Undiscovered assumptions stall automation — audit processes before build (50–100 chars)

Executive Summary

Undiscovered assumptions routinely stall automation projects: RPA and automation Centers of Excellence in enterprise and midmarket firms waste months and budget because business rules are scattered across tickets, chat, SOPs and human behavior that aren’t validated before build. The typical buyer set — about 500,000 enterprise and midmarket accounts worldwide — already spends roughly $60K annually on process discovery and validation, which underpins a $30.0B addressable market. You could build a pre-automation audit platform that combines process mining, desktop and SaaS telemetry, and LLM-powered extraction from unstructured sources to flag implicit assumptions, produce test cases, score automation risk, and generate remediation playbooks and governance artifacts. The timing is favorable: RPA adoption is rising, observability telemetry is increasing, and advances in LLMs make practical the automated extraction of rules and intent; independent market diligence scores this opportunity 92/100 with revenue potential 88/100. This product can stand out by optimizing for assumption discovery and closed-loop validation rather than just drawing process maps — provide explainable, high-precision findings, turnkey integrations with leading RPA tools, and measurable KPIs (e.g., reduce rework on automation builds in pilots). Strengths are clear demand and an attractive $60K ACV motion, but challenges are real: medium competition from process-mining vendors and consultancies, enterprise data access and compliance hurdles, and the need to prove model accuracy and ROI in live deployments. I’d recommend running 5–10 paid pilots to validate a conservative 30–50% reduction in failed automations before scaling.