a notification
Why this helps: Reduces HR interruptions while improving employee experience with instant, contextual answers.
Project 2: Resume Screening Assistant
Scenario: You receive a high volume of applicants and need to quickly assess fit for a role based on resumes and job descriptions.
How it works: Applicants submit resumes through a form or ATS. Make.com collects the submission, sends it to GPT-4 with the job description, and receives a scored summary with a short rationale.
Step-by-Step:
- Trigger:
- Watch new form submission or integrate with ATS (e.g., Google Form, Typeform)
- Collect name, resume text (or file), and job applied for
- AI Fit Evaluation (OpenAI):
- Prompt: “You are an HR recruiter. Based on this job description and the applicant resume, rate this candidate as High, Medium, or Low Fit. Provide a 1-sentence reason.”
Input: {{resume}}, {{job_description}}
- Prompt: “You are an HR recruiter. Based on this job description and the applicant resume, rate this candidate as High, Medium, or Low Fit. Provide a 1-sentence reason.”
- Parse Response:
- Extract score and reason using text parser or Set Variable
- Log Result:
- Add to Airtable or Google Sheet for internal review
- Optional:
- Notify hiring manager via Slack if rating is “High Fit”
Why this helps: Quickly filters high-potential candidates without sifting through every resume manually.
Project 3: Personalized Onboarding Sequence Generator
Scenario: New hires need to go through onboarding. Instead of sending the same emails manually or giving them generic documents, generate a tailored onboarding plan.
How it works: When a new hire is added to Airtable or your HRIS, GPT-4o generates a personalized onboarding checklist and intro email based on their role and department.
Step-by-Step:
- Trigger:
- Watch for a new employee record in Airtable (or Google Sheet or BambooHR)
- Generate Plan (OpenAI):
- Prompt: “You are an HR onboarding assistant. Based on this employee’s name, role, and department, write a custom onboarding checklist for their first 2 weeks. Also generate a welcome email.”
- Send Outputs:
- Email the onboarding checklist and welcome message to the new hire
- Optionally send a copy to their manager
- Log or Archive:
- Save plan to a shared onboarding doc or Notion database
Why this helps: Makes onboarding feel personal and organized without HR lifting a finger.
Support Teams: Automate Ticket Triage And Responses
Customer support is repetitive by nature, but that doesn’t mean it should be manual. With AI and automation, you can:
- Instantly classify and route tickets
- Auto-draft replies to common questions
- Summarize conversations for handoffs or escalations
- Proactively flag critical issues
Let’s break down a few powerful workflows you can launch today.
Project 1: AI-Powered Ticket Triage Bot
Scenario: Incoming support tickets vary widely. Some are technical, others are billing-related, some are spam. Instead of human agents triaging each one manually, AI can analyze and route them to the right person or tool.
How it works: Make.com monitors your support inbox or form. For each new ticket, GPT-4o classifies it (Billing, Technical, Account, Spam) and assigns urgency. Based on the result, the ticket is routed to the correct Slack channel, person, or tool.
Step-by-Step:
- Trigger:
- Watch new entries from Gmail, HelpScout, Intercom, or a form tool like Typeform.
- Capture subject, message body, and metadata.
- Classify Ticket (OpenAI):
- Prompt:“You are a support assistant. Read the message below and categorize it as one of: Billing, Technical, Account, Spam. Also assign an urgency level (Low, Medium, High). Respond like: Category: …, Urgency: …”
- Parse Output:
- Use a Text Parser or Set Variable module to extract Category and Urgency
- Route Based on Logic:
- Use a Router or Switch module
- Route Technical → #support-dev, Billing → #support-billing, etc.
- Notify urgent issues in a priority Slack channel or tag a team lead
- Log for Analytics (Optional):
- Save categorized tickets to Airtable or Sheets for trend tracking
Why this helps: Your team spends less time sorting and more time solving. Escalations are never missed.
Project 2: AI Auto-Responder for Common Questions
Scenario: Many support tickets are variations of the same FAQ: password resets, refund policies, shipping delays. Let AI draft helpful responses automatically, ready for human review or direct sending.
How it works: When a new ticket arrives, GPT-4o reviews the content and drafts a relevant reply using company policy snippets or a knowledge base.
Step-by-Step:
- Trigger:
- Monitor new support tickets via Help Desk or form integration
- Draft Response (OpenAI):
- Prompt:“You are a support rep. Read this customer message and write a helpful reply using our policies: {{kb_snippets}}. Message: {{ticket_text}}”
- Review Flow:
- Send AI draft to Slack for human review (or assign a Google Doc comment task)
- Use Slack emoji as approval trigger, or set manual override option
- Send Response:
- Upon approval, send email via Gmail, Outlook, or HelpDesk API
Why this helps: Reduces response time for repetitive inquiries and gives your team a first draft to edit instead of starting from scratch.
Project 3: Conversation Summary Generator for Escalations
Scenario: When tickets get escalated across teams, agents spend time writing summaries of what’s happened so far. Use AI to generate this summary instantly.
How it works: When a ticket is tagged for escalation or transfer, Make.com grabs the conversation thread and asks GPT-4o to summarize the key points.
Step-by-Step:
- Trigger:
- Tag change or status update in HelpScout/Intercom (e.g., “Escalated”)
- Summarize Conversation (OpenAI):
- Prompt:“Summarize this customer support conversation: who is the customer, what’s the issue, what’s been tried, and what’s needed next. Format as: Summary: … / Next Action: …”
- Send to Escalation Path:
- Post to Slack or assign Jira/Trello task with summary included
- Tag original agent and team lead
Why this helps: Handoffs are cleaner, faster, and no critical context is lost.
Start with something small
The best way to get started building automations is with something small and non-critical. If it fails, it shouldn’t bring the house down.
Over time, as you get comfortable with this, you can add on more complexity and transition from automations to autonomous AI agents.
If you need help with any of this, email me.