I’ve been experimenting with something that sounds too good to be true: describing our workflows in plain English and having the platform actually generate ready-to-run automation from that description. The pitch is that you skip a bunch of implementation time and can move faster from concept to production.
But here’s where I’m skeptical. In my experience, when someone hands you a process description—even a detailed one—there are always gaps between what’s written and what actually needs to happen. Context gets lost. Edge cases don’t make it into the description. Requirements change between the time you write it down and the time you need to deploy it.
So I’m trying to understand: when you feed a plain text process description into something like an AI copilot that generates workflows, how much of what comes out is actually production-ready? And how much of that “time saved” just gets pushed downstream into validation, testing, and rebuilding the parts that didn’t quite work?
I ran a test with one of our simpler processes—basically a lead qualification workflow. The generated workflow was maybe 70% there. We had to add custom logic for some of our specific business rules, rework the validation steps, and integrate it with our existing data pipeline. So we saved some time on scaffolding, but the actual complexity didn’t disappear—it just shifted.
Maybe the time savings are real if you’re starting from scratch and don’t have complex business rules. But I’m trying to figure out if this actually accelerates our migration from Camunda to an open-source BPM. When you’re migrating, you’re not starting from scratch—you already have documented processes. So the question becomes: is a plain-text-to-workflow tool actually faster than a proper migration strategy?
Has anyone actually deployed something that was generated from a plain text description and found it worked without significant rebuilding?
You’re right to be skeptical. I’ve been through this a few times, and the honest answer is that generated workflows are a starting point, not a finished product.
We tried this with a moderately complex process—customer onboarding. The AI-generated workflow got the happy path maybe 85% correct. But all of our validation logic, the places where we had to call external systems asynchronously, and the error handling—that needed rebuilding.
Where it actually saved time was on the boilerplate. We didn’t have to manually construct the basic flow structure or wire up the initial integrations. That’s genuinely useful. But the 20% of complexity that’s usually where 80% of the time goes—error handling, edge cases, custom business logic—that still requires engineering effort.
The real advantage we found was earlier validation. When you generate a workflow from plain text, you can show it to business stakeholders immediately. They can say “wait, that’s not how this process actually works” before you’ve spent a week building it manually. That feedback loop is valuable.
For a migration scenario, I’d use it differently. Don’t expect the generated workflow to be production-ready. Use it to validate your process documentation is complete. Use it to create a starting point that stakeholders can review and fix. Then treat the engineering work as actual engineering, not something the tool already handled.
The key is understanding what “ready-to-run” actually means. The workflow runs, sure. But production-ready is different.
We generated a workflow for a data processing task. The platform got the main steps correct. But we needed to add our own error handling, implement retry logic for API calls that sometimes fail, and optimize for our specific data volume. The generated version would have worked for light testing but would have failed under real load.
I’d estimate we saved maybe 30% of the implementation time. The platform eliminated writing boilerplate and basic structure. The 70% was still hands-on engineering—integrations, testing, optimization, production hardening.
Honestly, it depends on how complex your workflows are. For simple stuff, the generated workflows are usable with maybe 10-15% customization. For anything with real business logic or integration complexity, you’re looking at 40-60% rebuilding.
What changed for us was the ability to rapidly prototype and validate. We could generate a workflow, share it with stakeholders, get feedback, regenerate, and iterate. That cycle is much faster than manual design. But by the time something ships to production, it’s been heavily customized.
From my experience, the generated workflows are useful as communication tools and starting points, not as finished products. When we fed our process descriptions to an AI copilot, the output was about 60% accurate to what we actually needed. The rest required customization for our specific integrations, error handling, and business rules.
The time savings were real but modest—maybe 25-30% of total implementation time. The platform eliminated some boilerplate work and helped us think through the basic flow. But production hardening, testing, and integration still required substantial engineering.
For a migration context, generated workflows have a specific value: they help you validate that your current process documentation is accurate. If the generated workflow doesn’t match your actual process, it means your documentation has gaps. That’s useful information. But treating the generated workflow as production-ready would be a mistake.
The honest assessment from my work: AI-generated workflows save time on scaffolding and structure, not on complexity. When we generated workflows from plain text descriptions, the simple process automation came out mostly correct. But the moment you had conditional logic, error handling, or integration with external systems, you needed significant rework.
I’d estimate about 40% of implementation time was eliminated. The other 60% was still engineering. For a migration, that means you can use the generated workflow as validation that your process is documented correctly, but you can’t skip the detailed implementation work.
The test we ran on a customer data pipeline showed the generated workflow was structurally correct but lacked the robustness for production. Error handling was minimal, retry logic was basic, and performance optimization wasn’t there. We’ve learned to treat generated workflows as a design artifact first and a code artifact second. They’re good for getting stakeholder agreement on process flow, less good as finished products.
Generate the workflow, get stakeholder validation that it matches your process, then plan for significant engineering effort to make it production-hardened. That’s the realistic workflow. It’s not that the tool is bad—it’s that “ready-to-run” is marketing language, not engineering reality.
I tested this with one of our core workflows, and honestly, the results were better than I expected but not in the way everyone thinks they will be.
We generated a workflow for a multi-step approval process from a plain text description. The AI copilot created a workflow that captured about 75% of our process correctly. The remaining 25% needed customization for our specific business rules, integrations with our existing systems, and error handling.
But here’s what actually saved us time: we didn’t have to manually scaffold the entire flow or manually wire the initial integrations. The generated workflow gave us a head start that eliminated maybe 25-30% of the implementation work.
More importantly, we were able to show the generated workflow to stakeholders immediately for validation. They could see the process flow visually and say “wait, this step should happen before that step” or “we need to add a branch here for exceptions.” That feedback happened days earlier than it would have in a traditional implementation, which meant fewer expensive rework cycles later.
For migrating workflows, the AI copilot approach is valuable differently. It helps you validate that your current processes are properly documented. If the generated workflow doesn’t match reality, fix your documentation first. Then let your engineering team use the validated process as input for proper implementation.
Think of it as a rapid design and validation phase, not as code generation.