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/

Healthcare generates enormous amounts of data, yet much of it still goes unused. So what needs to change before AI can make a meaningful impact?

In this episode, Shirley Macbeth sits down with Arun Rangamani, SVP of Healthcare Payment Services at EXL, to explore what it actually takes to apply AI across one of the most complex industries in the US. Arun explains why getting the data right is the first step, how combining structured and unstructured healthcare data can unlock new insights, and why context and domain expertise are just as important as the technology itself.

They also discuss why healthcare organizations should avoid simply bolting AI onto existing processes, how AI can help healthcare professionals make better decisions rather than replace them, and where Arun sees the biggest opportunities ahead, from reducing administrative inefficiencies and waste to improving care coordination and managing chronic conditions more effectively.

Key takeaways:

• Why healthcare AI transformation starts with getting the data right
• How unstructured medical records can unlock deeper clinical and financial insights
• Why context and domain expertise are essential to effective AI
• How AI can augment healthcare professionals and improve decision-making
• Why point solutions can fall short in a highly connected healthcare ecosystem
• Where AI could have the greatest impact on care, cost, and efficiency

Highlights:
(00:00) Introducing Arun Rangamani
(02:35) The current state of AI in healthcare
(04:00) Why transformation starts with data
(05:24) Unlocking unstructured healthcare data
(08:08) What healthcare can learn from other industries
(10:38) Why context is critical to AI
(13:13) How AI can support healthcare workers
(16:25) Why point solutions fall short
(18:46) The future of AI in healthcare

Resources:

Arun's LinkedIn: https://www.linkedin.com/in/arunrangamani/

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/

AI is transforming healthcare, but how can organizations harness its benefits while staying ahead of increasingly sophisticated threats?

In this episode, Shirley Macbeth sits down with Kurt Spear, Vice President of Financial Investigation and Provider Review at Highmark, to explore how AI is improving payment integrity, fraud detection, and operational efficiency. Kurt also shares how his teams are applying AI to identify billing errors, review complex medical records, and uncover emerging fraud schemes.

You will hear why practical use cases matter, how data quality and governance influence AI success, and why human judgment remains essential as healthcare organizations scale AI across critical workflows.

Key takeaways:

• Apply AI to high-impact review workflows where speed and accuracy matter most
• Build AI initiatives around trusted data, governance, and cross-functional input
• Use human expertise to validate AI outputs and stay ahead of emerging fraud risks

Highlights:

(00:00) Introducing Kurt Spear

(01:55) Kurt’s role across fraud investigations and payment integrity

(03:04) The healthcare cost pressures impacting payment integrity

(04:29) Where AI is showing up across the healthcare system

(05:45) Using AI to review records, find errors, and detect fraud

(10:44) How to get started with practical AI use cases

(13:06) Why data quality still matters

(15:20) Bringing teams along in AI adoption

(19:12) Why human judgment is critical to AI decisions

(21:00) Staying ahead of AI-enabled fraud

(22:51) Why AI is now a business imperative

Resources:

Kurt's LinkedIn: https://www.linkedin.com/in/kurt-spear-cfe-cissp-8654842a/

Highmark website: https://www.highmark.com/

Highmark LinkedIn: https://www.linkedin.com/company/highmark/

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

In this episode, Shirley Macbeth sits down with Carolynn Smith, Head of US Service at Prudential, to discuss how AI is reshaping the way Prudential’s service organization delivers outcomes for employees and customers. Carolynn shares insights into how her team is embedding AI within their workflows, leveraging it to improve both employee and customer experiences.

You will hear about the key role frontline employees play in AI adoption, how AI is integrated into service journeys, and the importance of measuring AI outcomes for scaling. Carolynn also reveals how the U.S. Service organization is managing change and driving value by continuously evolving their AI models.

Key takeaways:

• Engage frontline employees in the AI adoption process for greater success
• Use AI to improve operational outcomes while enhancing employee and customer experiences
• Measure what matters to scale AI transformation responsibly

Highlights:
00:00) Introducing Carolynn Smith

(01:00) Why frontline employees are key to AI adoption at Prudential

(02:15) Embedding AI into existing service journeys for better outcomes

(04:00) Overcoming the natural resistance to AI

(06:00) Creating transparency and aligning expectations for successful AI implementation

(08:30) How recognizing and rewarding early AI adopters drives adoption

(10:00) Measuring success with new KPIs to ensure AI effectiveness

(12:15) The role of responsible AI in Prudential’s service transformation

(14:00) Enhancing empathy and customer service with AI

(16:30) From AI pilots to scaling across the organization

(18:00) Looking ahead: How Prudential is rethinking workflows with AI

Resources:

Carolynn’s LinkedIn: https://www.linkedin.com/in/carolynn-smith-2936b921a/

Prudential’s website: https://www.prudential.com/

Prudential’s LinkedIn: https://www.linkedin.com/company/prudential-financial/

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

Why do AI programs stall before they scale?

In this episode, Shirley Macbeth sits down with Tim Rafton, Executive General Manager at IAG, to break down how AI is changing the pace of transformation in insurance. With decades of legacy systems and complex processes, Tim shares how his team is finding faster paths to value without waiting for a full platform overhaul.

You will hear how AI handles unstructured data, where to start for early wins, and what it takes to move from experimentation to real workflow impact.

Key takeaways:

• Start with real workflow pain points to drive early AI adoption
• Use AI to unlock value from unstructured data without waiting for full transformation
• Reimagine processes before applying automation or autonomous agents

Highlights:
(00:00) Introducing Tim Rafton

(01:51) The real challenge of AI adoption in legacy industries

(03:31) Where to start with AI inside your organization

(05:00) Turning scattered documents into an AI-powered knowledge system

(06:16) Why fixing the process comes before applying AI

(07:30) Introducing autonomous agents into claims workflows

(10:30) Using AI to structure unstructured data and improve decisions

(12:22) Smarter triage and routing for better customer outcomes

(13:15) Getting executive alignment for AI transformation

(14:00) Why aiming for 100 percent automation changes everything

(15:30) Moving from slow releases to rapid AI iteration

(17:30) Driving adoption by solving real employee problems

Resources:

Tim’s LinkedIn: https://www.linkedin.com/in/timothy-rafton-62927749/?originalSubdomain=au

IAG website: https://www.iag.com.au/

IAG LinkedIn: https://www.linkedin.com/company/iag/

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

Agentic AI is more powerful than ever. How can organizations keep up with the latest developments to unlock the real potential of AI today?

In this episode of Make It Real, Shirley Macbeth talks to Samrah Khan, Head of North America Solution Services Partnerships at Google, about the rise of agentic AI. Samrah discusses the significance of the newest shifts in AI technology and how it is improving workflows in industries such as finance and healthcare. She unpacks the pivot to the “human on the loop”, what AI agents need to run efficiently in an organization’s system, and why AI adoption doesn’t just mean a shift in tech but in company culture.

Key takeaways:

• The new role of humans in AI-powered systems
• Why data health is the key to successful AI adoption
• How organizations can help employees overcome AI apprehension

Highlights:
(00:00) Introducing Samrah Khan
(04:50) The latest developments in agentic AI
(06:18) From “human in the loop” to “human on the loop”
(10:57) The hurdle keeping organizations from successfully adopting AI
(13:54) How agentic AI is making an impact in financial services

(16:52) The impact of AI on healthcare CX

(21:08) Why change management is a crucial part of AI adoption

Resources:

Samrah’s LinkedIn: https://www.linkedin.com/in/samrahk/

Google LinkedIn: https://www.linkedin.com/company/google/

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