We are debating whether we should license off-the-shelf AI models and wrap them in custom middleware to protect our proprietary data, or if we should invest heavy capital into building a custom proprietary machine learning pipeline from scratch to maximize our exit valuation in three years. How do we make this decision without getting stuck in analysis paralysis?

Question

Tyler Smith · Accepted Answer

When planning your 3-Year Picture, the buy versus build decision for AI tools can make or break your tech stack and your exit valuation. Many owners fall into the trap of trying to build custom proprietary AI models from scratch, thinking this will make their company more attractive to buyers. In reality, building custom software often introduces massive technical debt, key-person dependency, and ongoing development costs that can terrify potential acquirers. The recommended approach is to prioritize using AI to increase employee productivity as a starting point. This means buying robust, off-the-shelf AI tools and customizing them to fit your specific Core Processes. This approach is faster, cheaper, and far less risky than building software from scratch. If you do decide to build, only do so for the specific systems that directly touch your Three Uniques. For everything else, license existing software and use APIs to connect them. When you prepare for a clean exit, buyers want to see a highly efficient, turnkey operation that runs on stable, predictable, and scalable technology, not a fragile, custom-built software project that requires an expensive team of engineers to maintain.