I’m managing a large-scale migration from our proprietary BPM system to an open-source alternative, and the documentation translation piece is becoming a major bottleneck. We have hundreds of existing process documents that need to be converted into executable workflows in the new system.
I’ve been hearing about AI copilots that can supposedly take natural language descriptions and generate ready-to-run workflow templates. I’m skeptical but intrigued by the potential time savings.
Has anyone used this approach in a real-world migration project? I’m particularly interested in:
How accurate were the generated workflows compared to what you would have built manually?
What kind of documentation quality/format worked best as input?
How much manual refinement was typically needed after generation?
Time is a major factor for us - our current approach of having analysts manually rebuild each process is taking far too long. But I’m concerned about accuracy and whether this might actually create more work in the long run if the generated workflows have issues.
We faced this exact challenge migrating 200+ customer service workflows last year. Our documentation was a mix of flowcharts, Word docs, and tribal knowledge - converting it manually was taking 3-4 days per workflow.
We tried Latenode’s AI Copilot approach and it was game-changing. We’d input the documentation (sometimes just screenshots of our legacy system), describe the desired outcome in plain English, and the system would generate a complete workflow in minutes.
Accuracy was surprising - about 80% correct on the first attempt, which was far better than I expected. The remaining 20% needed tweaking, but even then it was just adjustments rather than rebuilding from scratch.
The best inputs were step-by-step procedures that included decision points. Even bullet points worked well. The system struggled more with highly technical integrations or vague requirements.
The biggest time-saver was the iterative approach - we could tell the AI what was wrong and it would refine the workflow rather than starting over. Our migration time dropped from days to hours per process.
One tip: start with your simplest processes to get a feel for it before tackling the complex ones.
We used this approach when migrating our insurance claims processing system last year. We had over 150 documented workflows that needed to be rebuilt in a new system.
Accuracy varied dramatically based on the quality of our input documentation. For well-structured processes with clear decision points, the AI generated workflows that were about 70-80% correct on the first try. For poorly documented processes with lots of tribal knowledge, it was closer to 40-50%.
What worked best as input was a combination of:
Structured flow diagrams (we converted Visio diagrams to text descriptions)
Clear if/then decision logic spelled out explicitly
Examples of inputs and expected outputs
Even with the 70-80% accuracy cases, we still needed human review and refinement, particularly for exception handling and edge cases. However, it was much faster to refine an existing workflow than build one from scratch.
Overall, we reduced our migration time by about 60%, which was significant. Just make sure you have good QA processes in place to verify the converted workflows before putting them into production.
I’ve implemented natural language to workflow conversion for three enterprise BPM migrations, and the technology has improved dramatically in the past 18 months.
Accuracy now typically ranges from 65-85% depending on several factors:
Documentation quality - structured documentation with clear decision trees performs best
Process complexity - linear workflows with clear rules convert more accurately than those with complex branching logic
The most effective approach I’ve found is to establish a standardized documentation format specifically optimized for AI conversion. This includes explicit sections for triggers, inputs, outputs, business rules, and exception handling.
Regarding efficiency, we’ve documented an average 70% reduction in conversion time across hundreds of workflows. However, the efficiency gain isn’t uniform - simple, well-documented processes might see 90% time savings, while complex processes with poor documentation might only see 30-40%.
Quality assurance remains essential - we still validate all AI-generated workflows through testing and subject matter expert review.
we converted 50+ sales workflows using AI last quarter. works pretty good but needs clean input docs. got about 70% accuracy on first try, then needed tweaking. saved tons of time vs manual rebuild. best for standard processes not complex integrations.