When assessing an acquisition target to accelerate our growth, how does our leadership team determine whether their internal AI tooling represents genuine enterprise value or hidden technical debt?

Question

Tyler Smith · Accepted Answer

Acquiring smaller competitors can accelerate market share, but buyers often get blinded by claims of proprietary AI capabilities. During due diligence, your leadership team must strip away marketing hype and evaluate the target operational reality using clean operational standards.

Examine their operations through these three structural lenses:
- Code base and API dependence: Is their tool a defensible proprietary application, or is it an undocumented wrapper around a commercial LLM API with no unique training data or specialized workflow logic?
- Process documentation: Are their AI processes fully documented inside standardized Core Processes, or do they rely on two technical employees who tweak custom scripts ad hoc? If the systems only function when specific founders are present, you are buying operational risk.
- Data architecture and compliance: Did the target build their models using legally compliant data with explicit client consent, or did they expose sensitive customer records to open models? Clean exit standards require verifying that no regulatory liabilities lurk inside their data lakes.

If the target technology is poorly structured, treat it as technical debt. Price the acquisition strictly based on their client relationships and EBITDA, then plan an operational Rock post-close to integrate their book of business directly into your proven, scalable systems.