I’ve been tasked with proving ROI for a workflow automation project at our company, and honestly, the spreadsheet approach is killing me. Every time someone asks ‘what if we automate X instead of Y’, I’m rebuilding the whole thing manually.
I read somewhere that some platforms now have AI that can take a plain text description of a workflow and generate something ready to run. The claim is you could describe your automation goal in a sentence or two and get back an actual calculator that models the savings.
But here’s what I’m skeptical about: has anyone actually tried this without ending up back at square one? Like, you describe your workflow, get something back, then realize it’s missing half the logic you needed?
I’m also wondering—if you do get a working calculator out of it, how much of it actually reflects reality versus what the AI guessed about your costs? The last thing I need is board-level decisions based on an AI’s assumptions about my labor costs or error rates.
Has anyone gone from ‘here’s my business problem’ to ‘here’s my ROI calculator’ without a developer or analyst having to rebuild it? What actually stayed and what broke?
I actually ran into this exact problem last year. We needed to model ROI for consolidating three separate data entry processes into one automated workflow.
What worked for us was describing the current state (manual process, time per task, headcount involved) and then what we wanted to automate. The AI generated a workflow that pulled the right variables—processing time, cost per hour, error rates.
The thing is, the first output was maybe 60% there. It had the logic right but missed some edge cases we actually encounter. Like, it didn’t account for our quarterly spikes or the manual QA we still do on 10% of items.
We had to customize it, but customizing something that already exists took us two days instead of the weeks it would’ve taken to build from scratch. The math stayed solid once we plugged in our actual numbers.
I’d say the real value isn’t getting 100% accuracy on the first try. It’s skipping the blank page problem and having something testable to iterate on instead of guessing.
I’ve done this with a few different platforms, and the consistency varies wildly. What matters most is how specific you are when describing the workflow. If you say ‘automate invoice processing’, you’ll get something generic. If you say ‘extract invoice number, vendor name, and amount from PDFs, match against POs, flag discrepancies for review’, you get something much closer to what you actually need.
The ROI calculator part is easier than the workflow itself. Most of these systems do a decent job pulling cost data if you tell them your current processing time and labor rates. Where it breaks is accounting for your business’s weird rules—the stuff that’s specific to how you actually work.
My advice: use the generated calculator as your baseline, then pressure-test it against three to five real invoices or transactions from your process. If it handles those, it’ll probably handle most of your volume. If it fails on even one, you’ve found where you need to customize.
The AI-generated approach works well for straightforward processes but struggles with workflows that have multiple decision points or exception handling. I tested this with an accounts payable workflow and found that the AI correctly identified the main path but missed several approval scenarios we needed.
The ROI calculation itself was solid because it relies on well-established formulas. What made the difference was providing concrete data upfront: current processing time per transaction, labor costs, error rates, and any compliance requirements. The AI used these as constants rather than making assumptions.
If your workflow is relatively linear, this approach can save you significant time. If it’s complex with many branches and exceptions, you’ll spend more time explaining edge cases than you would have building it traditionally. The generated calculator still provides value as a starting framework, but expect to refine it substantially.
Done it. Works if ur process is straightforward. Complex workflows need tweaks. ROI part stays accurate once u plug in real numbers. Estimate 30-40% faster than building manualy.
Start with clarity on inputs and outputs, then let the AI draft it.
I actually did this exact thing with a customer support routing workflow. Described it as ‘incoming tickets get categorized by department, assigned to available agents, escalate if unresolved after 4 hours’—basically a paragraph.
The platform generated a workflow that handled about 80% of our actual logic. We had to add a few custom rules for our specific SLA times and team overlaps, but the core flow was right on.
The ROI calculator connected to our Slack data and calculated hours saved plus accuracy improvements. We went from manual assignment taking three hours daily to fully automated in about a week of setup and tweaking.
The thing that sold me: the generated calculator updated automatically as the workflow ran. So after two weeks, we had actual performance data, not just assumptions. That’s when we showed leadership the ROI wasn’t theoretical—it was happening in real time.
If you’re evaluating tools for this, Latenode handles the AI-to-workflow part cleanly and lets you build these calculators without needing a developer. Worth testing: https://latenode.com