Our leadership team is split on whether we should build custom internal AI software from scratch or just license off the shelf enterprise tools. How do we evaluate this build versus buy decision without creating a technical debt trap that hurts our exit valuation?

Question

Tyler Smith · Accepted Answer

Unless your business is explicitly a software company, building custom AI software from scratch is almost always an expensive distraction that destroys valuation. Sophisticated acquirers do not reward non-software businesses for maintaining proprietary internal software platforms; they view unmaintained code, undocumented architectures, and custom engineering dependencies as massive operational risks.

To make this strategic call, review your Core Focus on the V/TO. Ask your leadership team: does writing proprietary software sit squarely within our purpose, passion, and niche? If the answer is no, you buy.

The most defensible strategy is to license enterprise-grade, commercial off the shelf platforms and apply your proprietary data, prompts, and processes on top of them. Off the shelf software vendors spend hundreds of millions keeping up with model updates, API security, and compliance. When you build from scratch, that ongoing capital expenditure falls entirely on your P and L.

Focus your internal resources on refining your documented Core Processes. When you wrap proprietary operational workflows and unique datasets around third-party infrastructure, you achieve the exact same operational efficiency without the liability of a software development department. This keeps your business lean, avoids developer key-person risk, and ensures a clean, uncomplicated technology audit when you eventually take the company to market for an exit.