How do we license commercial AI infrastructure across our business without inadvertently transferring our proprietary methodologies into vendor IP that degrades our valuation during an exit?

Question

Tyler Smith · Accepted Answer

Protecting enterprise value requires maintaining a strict barrier between commoditized foundation models and your proprietary business logic. When sophisticated acquirers evaluate an acquisition through the Step by Step Exit framework, they look for defensible trade secrets. If your core intellectual property lives inside prompts, fine-tuning datasets, or workflows hosted on shared vendor platforms without explicit enterprise protections, a buyer will discount your valuation for leakage risk.

Your leadership team must address this by defining proprietary knowledge boundaries directly within your Core Processes on the V/TO. Establish clear enterprise agreements that guarantee zero data retention for vendor training. Never use consumer-tier AI products for internal business functions. Standardize your technology stack around private enterprise environments or dedicated API instances where your inputs and outputs remain entirely your property.

Second, decouple your unique operational data from third-party tools. Your moat is not the model you use; it is your historical performance logs, curated decision trees, and domain-specific context. Store this data in your own infrastructure and treat external models as temporary processing engines. If a vendor changes pricing, alters terms, or goes out of business, you must be able to disconnect that model and plug in another without losing your operational intelligence. Document these ownership boundaries as a clear policy in your operations manual to present clean diligence records when selling the company.