Executive Summary

Many knowledge workers and their IT teams are drowning in point-AI proliferation: organizations of 200 million knowledge workers are trialing and buying dozens of horizontal and vertical AI tools, creating duplication, switching costs, data sprawl, and unpredictable spend that procurement and security teams struggle to control. The problem is most acute for mid-size and enterprise teams where governance, cost attribution, and risk management fall to a small set of admins while individual contributors keep adding niche tools to get immediate value. This creates a clear pain point for CIOs, IT security, and product/ops leaders who need to standardize usage without throttling productivity. You could build a lightweight orchestration/control-plane that actively limits and routes each user or team to a “peak 3” effective stack: an admin console to set policies and budgets, an identity-aware routing layer with SSO and connector templates, a desktop/browser plugin for client-side routing and usage caps, and reporting that attributes cost and outcomes back to teams. The minimal viable product is integrator-first: 10–20 prebuilt connectors to top AI tools and cloud models, policy templates for cost and data residency, plus behavioral nudges that intentionally simplify choice to three primary tools per workflow. Technical challenges include latency of chained calls, maintaining connectors to rapidly changing APIs, and earning enterprise security certifications. This is an attractive market now because the TAM sits at about $48.0B (200M knowledge workers × $240 ARPU/year), enterprise AI adoption is accelerating, and robust public APIs and SSO reduce integration friction—factors reflected in a market score of 95/100 and revenue potential of 88/100. To stand out you would need to be ruthlessly productized around “peak 3” simplicity, deliver measurable ROI (cost savings and reduced task-switching), and move faster than incumbents by offering open, auditable policy controls rather than trying to be another point tool. The real risks are platform lock-in from large vendors, sales motion complexity into IT and procurement, and the engineering burden of keeping connectors and security posture up to enterprise standards, but a focused value proposition and clear metrics could make this a viable, defensible business.

Market Opportunity

Reduce AI-tool overload — orchestrate & limit stacks to peak 3 targets a $48.0B = 200M knowledge workers x $240 ARPU/year (tool orchestration & productivity software) total addressable market with medium saturation and a year-over-year growth rate of 12-18% (knowledge-work productivity & automation markets).

Key trends driving demand: Proliferation-of-point-AI -- Rapid increase in horizontal & vertical AI point tools forces orchestration demand and creates switching inefficiencies.; Enterprise-AI-adoption -- Large organizations are standardizing AI use and need governance/orchestration to control cost and risk.; Integration-economy -- Robust public APIs and SSO providers lower friction for a central orchestration layer to plug into existing stacks.; Behavioral-research-backed-guidelines -- Studies showing optimal tool counts create a prescriptive product narrative and adoption lever..

Key competitors include Zapier, Workato, Make (formerly Integromat), Microsoft 365 Copilot (and Google Workspace AI), Workspace & Tab managers (Notion, Asana, Workona/Toby) — user workarounds.

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Reduce AI-tool overload — orchestrate & limit stacks to peak 3

Executive Summary

Market Validation

Market Opportunity

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