process-mapper

Process mapping systematically documents workflows to expose undocumented SOPs and reveal where automation can deliver high ROI. It captures actual decision points and context dependencies through structured interviews, then classifies each step on a tractability grid to identify which tasks are viable for RPA or AI automation.

Tacit knowledge is the undocumented, unwritten expertise people apply daily—decision rules, workarounds, context clues. Capturing it via labeled decision points and standardized documentation patterns prevents automation efforts from targeting the wrong processes and ensures you automate what actually happens, not what documentation claims.

Process discovery uses a 5-round interview sequence to extract real workflows from domain experts. Each round captures workflow steps, decision criteria, exceptions, and context dependencies that shadow processes hide. This reveals bottlenecks and manual work that documentation misses.

The tractability grid classifies process steps into nine zones based on complexity and repeatability. High-ROI candidates—rules-based, high-volume, low-exception tasks—suit RPA or AI copilots. Low-ROI steps involve judgment calls, rare exceptions, or customer-specific logic where automation effort outweighs benefit.

Yes. Process mapping produces standardized documentation artifacts—process maps, written SOPs, and ROI analyses. These outputs document the workflows you've captured, establish a baseline for measurement, and quantify automation's expected efficiency gains and cost reduction across value streams and teams.

Avoid automation when steps require frequent human judgment, involve exception handling that changes by customer or context, or have low volume and high complexity. Process mapping identifies these constraints upfront, preventing wasted effort on untractable problems that lack the repeatability and clarity automation needs.