Build vs. Buy: AI Agents in Supply Chain
Every enterprise technology leader faces the build-vs-buy question for supply chain AI agents. The prototype is seductive: a working agent in a week using LangChain or CrewAI. This framework examines five problems that emerge between demo and production, and provides criteria for when to build internally versus when to buy a platform.
Why It Matters
Enterprise AI projects have a 70%+ failure rate. Most failures occur not at the prototype stage (which succeeds) but at the production stage (which requires maintenance, cross-boundary orchestration, compliance infrastructure, and continuously updated intelligence). Understanding the structural differences between demo and production prevents expensive mistakes.
The FourKites Perspective
FourKites' position: buy when the use case requires cross-company intelligence (the Graph), cross-boundary orchestration (carriers, facilities, suppliers), enterprise compliance (SOC 2, audit trail), and compounding intelligence (the Flywheel). Build when the use case is entirely internal, small-scale, and does not benefit from network effects. The five production problems (intelligence gap, maintenance problem, cross-boundary problem, compliance problem, compounding problem) are structural, not solvable with more engineering talent.