AI-intensive acquisition diligence
Assesses whether a target's data rights, AI uses and model controls support its business claims. Material exposure is captured in a deal-risk memo with proposed transaction conditions.
— Product & Tech
Data, AI & technology governance review
Reviews data rights, AI use and model controls against governance maturity, producing a deal-risk memo or readiness summary that clarifies exposure and its implications for the transaction.
- Product & Tech
- Human review built in
- Traceable reasoning
- Runs anywhere
2–3 weeks → ~25 minutes
For a typical multi-document review workflow
No coding required
- Input
- Data-sharing and customer agreements
- Output
- Data rights and contractual restrictions assessment
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~2–3 weeks per run
— USE CASES
What people use Data, AI & technology governance review for
Uncertain data usage rights
Examines data-sharing and customer agreements for rights and restrictions relevant to AI development and deployment. Flags potential unlicensed training data and contractual gaps that could undermine the target's operating model.
Fragmented AI oversight
Reviews documented AI uses, models and governance controls for evidence of unmanaged AI adoption and missing human oversight. Provides a maturity assessment that makes control weaknesses visible to deal teams.
Lower-exposure investment review
Produces a readiness summary where assessed exposure is low, explaining the governance position and any remaining gaps relevant to the transaction.
What it works from
- Data-sharing and customer agreements
- AI use-case and model inventories
- Model documentation and control evidence
- Data, AI and technology governance policies supplied for review
What you get back
- Data rights and contractual restrictions assessment
- AI use-case, model and control coverage review
- Unlicensed training data, unmanaged AI and human oversight risk findings
- Public AI claims and sector-specific regulatory assessment
— HOW IT BEHAVES
How Data, AI & technology governance review produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Consistent classification of decisions
Decisions are sorted into your categories using the same rules each time, which is what makes a large volume of governance and authority documentation readable.
Scored against your criteria
Each decision is assessed against criteria you control and weight, so the same standard applies to every decision in the decision set in scope.
Governance and authority documentation pulled into one schema
Every decision is captured in the same field structure, so records drawn from different documents and sources stay comparable.
Whole sets of governance and authority documentation at once
All of your governance and authority documentation is processed as one set, so patterns across documents surface instead of being read one file at a time.
Live research on the decision set in scope
Current external sources on the decision set in scope are researched during the run rather than recalled from training data, and every source travels with the output.
Branching on what the evidence shows
Thin cases and strong cases among your decisions are handled differently by design, based on what the analysis actually found.
Why this is expensive by hand
Data and AI exposure can materially affect a transaction, yet the evidence is often scattered across contracts, technical documentation and governance policies. AI-intensive acquisition diligence is the typical trigger — assesses whether a target's data rights, AI uses and model controls support its business claims. Material exposure is captured in a deal-risk memo with proposed transaction conditions. Done properly it is defensible; done at pace it becomes a judgement call nobody can retrace. And "properly" usually means 2–3 weeks of manual work.
How this Skill produces it
As a Skill, the work is already sequenced. You bring the evidence, and the run produces data rights and contractual restrictions assessment plus aI use-case, model and control coverage review. The criteria, ordering and review points that make the answer trustworthy are encoded in the Skill itself — which is the difference between a structured method and a prompt someone pastes in. The practical effect: 2–3 weeks of manual work becomes a ~25 minutes run, held to an identical standard on the tenth engagement as on the first.
Who it's for
- Independent consultants codifying their own methodology
- Strategy and transformation teams standardising delivery
- Internal advisory functions under pressure to produce faster
- Operators who need defensible output, not a one-off chat answer
Run it in Skillsize — or export it anywhere
Data, AI & technology governance review exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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