Most enterprise AI initiatives do not fail because models are weak. They fail for two more basic reasons: 1) AI is not given a reliable way to read business context or take action across systems, and 2) organizations do not translate ambitious AI visions into clearly defined outcomes, projects, and next steps.
In other words, the problem is often both architectural and operational.
This session introduces a practical framework for making enterprise AI actually work. On the technical side, I will outline two core ideas: a Read Contract, which gives AI governed and auditable access to live business context, and a Write Contract, which defines how AI can safely and reliably execute actions across enterprise systems. On the execution side, I will show why AI initiatives also need a more disciplined planning model - one that breaks the vision down into concrete outcomes, projects, and tasks rather than leaving teams stuck with broad aspirations and disconnected pilots. Attendees will leave with a clear framework for diagnosing why AI efforts stall, designing stronger foundations for production use, and turning AI strategy into executable enterprise delivery.
Kanishk Mittal is a Principal Technology Architect and developer at InterSystems, where he leads the development of an enterprise-wide data lake supporting analytics and AI. He has spoken at Ai4, PegaWorld, and InterSystems READY on topics spanning data, enterprise architecture, and emerging technology.
Kanishk concurrently pursued a master’s degree in Computer Science from Georgia Tech and graduated from Harvard University with a double degree in Physics and Mathematics and a minor in Economics. He is passionate about technology’s ability to solve human problems at scale and enjoys connecting with others around sports, emerging tech, books, and the outdoors.