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Executive Summary

Many organizations are automating first and mapping later, which repeatedly causes failed automations, hidden process debt, and wasted spend for both IT and business teams. This problem hits enterprises and mid-market companies most acutely—process owners, RPA teams, and CIOs—across an addressable base of roughly 50 million businesses spending about $1,600 per year on automation and orchestration tools, a roughly $80.0B market. A practical product is a SaaS platform that "maps before you automate": it ingests screen recordings, system logs and observability traces, uses AI to generate canonical process maps and decision models, and provides simulation, ROI estimation and ready-made connectors to RPA/orchestration platforms for safe deployment. Key design choices would be human-in-the-loop editing, detailed audit trails for compliance, prescriptive remediation guidance (e.g., where to simplify or add controls) and a lightweight implementation service to jump-start adoption. Timing is favorable because AI-enabled process discovery materially lowers the cost of extracting workflows from video and logs, observability-first operations are making the necessary telemetry available, and many organizations are re-evaluating toolchains amid RPA fatigue and high failure rates. Market metrics support this: we rate the opportunity 88/100 on market attractiveness with revenue potential at 92/100, reflecting a large addressable spend, consolidating vendor interest and a clear ROI story when automation is grounded in accurate process maps. To stand out you need deep integration with observability and logging layers to deliver high-fidelity maps, strong simulation/verification that reduces false starts, and partner channels into existing RPA vendors so the tool becomes a gating control rather than another point product. Challenges include medium competitive intensity, the need to prove ROI in long enterprise sales cycles, and upfront integration and security requirements, but if you can demonstrate a measurable reduction in failed automations and time-to-value within the first 90–180 days this product can capture a significant slice of the $80B spend.

Executive Summary

Map processes first — prevent automating broken manual workflows

Executive Summary

Map processes first — prevent automating broken manual workflows

Executive Summary

Market Validation

Market Opportunity

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Small-business grant discovery — centralized database + automated alerts

Streamline dealership ops — centralize inventory, leads, docs & payments

Early-traction trap: convert one-off signups into repeat video-contract users

Manual quoting & installs — AI visual quoting + field workflow automation

Outdated workflows: autonomous AI agents orchestrating ops & data (pain → AI OS)

SMB operations slowed by custom builds — no-code AI ERP to automate workflows