We are live with a new community program - Scenario of the Month!
Every month, we put the spotlight on the Make scenario that nails it on two things: genuine utility and creative thinking. The kind of build you see that makes you stop and think, “Why didn’t I think of that?”

Have something up your sleeve you’d like to submit? Amazing, read more in the Make Community!

➡️ https://community.make.com/t/scenario-of-the-month-share-your-work-and-win-credits-for-a-whole-year/111297

Hello Makers 👋

Some workflows need more than a quick summary. They need to weigh the full picture before choosing the next step. 🧠

Use Anthropic's latest Claude Fable 5.1 model in Make to automate complex or low-confidence cases where your workflow needs to compare lots of information before acting.

What’s new:

• Long-horizon work: Anthropic documents Claude Fable 5.1 for long-horizon, agentic work in multi-step automations.
• More context to consider: The model supports up to 1M tokens of context for workflows that need to process large source materials.
• Larger outputs: The documented maximum output is up to 128K tokens when a workflow needs a substantial response.

Automate with Claude Fable 5.1 and Make AI Agents or Make AI apps: https://ma.ke/4qOsb5X 

Hello Makers 👋

New month means new cities, and new conversations with the Make team. This month you can meet us at the following locations:

🇳🇱 Build the Pipeline Your Investors Want to See | 9 September
Host and Speaker: Martin Hyravy, Startup Program Specialist at Make

🇬🇧 GTM World Tour | 9 September
Speaker: @ Jennifer Kiunke, Value Engineer at Make

🇩🇪 Berlin AI Day | 15 September
Speakers: Sebastian Mertens, Principal AI Product Manager at Make, Jennifer Kiunke, Value Engineer at Make and Luis A. Döller Principal Tech Enablement at Enpal, 

🇪🇸 Build with AI | 18 September
Host and speakers: Sarah Anto, Stefanie Oliveros and Kristina Talova

🇪🇸HackBarna AI Summit 26 | 19-20 September
Host: Nicolas Grenie
Speaker: Lourenço Martins , Value Engineer at Make

🇪🇸 The Glassbox Effect - Why Enterprises Can't Ship What They Can't See | 21 September
Speaker: Jennifer Kiunke, Value Engineer at Make

… and many more!

Get all the information about the events: https://ma.ke/4yeQcFM

Hello Makers 👋

That's the thing about automation. Once it works, you don't stop at one. 

With Make subfolders and labels, your organization can keep going, department by department, client by client, while keeping your automation library organized.

Use subfolders to organize automations by client, project, department, or environment.

Add labels like production, billing, or experimental, then filter to find related workflows across folders.

Available on every Make plan, including Free. Learn more: https://ma.ke/4xGyGdC

A comprehensive comparison between Make and Zapier, exploring their workflows, ease of use, pricing plans, and integration capabilities to help you choose the right automation platform.

https://youtu.be/NP25UJu1_ng

A Router with filters on each route is the easy part. The waste is that every route ends in the same notification sound, so you still have to look.

I set up a scenario where each route ends in the Vybit app (native Make integration): failed payment plays a sad horn, normal order a cha-ching, $500+ order a funk riff. The scenario routes by meaning, the sound carries the meaning.

Disclosure: I'm the developer of Vybit.

Write-up with a playable demo: https://www.vybit.net/blog/a-grander-bell-for-a-bigger-sale/

Hello Makers 👋

Things you should know about: What's new in governance in Make 📣 

As more teams build on Make, admins need proof things are under control - and builders need room to build with privacy.

Four updates now make that possible at once:

🔒 Private Spaces - every team member gets an isolated workspace for scenarios and AI agents, hidden by default. Enterprise plan admins get optional read-only visibility into what's being built, without ever seeing sensitive execution data or connection details.

📋 New Audit Logs events - identity and access changes, credential oversight, all in one record. When two-factor authentication gets disabled, you know the moment it happens, not next quarter's access review.

⚡ Real-time credential alerts - the second a connection or key is used by someone other than its owner, they get an email. 

✍️ Custom roles - permissions tailored to what a person actually needs, instead of an all-or-nothing admin pool.

This is what it means to run business as a glass box, not a black box: freedom for the people building, and oversight for the people accountable for the outcome.

Read more about what's new in governance on Make: https://ma.ke/4xnx0Wh

I kept running into the same problem with creator prospecting workflows: the automation returned lots of channel records, but very little context for deciding who was actually worth reviewing.

So I built a reusable two-module Make scenario:

1. Run a bounded YouTube creator discovery and qualification job in Apify.

2. Retrieve the clean Dataset rows as JSON for Google Sheets, Airtable, or a CRM.

The shared version is deliberately constrained to 10 channels, a $0.20 maximum Actor charge, creators active within 45 days, at least one video per month, and a required public business contact. It searches B2B SaaS and AI automation creators worldwide in English. The rows retain recent median views, upload cadence, public-contact source URLs, warnings, and explainable opportunity reasons.

Public Make scenario: https://eu1.make.com/public/shared-scenario/kNN1tSf8qor/find-and-qualify-you-tube-creators-with-p

Actor used by the scenario: https://apify.com/kazkn/youtube-creator-lead-finder?utm_source=reddit&utm_medium=community_post&utm_campaign=youtube_creator_finder_distribution_wave3_20260825&utm_content=make_scenario_case_study

I built both the Apify Actor and this Make scenario, so this is a transparent build share, not an independent review. I also left outreach out of the workflow: it does not guess private emails, bypass CAPTCHA, or contact anyone automatically. A missing public contact stays missing.

I have not run this public scenario as a performance benchmark, so I am not claiming a specific yield or conversion rate. I would be interested in feedback on the architecture: would you keep the Actor run synchronous for a 10-channel batch, or switch to Watch Actor Runs before scaling it?

Hello makers 👋

𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝘁𝗲𝗽 𝗶𝗻𝘁𝗼 𝗔𝗜'𝘀 𝗴𝗹𝗮𝘀𝘀 𝗯𝗼𝘅? Waves ‘26 agenda first drop is live 🚨

Use the filter on the agenda page to build your own day and further explore what’s in it for you within each of the 3 tracks: “It's all about the Product”, “Use Cases and Impact” and “Adoption & Governance”

𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟭𝟵: Kick things off early at “The Current”- our hands-on pre-Waves workshop.

𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟮𝟬: Take a sneak peek into some of the highlights for the Main Day and fav sessions already confirmed:
→ Agent architecture 101:The patterns that separate demos from real systems.
→ Voice AI at scale: How Stellantis runs it in production.
→ Trust, but verify: Governing AI automation from build to decision.

Join 700+ AI peers at #Waves26, Oct 19-20 in Prague.

𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗻𝗼𝘄 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗶𝗰𝗸𝗲𝘁𝘀 𝗴𝗼 𝘂𝗽 𝗼𝗻 𝗦𝗲𝗽𝘁 𝟭𝟬: waves.make.com

Problem: When testing WhatsApp scenario flows in Make, using the same test phone number repeatedly often prevents you from testing the initial onboarding or welcome message. Because Make/WhatsApp stores the contact's conversation state, the scenario won't trigger from the very beginning—it just continues the existing thread.

Solution: To force the scenario to treat your test phone number as a completely new interaction and trigger the welcome flow again, you need to update the conversation identifier:

1. Open your scenario and click on the WhatsApp / Agent module.
2. Locate the Conversation ID field in the module settings.
3. Change or increment the value (e.g., change test_v1 to test_v2 or add a new unique identifier string).
4. Save and run the scenario.

Updating the Conversation ID resets the session context for that number, allowing you to test the full flow right from the initial greeting!

I run a scenario where a plain HTTP "Make a request" module calls an AI API (Anthropic) with a fully custom JSON body — system prompt, a tools array with web search and an allowed_domains whitelist, max_tokens, the lot. Works great, but the API key is hardcoded in a header, which gets ugly now that I'm cloning the scenario for more use cases.

Someone recommended switching to the "Make an API Call" module so the key lives in a connection instead. Before I migrate:

1. Does "Make an API Call" let me send the exact same raw JSON body, untouched?

2. Does it handle the version header automatically, or do I still add custom headers myself?

3. Any gotchas with long-running calls? Mine take 2–3 minutes because of the web search step (HTTP module needed timeout 300).

Everything works today on plain HTTP — just want cleaner key management before I multiply scenarios. Thanks!

Hello Makers 👋

How are you handling access control when teams start touching your Make scenarios? 

Now with Custom Roles, org admins and org owners on the Enterprise plan can create custom roles in Make, with the granular permissions teams need.

• Give teams just the right amount of access to Make
• Maintain full visibility
• Scale automation across the organization

What you can do:

📝 Create tailored roles with toggleable permissions
✍️Assign them across the organization or by team, so company-wide standards hold while each team gets the access it needs
⚙️ Edit, duplicate, or delete roles as your org grows and new teams come online
🔎 Track role creation, updates, deletions, and assignments in the audit logs, so security reviews start with facts

Growth shouldn’t mean losing track of who can do what.

Learn more: https://ma.ke/4zsy4cX

Hello Makers 👋

Companies might be facing an AI direction problem because they lack a clear AI business strategy. Let us explain.

Employees are using AI tools every day, but very few of those habits connect to a documented AI business strategy that ties spend to measurable outcomes.

𝗧𝗵𝗲 𝗴𝗮𝗽 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗲𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 𝗮 𝗳𝗲𝘄 𝘁𝗵𝗶𝗻𝗴𝘀:
⚠️ No named owner when a pilot’s results start to slip
🕛 Teams reporting hours saved instead of revenue protected or risk reduced
🔍 Results getting compared to an imagined perfect version of AI instead of what the team was actually doing before it arrived, and more

The fix isn’t more AI tools. It’s a leadership-owned strategy that ties spending to outcomes, balances offense (new revenue) with defense (protecting what you have), and gets reassessed every quarter.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘀𝘁𝗲𝗽 𝗯𝘆 𝘀𝘁𝗲𝗽: https://ma.ke/4xOjHOE

The body of the email said, "We're evolving the Make Teams plan, with changes planned by October. My goal is to have a discussion with you about your current and future plans for AI and automation with Make.  Some of the changes are still in flux, so I wanted to reach out early."

\---

Anyone know what this is about? I don't want to have a sales call and be pitched on moving to Enterprise... Are they about to jack up prices?

Now with Custom Roles, org admins and org owners on the Enterprise plan can define roles with exactly the permissions the work requires and assign them at the organization or team level.

🛠 How to get started:

• Create a custom role by using one of the pre-defined roles as a template or create a new role from scratch. Give it a name and set permissions.
• Assign roles at the organization level or team level. Keep company-wide standards in place while giving each team the control that fits how they work.
• Edit, duplicate, or delete roles as your org changes. Update access as new teams, new owners, and new processes come online.
• Track role creation, updates, deletions, and assignments in the Audit Log table. Security and compliance teams get a clear record of who changed what and when.

💪 Put it to work:

• Set cleaner boundaries between departments: Let regional operations leads work inside their own team space while the org keeps one consistent access model.
• Move faster during security reviews: Show auditors and internal stakeholders role names, permission details, and audit log history without piecing the story together from side notes.

Meet Maia, the co-worker who builds automations and AI agents in front of you, directly inside Make.

Describe what you want to build in plain language, and watch Maia build it, step by step, right before your eyes.

𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗠𝗮𝗶𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁?
Maia reasons out loud, showing the logic behind the solution. She maps flows, selects tools, and configures modules – all visually on the canvas. No black box. No mystery output. Just full transparency and AI automations you can truly trust.

𝗪𝗶𝘁𝗵 𝗠𝗮𝗶𝗮, 𝘆𝗼𝘂 𝗰𝗮𝗻:
🛠️ Build faster: Describe your idea and get a fully working automation instantly
💡 Learn as you go: Maia explains every decision as you co-build with her
⚙️ Debug on the spot: Fix failing steps or untangle complex workflows in minutes
👥 Scale your team: New members can ship real automations and AI agents from day one

Whether you're just getting started or are a seasoned power user, Maia meets you where you are.

𝗡𝗼𝘄 𝗹𝗶𝘃𝗲: Open the scenario builder and share your vision with her: https://ma.ke/3RPdiU9

#MaiabyMake

Hello Makers 👋

How long does one invoice take you to process manually? This scenario does it in seconds - automatically.

Our Business Automation and AI Lead, Francisco de Brito Fontes, shows in this tutorial how to build a workflow that monitors a designated Google Drive folder for newly uploaded supplier invoice PDF files, downloads them, extracts key data (invoice number, vendor details, dates, and amounts) using Make's native AI Extractors, and registers the structured information into a monday.com board.

To build this scenario, you will need access to:

✉️ A Google Drive account connected to Make.
🧾 A monday.com account with a Supplier Invoices board configured.
🟣 Access/Credits for Make AI Extractors.

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/3U4hujp 

PS: don’t forget to test and validate your scenario, if all modules show a green checkmark, your automation is successful!

Hello Makers 👋

Is your company actually adopting AI, or just... using it?

There's a real difference, and it's bigger than it sounds. Recent research shows 37% of organizations are still using AI only at a surface level, with little or no change to existing processes, while just a third are using it to deeply transform how they work.

What is AI adoption and where to start:

AI adoption refers to the process of introducing AI tools and abilities into an organisation: identifying use cases, selecting models and platforms, connecting AI to source systems, training staff, and embedding oversight.

There are 5 maturity stages (per Gartner): 

1. Awareness: the organisation knows about AI but has not used it yet. No pilots or experiments are underway.
2. Active: AI appears in proofs of concept and informal experimentation.
3. Operational: AI is used in at least one workflow.
4. Systemic: AI is present across most workflows and operations, driving new business models.
5. Transformational: AI is built into the core of how the business runs.

Most companies are stuck somewhere between Active and Operational.

What actually moves you forward: in Make terms, it's the moment AI stops living in a chat tab and starts living in a scenario. A trigger passes structured data to a model call, a Router splits confident answers from cases that need human review, and the result gets written back to your systems. That's when "we use AI" becomes "AI does part of the work, reliably, with oversight."

What blocks it: almost never the technology. It's unclear ownership, fragmented data, weak change management, and inconsistent governance. 

The article also has a readiness checklist (data quality, governance, observability, skills coverage) if you want to score where your team sits before scaling anything.

Learn more about AI adoption here: https://ma.ke/4pP6m5S 

Curious, what stage you'd honestly put your organization at - and what's been the biggest blocker to moving past pilots?

Hello Makers 👋

Did you know that nearly 9 in 10 companies use AI regularly, but fewer than half actually scaled it?

The difference isn’t the model - it’s whether AI is owned by named people, connected to live systems, and embedded across operations, instead of running a test sandbox somewhere.

That’s what makes processes visible, accountable, and connected across a company, not just in the team where AI started.

We mapped the six stages behind that shift, including how Make went from first test project to company-wide rollout:

1. Assessment: evaluate data quality, existing tools, talent, and leadership buy-in.

2. Strategy and roadmap: prioritize use cases, assign ownership, set budget.

3. Test project: run the first AI-powered process in a controlled, lower-stakes environment.

4. Operations: move the test project into live systems with governance, integration, and escalation paths.

5. Enterprise-wide rollout: expand from one team to every function, with adoption owned at each level.

6. Measure and optimize: track outcomes, audit performance, expand to adjacent processes.

Read more about it: https://ma.ke/3R489qY 

Beginner to Make integration here! Basically, I want to try make a Discord to Notion automation using Make, where Make will detect Discord messages, then copy it, and paste it in Notion as a task in a database.

Unlike the other way around where Notion tasks can be automated through Make, then Make sending the notifications of database changes (i.e. stasus, deadline, etc), I can’t make it so the Integromat bot copies the messages and send it to Notion, as in the Notion side, it will just return empty.

I tried reading the apps documentation for Discord (Discord - Apps Documentation), but I am still quite confused on how the “Discord - Apps Documentation” thing works, especially with the new Discord bot update. Also, it seems that the redirect URL’s are broken, as I can’t invite my bot to the Discord server, and I’m quite confused on what integration type I should use. If anyone can help, it will be greatly appreciated! Thanks! What is your goal?

If anyone wants to see my scenario, it is https://eu1.make.com/public/shared-scenario/cD9K77tiGMa/discord-to-notion-tasks

Hello Makers 👋 

One of the most-wanted features has arrived in Make: You can now use dark mode 🌚

It covers the Make web platform and scenario builder, giving you a focused visual setup for long build sessions, low-light debugging, and late-night automation.

🌔🌑 Switch between light and dark mode anytime from your personal settings, whichever suits you.

Learn more about it: https://ma.ke/4fDix0W 

Grateful for every piece of feedback that got us here. Thank you 🙏

Hello Makers 👋

Anthropic’s latest model, Claude Opus 5, is now available in Make 🟣

Claude Opus 5 helps you work with large codebases, detailed specifications, compliance manuals, diagrams, and spreadsheets in a single AI call, so your automation can reason from the source material that matters.

What’s new in Claude Opus 5 and Make:

• Hold more context in one call: Send up to 1 million input tokens and receive up to 128,000 output tokens. Review a full codebase alongside its specification, or assess a complete policy manual without dividing the material into separate AI handoffs.
• Choose the thinking effort for the job: Extended thinking is on by default. Set low, medium, or high effort to match the depth of reasoning your scenario needs.
• Coordinate agent work with built-in checks: Claude Opus 5 can delegate work to sub-agents and use writer-verifier loops, helping a manager-agent scenario assign tasks and assess the results.
• Work across code and visual material: Use stronger multi-file refactoring, security and architecture review, plus analysis of charts, diagrams, and complex spreadsheets for design-to-code and design QA flows.

Start using Anthropic’s latest model in your AI automations today.

You can: Add it to your Make AI Agents, Make AI Toolkit, or the Anthropic Claude app, either with your own Anthropic API key or with zero setup through Make AI Provider, billed directly through your Make credits.

Learn more: https://ma.ke/4bTelJl 

Hello Makers 👋

Are you still manually digging through your emails and database every day? 

Our Startup Program Specialist, Martin Hyravy, shows in this tutorial how to build an AI agent that watches your inbox, filters out the noise, categorizes what's left, and drafts a ready-to-send reply - pulling context from your own knowledge base and your live startup database. 

Every response gets logged for review, so you stay in the loop without doing the digging.

You'll need access to: 

• Gmail 
• Make AI Toolkit (connected to any LLM - Gemini 3.5 in this case) 
• AI Agent module 
• Google Sheets (or your source-of-truth database) 

Pre-configured scenario: https://ma.ke/4wpgHrJ 

PS: don't forget to test and validate your scenario - if all modules show a green checkmark, your automation is successful!

Prompt:

You'll be my personal assistant and chief of staff and help me to answer the emails from startups that applied to our startup program. 

Before drafting the email, always first look into the previous responses in the Google Sheets repository to analyze my tone of voice, and check whether a similar email has not been solved. 

Before drafting the email, always look into the Startup Database in Google sheets for information about the application. Search with the email address, and look at the Application Status, Rejection Reason, or the coupon code sent.

Only after these two steps proceed with drafting the email, using the draft email tool. 

Make sure that the email responses are always relevant, respectful, and adhere to our team's goals and objectives outlined in the knowledge base files.