Enterprise Reconstruction · Module 9
AI Readiness asks whether this enterprise can adopt AI. This asks a colder question: how much of what you are paying for can a general-purpose model already do — or will be able to do within the hold period? That number does not show up in the P&L you were handed. It shows up eighteen months after close, in the multiple you can no longer get.
Move each parameter to reflect the target as it actually operates today — not as its pitch deck describes it.
Model dependency — How much of the core product is a wrapper?
If the value delivered to the customer is mostly a general-purpose model with a thin layer on top, the moat is thinner than the multiple assumes.
Data moat — What can't a model get anywhere else?
Proprietary, hard-to-replicate data and validated expertise are the actual defensibility. Public or easily scraped data isn't.
Agentic substitution risk — Can a workflow, not just a task, be replaced?
The newer risk isn't a model answering one question. It's an agent chaining several steps end-to-end and removing the product entirely, not just one feature of it.
AI talent concentration — Who actually knows how to keep the moat current?
A data moat with no one left to maintain it degrades quickly. This mirrors the Knowledge Concentration Index in Reconstruction Cost — the same fragility, aimed at AI-specific expertise.
Time horizon — How long before the exposure becomes the story?
Every exposure has a clock. PwC's 2026 guidance to dealmakers is to price AI's impact over a three-to-five year window, not the trailing twelve months on the CIM.
AI Disruption Exposure Score
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Time-adjusted urgency
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Model dependency
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Data moat (inverted)
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Agentic substitution risk
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Talent concentration
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Precedent · Not a hypothetical
In 2023, Thomson Reuters paid $650 million in cash for Casetext — a 104-person legal AI company that, by the acquirer's own analysts' account, was not expected to move the revenue needle in the near term. The deal was projected to be margin-dilutive through the following year. Thomson Reuters bought it anyway.
The reason maps directly onto this tool. Thomson Reuters' core franchise — Westlaw, legal research — was itself exposed to model-dependency risk: a general-purpose model could increasingly do what a paid legal research seat did. Casetext's product, CoCounsel, had early access to GPT-4 and had already proven the workflow could be automated end-to-end. Rather than let a competitor or a startup own that substitution, Thomson Reuters bought the exposure and put it inside its own moat — its proprietary, expert-validated content and its existing customer base of over 10,000 firms.
The lesson is not "buy AI companies." It's that the exposure was priced and acted on before it showed up in a quarter. Run this enterprise's numbers through the sliders above and ask the harder question: if you're the seller, would you rather be Casetext being bought ahead of the disruption — or the target nobody bought in time?
Sources: Thomson Reuters press materials and SEC filings, 2023; TechCrunch, June 2023; CIBC Research deal note, June 2023; MLQ.ai, February 2026.