AI Capability Assessment
AI capabilities are increasingly driving deal premiums. Before paying for AI, it is worth knowing what is actually there — a CAIO-level assessment that looks past the demo and into the architecture.
Most "AI" in acquisition targets is thinner than it looks
AI capabilities are now routinely cited in deal narratives and used to justify valuation premiums. Many of these capabilities are real; many are not. The assessment problem: AI is easy to demo and hard to audit.
A few API calls to a commercial LLM, a marketing narrative, and a dashboard can look identical from the outside to a genuine AI capability that took months to architect and is genuinely defensible. Distinguishing the two requires someone who has built the real version.
Having deployed a private LLM in production for an oil development company and designed RAG architecture for a PE-exit platform, I know what a real implementation looks like — and what a thin wrapper looks like when someone is trying to make it appear equivalent.
The architecture questions that reveal AI maturity
A credible AI capability assessment examines the dimensions that actually determine whether AI creates durable value:
- Data infrastructure — Is there a proprietary data asset, or does the AI run on the same public data that any competitor could access?
- Model architecture — Is it a fine-tuned or custom model, or a commercial API call with a sophisticated prompt?
- RAG implementation quality — If it is RAG, what is the chunking strategy, embedding model, retrieval quality, and hallucination mitigation design?
- Training data and versioning — Is the training process documented, reproducible, and owned by the company?
- Inference infrastructure — Is the AI running in production at scale, or is it demo-stage?
- AI governance — Are there evaluation frameworks, monitoring systems, and documented model behavior in production?
Six areas of AI capability evaluation
Data Asset Evaluation
Is the AI powered by proprietary data that creates a moat, or public data available to any competitor?
Model Architecture Review
Fine-tuned models vs. API wrappers — assessing what has actually been built and who owns it.
RAG System Assessment
For retrieval-augmented systems: evaluating chunking, embedding quality, retrieval accuracy, and context management.
Production Readiness
The gap between demo performance and production performance at scale — testing the difference before it becomes the acquirer's problem.
AI Governance Audit
Evaluation frameworks, monitoring infrastructure, bias assessment, and model behavior documentation in production.
Competitive Moat Analysis
Does the AI capability create durable advantage, or is it replicable in 6 months with a standard API key?
"Shawn helps clients translate AI potential into practical strategy. He's one of the few people who can make models and algorithms feel like natural business tools."
What an AI capability assessment covers and how long it takes
A standard AI capability assessment runs one to three weeks, depending on system complexity and access. The timeline is shaped by the deal schedule — accelerated reviews are available when deal timelines require it.
- Access requirements — Code repositories, model artifacts, data pipeline documentation, infrastructure configuration, and interviews with the engineering team responsible for the AI system.
- Technical review — Hands-on examination of the AI architecture: model selection rationale, data preparation process, inference infrastructure, evaluation methodology, and production monitoring.
- Findings report — Written assessment documenting confirmed vs. claimed capabilities, defensibility analysis, replication risk (how long would it take a well-funded competitor to reproduce this?), and integration implications for the acquirer.
- Briefing — Executive-level presentation of findings with full technical backup, structured for both the deal team and the technical leadership that will own the system post-close.
If the AI story of an acquisition target has not been independently verified by someone who has built comparable systems, it has not been verified at all. Reach out to discuss an assessment.
Ready to assess an acquisition target's AI capabilities?
Independent AI capability assessment from a CAIO with production deployment experience — evaluating what is actually there before it affects the deal.
