I saw something about AI Copilot that claimed you can just describe what you want in plain English and get a working workflow back. That sounds almost too good to be true, and I’m skeptical about whether it actually delivers for apples-to-apples ROI comparisons.
The use case I’m thinking about is this: our CEO wants to evaluate whether we should stick with Make or move to Zapier. Right now, someone has to manually build out test scenarios in both platforms to compare setup time and costs. That takes days. The claim is that AI Copilot can turn a description like ‘pull customer data from Salesforce, enrich it with AI analysis, then push to Slack and store in a database’ into ready-to-run workflows automatically.
If that actually works, the time savings would be massive. You could prototype multiple scenarios overnight instead of waiting for someone to hand-build them. That means faster financial analysis and quicker decisions.
But I’m wondering about the reality check. Does it actually generate workflows that work on the first run, or does it create something that’s 80% there and needs significant rework? Because if it’s the latter, you’re not actually saving that much time—you’re just shifting the work around.
Also, when it generates a Make scenario versus a Zapier scenario for the same requirement, are they actually comparable? Or does it make different assumptions about how each platform would solve the problem, making the ROI comparison basically useless?
Has anyone actually used this for real comparisons? Does it work?
I tested this about two months ago with a data processing workflow. The AI Copilot generated something that was genuinely usable right away. I described wanting to pull data from an API, parse it, filter by date, then send results to email. It created a working workflow in maybe 30 seconds.
The thing is, it wasn’t perfect. It made some assumptions about error handling that I wouldn’t have made manually. But those assumptions were actually reasonable for a first pass. I didn’t have to start from scratch and build scaffolding.
What surprised me more was that when I asked for the same workflow in a different way—slightly different business logic, same end goal—it generated something structurally similar but with different steps optimized for efficiency. That suggests the generation isn’t just template matching.
For ROI comparisons specifically, I think the real value is speed of iteration. You can generate five scenarios quickly and pick the best one, rather than building two scenarios carefully. That’s a different workflow, but it works when your goal is finding the right approach, not necessarily the perfect approach.
The generated workflows do need review before production. Not heavy rework, but you’re not deploying them unchanged. That matters for your TCO calculation.
The plain text to workflow generation is real, but the comparison aspect is where it gets tricky. When you ask for the same automation in both Make and Zapier, you’re getting solutions that are platform-optimized, not logically identical.
Make has different native capabilities than Zapier. The AI knows this. So when it generates a Make scenario, it might use Make-specific features like the router or the aggregator. For Zapier, it would do something different because Zapier doesn’t have exactly those tools.
This means your workflows aren’t perfectly comparable. They’re solving the same business problem, but they’re taking different technical paths. For ROI analysis, that’s actually valuable because it shows you how each platform would naturally solve it. But if you’re looking for pure apples-to-apples comparison, the generated scenarios won’t be identical.
What works better is using generation as a baseline. You get a starting point from AI, then you manually adjust both scenarios to be mathematically equivalent in terms of operations or execution steps. Then you run the test. That takes some effort, but way less than building from zero.
yes it works. generated workflows are usualy about 70-80% production ready. Speeds up comparison testing significantly. just need review before going live.
We actually use AI Copilot for exactly this reason—converting business descriptions into workflows fast. The key difference I’ve noticed is that when the AI understands your requirements, it can generate production-ready scenarios way faster than manual work. You describe what you need, and you get something you can test immediately.
For Make versus Zapier comparisons, the real power is that you can iterate quickly. Build scenario A, build scenario B, run the numbers, adjust, repeat. In a day, you can explore what would take a week if you were hand-building everything.
The workflows generated aren’t always perfect on the first shot, but they’re complete enough to be testable. And since you can see how the AI approaches the same problem in different contexts, you actually learn something about how each platform works.
If your goal is to make a faster financial decision about which platform fits your workflow patterns, this is genuinely how to do it. You’re not comparing theory—you’re comparing actual execution.