A potential buyer wants us to show how our operations can scale using modern automation, but our legacy databases are fragmented. Should we spend our runway training custom AI models to automate our customer service or will a buyer prefer we leave the database raw so they can integrate it into their own systems?

Question

Tyler Smith · Accepted Answer

Attempting to build complex, custom AI models right before an exit is a common mistake that often results in a high dumb tax. Buyers are skeptical of home-grown technology, and they do not want to pay for raw, unproven algorithms that require specialized talent to maintain.

Instead of building custom models, focus your runway on cleaning your data structure and automating basic completion tasks using standard, commercial APIs. A buyer wants to see clean, organized data pipelines that can easily integrate into their existing enterprise resource planning systems.

Use a strategic pause to audit your current technology stack. Document how your data flows across departments and simplify your customer service workflows. By framing your operations as a series of predictable completion tasks managed by standard software, you show the buyer that your systems are highly transferable and do not rely on key-person technical risk.

A buyer will pay a premium for a clean, structured database and simple automated workflows that yield immediate efficiency gains. Leave the heavy AI model training to the acquiring company; your job during the runway is to deliver a plug-and-play operational foundation that they can easily scale.