Migrating from Zapier to Make is less painful than most people expect — especially when AI is doing the heavy lifting on the build side. The process has a clear shape: inventory what you have, categorize by complexity, brief your AI in batches, review the outputs, test, and cut over. Done in that order, it is methodical. Done any other way, it turns into a weekend you will not get back.

The migration workflow described here connects directly to the broader conversation about using AI to accelerate Make scenario builds in production. That context matters because migration is not a special case — it is just a concentrated version of the same build-brief-review loop you will use every time you build anything with AI assistance going forward.

One clarification before we start: AI handles the construction work. You handle the business logic audit. Those are two different jobs and they do not overlap. Keep them separate throughout this process.

What Is the Short Answer?

Inventory your Zaps, sort them by complexity, write a structured brief for each workflow you want rebuilt in Make, hand those briefs to Claude or your AI tool of choice, review the output against your business logic, test in a sandbox environment, then cut over workflow by workflow — not all at once. The whole migration is a series of small, reviewable steps. Not one big switch.

Before You Start

Do not start building in Make until you have done the inventory. This is where most migrations go sideways — people start rebuilding their most familiar Zaps first, then discover halfway through that the simpler ones have hidden dependencies on the complex ones they skipped.

You also need to understand what Make handles differently than Zapier. The data model is different. Zapier thinks in linear steps; Make thinks in connected modules that can branch, loop, and route. That is not a downgrade — it is more capability — but it means a direct one-to-one rebuild is not always the right move. Some Zaps are better rebuilt as a tighter, smarter scenario once you understand what Make can actually do.

Gather these before you touch anything:

• A full export or manual list of every active Zap — name, trigger app, action apps, and whether it is a single-step or multi-step
• Login credentials and API keys for every connected app
• A Make account with enough operations headroom for testing
• A simple spreadsheet to track migration status per workflow

Step 1: Is Your Zap Inventory Complete and Categorized?

Start in Zapier’s dashboard. Go through every Zap — active and paused. Paused Zaps are not dead; they are often workflows someone meant to fix and forgot about. Document each one.

Once documented, sort by complexity. A simple framework that works:

• Tier 1 — Single-step Zaps: One trigger, one action. These are the easiest to rebuild and the best starting point for testing your AI-brief process before you tackle anything complex.
• Tier 2 — Multi-step, linear Zaps: One trigger, two to five sequential actions, no conditional logic. Still straightforward but require complete documentation of every step.
• Tier 3 — Zaps with filters, paths, or custom code: Conditional logic, Zapier Paths, JavaScript/Python code steps, or anything with a formatter step doing heavy transformation. These need the most precise briefs and the most careful review.

Do your Tier 1s first. They build your confidence in the process and help you develop a brief format that works before you hit anything complicated.

Step 2: How Do You Write a Brief That AI Can Actually Build From?

This is the step most people underinvest in. The brief is everything. Claude — or any AI tool — builds exactly what you describe. Vague input produces vague scenarios. Specific input produces working ones.

A solid brief for a Zap-to-Make migration covers:

• Trigger: What app fires the scenario, what event triggers it, and any filter conditions on the trigger
• Data inputs: Every field that flows from the trigger and how it is used downstream
• Actions in order: Each action, the app it touches, what data it sends or receives, and what happens if it fails
• Conditional logic: If any step only runs under certain conditions, spell out the condition explicitly — do not assume the AI will infer it
• Error behavior: What should happen when a module fails — retry, skip, notify, halt
• Output expectations: What the end state looks like when the scenario runs successfully

Batch your briefs by tier. Do all your Tier 1 briefs as a set, submit them together, review together. Do not mix tiers in a single batch — the review complexity compounds.

One practical note from experience building with Make’s MCP: once you seed your AI setup with real Make JSON so it understands the module structure, it fills in credentials and module internals correctly without you having to specify every technical detail. That seeding step happens once and pays dividends across every brief after that. See the full field report on AI-assisted Make builds for how we handle that in practice.

Step 3: What Does the Review Process Look Like Before You Test?

AI builds the scenario. You review the business logic. These are not the same job.

When you get a built scenario back — whether from Claude via MCP, from a JSON output you import into Make, or from any other AI-assisted build method — your review has two layers:

Layer 1 — Technical completeness: Are all modules present? Are the connections mapped correctly? Are HTTP modules formatted properly if the workflow touches an API without a native Make app? Is error handling present on modules that could fail?

Layer 2 — Business logic accuracy: Does this scenario actually do what the Zap was doing? Does the data flow to the right fields? Does conditional logic fire under the right conditions? Does the failure behavior match what your business actually needs?

Layer 1 is fast — you are scanning structure. Layer 2 takes longer because only you know whether the business logic is right. No AI can verify that for you. For a more detailed breakdown of this review process, the pre-production evaluation checklist covers both layers in depth.

Step 4: How Should You Test Before Cutting Over?

Do not test against live data first. Make has a scenario testing mode that lets you run with sample data before you activate anything. Use it.

For each scenario, run through this sequence:

1. Run the scenario manually with test data in Make’s builder — watch each module execute and confirm the data passing between modules looks right
2. If the scenario touches external systems, confirm the records land correctly in those systems — do not assume a green check in Make means the downstream data is accurate
3. Test at least one error condition — disconnect a module, pass bad data, or simulate a failed API call — and confirm your error handler fires the way you told it to
4. Run the scenario a second time with production-representative data before you mark it ready

The gap between AI-built scenarios and manually built ones in terms of test failure rate is smaller than most people expect — but it does not go to zero. That comparison is worth reviewing if you want a realistic picture of where AI-built scenarios succeed and where they still need human review.

Step 5: What Is the Right Cutover Sequence?

Workflow by workflow. Not all at once.

Pick your least critical, cleanest Tier 1 scenario to go first. Activate it in Make. Pause the equivalent Zap — do not delete it yet. Watch the Make scenario run for two to three business days. Confirm records, confirm outputs, confirm nothing is falling through the cracks.

Once that scenario is confirmed stable, move to the next. Build your confidence in the process before you migrate anything business-critical. Only after your Tier 1s are running cleanly should you move to Tier 2 and eventually Tier 3.

When a scenario has been running in Make for at least two weeks without issues, delete the Zap. Until then, keep it paused as a fallback. Do not let the paused Zaps run in parallel — you will create duplicate records and confusion. Paused means off, just not yet deleted.

How Do You Know the Migration Worked?

The migration is complete when:

• Every Zap on your original inventory has a corresponding active Make scenario or has been intentionally retired
• All active Make scenarios have run successfully in production for at least ten business days
• No downstream system is reporting missing records, duplicate records, or data errors that did not exist before migration
• Your team knows which workflows moved to Make and where to find them
• Your Zapier account has been canceled or downgraded — money saved confirms completion

What Are the Most Common Mistakes in This Migration?

Rebuilding instead of rethinking. Some Zaps exist the way they do because of Zapier’s limitations, not because the workflow design is right. Migration is a good moment to ask whether the logic still makes sense. If it does not, redesign before you rebuild.

Vague briefs. Telling AI to “rebuild my Zap that syncs contacts” without specifying which fields, which direction, which trigger condition, and what happens on conflict will produce a scenario that looks complete and does not work correctly. Write the brief like you are explaining it to a new hire on day one.

Skipping error handling. Zapier handles errors quietly — usually by stopping and emailing you. Make’s error handling is more powerful but only if you build it in. If you brief a scenario without specifying error behavior, the AI may leave modules without error routes. Add routed error handling to every brief.

Testing in production. Make has a testing environment. Zapier doesn’t, really. Use Make’s testing mode. The cost of a bad run in production — duplicate records, misfired notifications, partial data writes — is higher than the time it takes to test properly.

Cutting over everything at once. The staged cutover sequence exists for a reason. One bad scenario in a batch migration creates chaos and makes it hard to identify the source. One bad scenario in a staged migration is a five-minute fix.

Information in this article is deemed to be accurate at time of publishing. 4Spot Consulting reviews and updates content periodically as best practices evolve.

Related Reading

• AI and Make in production: what we learned building with Claude
• Make vs. Zapier in 2026: an operations-focused comparison
• AI-assisted Make builds vs. manual builds: how they actually compare
• How to evaluate an AI-built Make scenario before it goes to production

Sources & Further Reading

• Zapier vs. Make: An Honest Comparison — Zapier
• What Is AI Automation — Make.com
• Introducing Make AI Agents — Make.com
• Scenarios Documentation — Make Help Center
• 32+ Business Process Automation Statistics — DoIT Software