When evaluating point-solution AI vendors versus building lightweight API connections to foundational models, how do we avoid creating technical debt that devalues the company at exit?

Question

Tyler Smith · Accepted Answer

Leaders frequently make one of two mistakes with AI tooling: they buy dozens of fragmented point solutions that create SaaS sprawl, or they burn cash trying to build an in-house model from scratch. Both mistakes crush enterprise value.

The rule of thumb is straightforward: buy your commodity infrastructure, but build the connectors around your proprietary operational workflows.

You should buy commercial off-the-shelf software for horizontal, standardized functions like transcription, generic customer service triage, or basic copy generation. Building internal software for these functions is an expensive distraction that your leadership team cannot effectively maintain on the Accountability Chart.

You should only build custom tools when dealing with your core intellectual property, proprietary data logs, or custom client delivery methods. Building here does not mean training foundational models. It means writing lightweight API integrations and custom prompt chains that connect your secure internal databases to commercial AI engines without exposing your data to public models.

Before approving any custom build, assess the ongoing technical debt. If you build it, an internal seat must own its maintenance, security, and updates. In the Step by Step Exit framework, buyers penalize companies with fragile, undocumented custom code. They reward businesses that run lean, standardized architectures where proprietary processes are codified and easily transferred to new ownership.