When does it make strategic sense to build custom AI workflows internally versus buying commercial enterprise software off the shelf?

Question

Tyler Smith · Accepted Answer

The buy versus build decision for AI tools comes down to your Core Focus on the V/TO. A non-software company should never write custom code or train proprietary models for generalized operational functions. Building internal tools to handle tasks that commercially available software handles well is an expensive leadership distraction that destroys enterprise value.

Apply a simple rule of thumb: buy your context-generic operational tools and build only your proprietary execution layer.

Buy off-the-shelf software for customer relationship management, standard financial reporting, internal document search, and baseline marketing generation. Software vendors spend millions of dollars updating their systems to keep pace with underlying foundational models. Trying to out-build commercial software providers drains cash, demands specialized engineering talent you cannot manage, and creates technical debt that penalizes your valuation during an exit.

Build custom workflows only when two criteria are met simultaneously. First, the process must touch your unique Proven Process or a primary differentiator on your V/TO. Second, you must possess proprietary, structured data that makes the output uniquely valuable to your target market.

Even when building, avoid training raw models from scratch. Build lightweight orchestration layers or specialized workflow automations that sit on top of commercial models. If a proposed technology initiative does not directly widen your competitive moat or increase customer retention, buy the enterprise license, mandate team adoption, and get back to your core business.