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Market scale: a huge surface, uneven adoption

Agentic AI ultimately competes for the same recurring enterprise software and SaaS wallet that already shows up as trillion-class forecasts in IT data. The friction is adoption: businesses try tools quickly but move to scaled, trusted production slowly.

  • ~$1.25TGartner’s January 2025 IT spending forecast puts worldwide enterprise software at about $1.25 trillion in 2025, up from about $1.09 trillion in 2024 (mid-teens percent year over year in that table). That is the same budget pool business applications and SaaS monetize today, a practical order-of-magnitude proxy for how large the agent opportunity can get if products earn production trust.Gartner press release (IT spending forecast, Jan 2025)
  • 88%McKinsey 2025 global survey: share of respondents who report regular use of AI in at least one business function, versus 78% one year earlier. Breadth of experimentation is no longer the headline problem; depth is.McKinsey, The State of AI: Global Survey 2025 (public article)
  • ~⅔Same McKinsey wave: share of respondents who say their businesses have not yet begun scaling AI across the enterprise (key finding in the public article). Pilot volume is high, business scaling is not.McKinsey, The State of AI: Global Survey 2025 (public article)
  • 62% / 23%Share at least experimenting with AI agents / share scaling an agentic AI system in at least one business function (McKinsey also reports an additional 39% experimenting in the article body). Agent narratives run ahead of controlled rollout.McKinsey, The State of AI: Global Survey 2025 (public article)

Adoption vs. scaled deployment (overlay)

Tooling and gen AI show up in workflows faster than organizations move past pilots into scaled deployment. The gap is one lens on why buyer trust and operational proof still lag headlines.

Vertical axis: % of respondents (same scale for both lines).

Line chart 2023 to 2025. Generative AI use rises from 33 to 79 percent. Scaled beyond pilots rises from 24 to 38 percent.025507510020232024202533%65%79%24%32%38%
Generative AI use (≥1 business function, reported)
Scaled beyond pilots (reported)

Trend points are commonly summarized alongside McKinsey’s multi-year global AI surveys; confirm values in The State of AI exhibits before reusing in external materials.

Trust. McKinsey’s 2025 public write-up also notes that nearly one-third of respondents report negative consequences linked to AI inaccuracy, with inaccuracy among the most-mitigated risks.

Illustrative visualization; not Signal’s own survey data.

Who else cares (ecosystem map)

Frontier labs & startups building evals

Research · eval harnesses · rubrics

Teams shipping evaluation infrastructure need rubrics that match how buyers actually decide, not only synthetic leaderboards. Signal collects real-world evaluation criteria from live workflows and procurement so you can ground harnesses and benchmarks in operational truth. Similar in spirit to expert-led eval marketplaces (e.g. Mercor-style mission rubrics): structured tasks, clear pass or fail signals, and outcomes buyers can reuse.

  • Buyer-grounded rubrics from the field, not toy-task scores alone
  • Sharper alignment between harness design and pilot scorecards
  • Less rework when eval claims meet real procurement

Insurance & underwriting tech

Carriers · MGAs · insurtech

Teams evaluating agents for submission intake, triage, claims documentation, and risk signals need repeatability and audit posture, not slide narratives.

  • Workflow evidence over demo scripts
  • Regulatory and partner scrutiny on automation

PE & rollups

Operating partners · portfolio ops

Buy-side and operators look for agents that compress cost-to-serve, standardize processes across portfolio companies, and show up in margin, not only in slide decks.

  • Margin and throughput, not “AI strategy” slides
  • Cross-portfolio pattern recognition

Venture & growth equity

Pre-investment diligence

Funds underwriting agent startups need a fast read on whether “production” is real: routing, failure modes, change control, not a polished sandbox.

  • Technical + operational truth checks
  • Shorten path to conviction or pass

Where Signal fits in a sea of Noise

Revenue model: two engines, one data moat

TAM~$1.25T
SAM~$225M
SOM~$96M

Engine 1

Agent Team SaaS (today)

  • Signal monetizes agent teams directly through a recurring SaaS subscription.
  • Teams pay to run evals against verified buyer workflows, list in the marketplace, and share trace evidence buyers can review.
  • This engine drives near-term recurring revenue while expanding the supply side feeding the broader platform.
  • Buyer-grounded rubric licensing (API, catalog, refresh) for other eval builders is folded into the same Engine 1 TAM/SAM/SOM view as an illustrative layer.

Who's building Signal

The founders worked together at Amazon and AngelList, where they built AI applications for fund administration. They left to start their own venture-backed startups. They kept seeing the same problem: strong claims with too little workflow-grounded proof for buyers and capital, and they came back together to build Signal.

This page is not an offer of securities. For investment discussions, contact Signal founders.

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