Having the right data and AI models doesn't always translate into business results. What if the missing piece is understanding how your business actually works?

In this episode, Shirley Macbeth sits down with Jennifer Hamel, Research Vice President of Enterprise Data and AI Services at IDC, to explore why business context is essential to successful enterprise AI. Jennifer shares insights from IDC's research on what separates organizations generating measurable AI value from those still stuck in experimentation.

They discuss how organizations can turn fragmented data and institutional knowledge into reliable AI capabilities. Jennifer explains how knowledge graphs, ontologies, and semantic layers help AI agents make better decisions, why capturing employee expertise matters, and how human oversight must evolve as AI becomes more autonomous. They also explore the importance of governance, cross-functional collaboration, and the right expertise in scaling AI and delivering measurable business outcomes.

Key takeaways:

• Why business context is essential for reliable, scalable enterprise AI
• How knowledge graphs, ontologies, and semantic layers improve AI decision-making
• Why capturing tribal knowledge and unstructured data is critical to AI success
• How human-in-the-loop and human-on-the-loop approaches are changing work
• Why employee involvement and cross-functional collaboration matter for AI adoption
• How governance, domain expertise, and operating models support AI at scale
• Where organizations should start to move beyond AI pilots and deliver measurable business value

Highlights:

(00:00) Introducing Jennifer Hamel

(01:54) What context really means for AI

(04:30) How knowledge graphs and semantic layers connect data

(08:56) Unlocking unstructured data and tribal knowledge

(11:08) Why employees are essential to AI transformation

(12:54) Human-in-the-loop vs. human-on-the-loop

(16:10) Overcoming organizational barriers to AI adoption

(18:52) Combining technical expertise with business knowledge

(20:45) Capturing institutional knowledge before employees retire

(23:11) Where to start and how to scale AI

(24:34) Rapid-fire insights on the future of AI

Resources:

• Jennifer’s LinkedIn: https://www.linkedin.com/company/idc/home/
• IDC website: https://www.idc.com/
• IDC LinkedIn: https://www.linkedin.com/company/idc/
• Shirley's LinkedIn: https://www.linkedin.com/in/shirleymacbeth/

Agentic AI is reshaping insurance, but how can organizations move beyond simply adopting AI and rethink the way work gets done?

In this episode, Shirley Macbeth sits down with Ronak Doshi Partner at Everest Group and Sarat Varanasi SVP and Global Insurance Practice Lead for Analytics, AI Services, and Platforms at EXL, to explore how agentic AI is helping insurers modernize legacy systems, unlock hidden insights, and build the next generation of insurance operations. Together, they discuss why AI creates the most value when organizations rethink processes, workflows, and operating models rather than simply adding technology to existing ways of working.

You will hear how agentic AI is helping insurers accelerate product innovation, transform customer experiences, and make better decisions using previously disconnected data. Ronak and Sarat also share why successful AI transformation requires strong governance, change management, and a human-centered approach as people and AI agents begin working together.

Key takeaways:

• Use AI to modernize legacy systems, uncover insights, and improve insurance workflows
• Redesign processes around agentic AI instead of layering technology onto outdated ways of working
• Combine human expertise, governance, and AI capabilities to create scalable transformation

Highlights:

(00:00) Introducing Ronak Doshi and Sarat Varanasi

(02:13) The trends in the insurance industry today

(03:31) How AI is accelerating product innovation and customer experiences

(07:27) Why AI can modernize legacy systems

(08:22) How AI is helping organizations understand complex technology environments

(10:21) Turning unstructured data into actionable insights

(11:50) How data enables intelligent workflows

(14:55) Why change management is critical for AI adoption

(16:27) How work decomposition supports AI transformation

(18:20) The future of human and AI agent collaboration

(19:42) Building AI-native workflows instead of adding AI to existing processes

(22:35) The biggest lessons for making AI transformation real

Resources:

Ronak's LinkedIn: https://www.linkedin.com/in/ronak-doshi-everestgroup/

Everest Group’s LinkedIn: https://www.linkedin.com/company/everest-group/

Everest Group website:https://www.everestgrp.com/

Sarat's LinkedIn: https://www.linkedin.com/in/varanasisarathbabu/

EXL's LinkedIn: https://www.linkedin.com/company/exl-service/

EXL website: http://www.exlservice.com/

Shirley's LinkedIn: https://www.linkedin.com/in/shirleymacbeth/

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/