Why AI Agents Need Better Operating Models to Scale with Sumit Taneja | EXL cover art

Why AI Agents Need Better Operating Models to Scale with Sumit Taneja | EXL

Why AI Agents Need Better Operating Models to Scale with Sumit Taneja | EXL

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Summary


AI agents are becoming easier to build. But what does it take to move from experimentation to real business transformation?

In this episode, Shirley Macbeth sits down with Sumit Taneja, SVP and global head of AI consulting and implementation at EXL, to explore what separates AI initiatives that scale from those that stall. Sumit shares why organizations need to start with business outcomes, rethink existing workflows, and build operating models that allow AI to create lasting value.

They discuss why simply adding AI to existing processes can limit transformation, how domain expertise and context help AI agents deliver better outcomes, and why organizations need new approaches to governance, measurement, and assurance as AI moves into production. Sumit also shares how leaders can balance innovation with reliability by starting small, building the right foundations, and continuously improving along the way.

Key takeaways:

  • Why successful AI transformation starts with business outcomes, not technology
  • How organizations can move beyond automation and redesign workflows with AI
  • Why data, context, and domain expertise are critical for effective AI agents
  • How AI is changing roles, operating models, and the future of work
  • Why organizations need governance, measurement, and assurance frameworks to scale AI responsibly
  • How leaders can build AI strategies that create measurable business value over time
  • Why better operating models may matter more than better AI models

Highlights:

(00:00) Introducing Sumit Taneja

(01:45) The common AI mistakes that limit transformation

(04:00) Starting with business value, not technology

(05:30) Why AI needs data and domain context

(09:22) How AI changes the underwriter’s role

(12:13) The rise of agent assurance

(14:38) Building guardrails before AI goes live

(16:10) Why you should start small and build in parallel

(17:15) How to measure the real value of AI

(19:52) Who should actually own AI deployment

(21:33) What separates successful AI deployments


Resources:

  • Sumit’s LinkedIn: https://www.linkedin.com/in/sumit1313/
  • EXL website:https://www.exlservice.com/
  • EXL LinkedIn:https://www.linkedin.com/company/exl-service/home/
  • Shirley's LinkedIn: https://www.linkedin.com/in/shirleymacbeth/
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