Stop automating the wrong tasks — diagnose processes & prioritize AI fixes

Executive Summary

Many mid-market and enterprise teams waste time automating low-value or infeasible tasks because they lack rigorous discovery; operations, automation, and IT leaders across an addressable base of roughly 8,000,000 businesses face fragmentation across documents, logs, and point tools that hides true process bottlenecks. The consequence is frequent failed RPA projects, sunk implementation costs, and delayed ROI that disproportionately hits organizations with limited automation headroom. You could build a SaaS platform that ingests structured and unstructured sources, uses LLMs to infer process maps and decision points, then scores automation candidates by estimated ROI, effort, risk and compliance impact and produces prioritized implementation plans with sample connectors and pilot scripts. Core features would include automated discovery from emails, PDFs and systems-of-record, a transparent scoring model (e.g., estimated hours saved, error reduction, integration complexity), and one-click pilot generation leveraging API-first integrations to prove value in days. A go-to-market route could target a $6K ACV for baseline assessments with tiered offerings for continuous monitoring and enterprise advisory. This market is attractive now because generative-AI-enabled analysis, an explosion of automation vendors, and an API-first SaaS ecosystem materially lower discovery cost and pilot time, supporting a $48.0B TAM (8,000,000 targets x $6K ACV) and the product’s strong market/revenue scores (market score 92/100, revenue potential 90/100). To stand out you should combine auditable, quantitative ROI models and privacy-safe data handling with curated integration templates and a services arm to manage change, while being realistic that long sales cycles, intermittent data access, and organizational resistance are the principal challenges to scaling.