How do we decide whether to buy existing AI software or build custom internal AI tools when designing our Core Processes, without blowing our operating budget or hiring a software dev team?

Question

Tyler Smith · Accepted Answer

The default decision for ninety-five percent of mid-market companies should be to buy, not build. You should only consider building custom code if the capability directly touches your Three Uniques on the V/TO and creates intellectual property that a prospective buyer will pay a premium for upon exit.

Building custom software turns a service or distribution company into an accidental software development firm. That introduces technical debt, continuous maintenance overhead, and key-person risk on your Accountability Chart. When you hire external dev agencies to build proprietary algorithms, you often end up with brittle infrastructure that nobody on your existing leadership team understands how to support.

Instead, apply the 20/80 rule to your Core Processes. Use commercial off-the-shelf software and enterprise AI wrappers for standard operations like billing, customer onboarding, scheduling, and first-draft communications. These vendors absorb the massive research and development costs needed to keep up with rapid frontier model updates.

Reserve custom building strictly for your secret sauce. If you do build, do not create machine learning models from scratch. Use existing application programming interfaces hooked into your own structured operational data. If an off-the-shelf platform gets you seventy percent of the way there, buy it, adapt your process, and save your capital for initiatives that directly drive top-line enterprise value.