The Single-Model Trap That's Stalling Enterprise AI

InnovationThe Single-Model Trap That's Stalling Enterprise AIBySandeep Shivam,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 08, 2026, 09:30am EDTSandeep Shivam is an Associate Director at Tavant, building AI-powered lending products that improve efficiency and customer experience. getty​During a prospective client engagement at a financial institution, I learned that they had spent four months building an AI agent for a consumer-facing workflow. The team was experienced, the budget was real and the model selection seemed obvious: Pick the strongest large language model (LLM) available and build the agent around it.​The pilot looked promising. The production rollout did not. Latency on consumer journeys crept past acceptable thresholds. Inference costs climbed faster than the value created. Accuracy was uneven across the workflow. After four months, the initiative was paused and redesigned. Not because the technology was wrong, but because the architecture was that is when my team was engaged to redesign it. That story​ is becoming common, with the ​widely cited report from MIT's NANDA initiative last year finding that 95% of enterprise generative AI pilots are failing to deliver measurable financial impact.From my experience, while the model is often the first suspect for pilots stalling, the architecture is the more likely culprit. In most cases, a production AI agent should not be one model doing everything. It should be a coordinated system of specialized models, each doing what it is best suited to perform.The Kitchen Brigade AnalogyThink about how a serious restaurant kitchen works. There is no single chef preparing every dish. There is a brigade. The saucier handles sauces. The grill station handles proteins. The pastry chef handles desserts. The expediter coordinates the pass. Each role exists beca...