Sep 10, 2026 · 13 min
Enterprise AI needs anchor use cases, not scattered pilots
EP 69: "You Don't Build a Power Plant to Charge Phones" — Why Enterprise AI Should Start Big | Nikunj Bajaj, TrueFoundry
As AI budgets tighten, the difference between durable enterprise adoption and abandoned experimentation increasingly comes down to governance, attribution, and measurable returns.
- 1Anchor AI programs in high-value enterprise workflows before expanding to broader adoption.
- 2Centralized control planes can enforce policies, protect sensitive data, and attribute model usage across organizations.
- 3Model routing and right-sizing are becoming essential as enterprises scrutinize token costs and AI returns.
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Nikunj Bajaj’s central argument is that enterprises should build around a major, high-value workflow first, then use the resulting infrastructure to support broader AI adoption.
The brief
Nikunj Bajaj, co-founder and CEO of TrueFoundry, argues that enterprises should begin AI programs with a high-value anchor use case rather than disconnected pilots.
TrueFoundry’s control plane connects agents, models, MCPs, and sub-agents through a common gateway, adding observability and policy enforcement to deployment.
The interview links failed pilots to weak guardrails, inadequate data controls, and incidents that force organizations to roll deployments back.
Governance becomes operational through request tagging by developer, user, business unit, permissions, and other attributes, while sensitive data such as PII stays protected.
The sharpest cost lesson is to route each task to an appropriately sized model, replacing indiscriminate token spending with use cases that can produce measurable returns.
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