Translate the AI story into testable claims
AI can matter to revenue, margin, defensibility, customer experience, or operating leverage. Diligence starts by separating those claims and naming the evidence each requires.
Value mechanism
Identify exactly where AI changes the product or economics: an automated workflow, a ranked decision, a generated artifact, or a differentiated data loop. Avoid treating every model-backed feature as one asset.
Benchmark provenance
Inspect what was evaluated, on which data, against which baseline, under which version, and with which acceptance rule. A headline score without representative conditions cannot support the thesis.
Operating evidence
Reconcile offline performance with production usage, human override, failure handling, monitoring, and customer impact. Document where only management assertion is available.
Verify models, data, and provider dependence
The asset may sit partly outside the target—in licensed data, hosted models, cloud APIs, and people who know how the system actually behaves.
Model and provider map
Record foundation models, fine-tunes, embeddings, routing, fallback paths, versions, account boundaries, rate limits, contractual dependencies, and the impact of a provider change.
Data rights and lineage
Trace training, retrieval, evaluation, feedback, and customer data to their source, permitted use, retention, and transformation. Separate owned data advantage from access that can be withdrawn.
Inference economics
Rebuild unit economics around real context length, retries, tools, caching, review, failure remediation, and provider price sensitivity—not a single list-price token estimate.
Diligence the AI system—not only the model
Many operating failures emerge from orchestration, permissions, retrieval, workflow design, and human handoffs rather than model capability in isolation.
Reproducibility and change control
Check whether the team can recreate evaluations, pin model and prompt versions, explain changes, and connect a release to the evidence used to approve it.
Security and abuse paths
Map sensitive tools and data, prompt and retrieval boundaries, identity and authorization, logging, abuse monitoring, and the blast radius of an incorrect or manipulated output.
Human authority
Name who may approve, override, escalate, contain, and roll back consequential behavior. A policy without an operator and exercised path is not an operating control.
Carry the result into the transaction
The conclusion should distinguish a damaged thesis from a solvable operating gap and show what must happen next.
Thesis status
State which claims are supported, conditional, unsupported, or unavailable and how each state affects valuation, integration, or the roadmap.
Concentration and portability
Expose reliance on one provider, dataset, employee, integration partner, or undocumented evaluation process before it becomes a post-close surprise.
Post-close evidence plan
Assign the tests, contracts, ownership, monitoring, and specialist reviews that could not be completed in the transaction window.