In this episode, Sanjay Sankolli, Chief Architect for AI and Data at Truist Financial Corporation, explains where AI is delivering real, measurable impact today inside a large, regulated enterprise.

Moving beyond the hype around autonomous AI, Sankolli highlights how organizations are seeing gains through decision augmentation, workflow acceleration, and improved developer productivity.

Key topics include:

🔹Why augmentation is outperforming automation today

🔹How agentic workflows are evolving beyond traditional RPA

🔹The growing importance of unstructured data in enterprise AI

🔹Why developer productivity is a critical, overlooked enabler

🔹The role of fragmented data architectures in limiting AI at scale

In conversation with Karan Jain of NayaOne.

This episode is Part 2 of a 4-part series on operationalizing AI in banking.

AI in oncology is often discussed in terms of breakthroughs. But inside leading cancer centers, the focus is much more practical: what works, for whom, and how do you measure success?

In this first episode of a four-part series, Nasim Eftekhari, Chief AI and Analytics Officer at City of Hope, joins Erik Pupo of Guidehouse to examine how oncology AI is evaluated in real-world settings.

Eftekhari outlines why performance metrics take priority over financial return, why FDA-approved solutions still require local validation, and where AI is already delivering measurable impact across diagnostics and clinical workflows.

The conversation also explores how organizations should think about success versus failure when outcomes can range from incremental improvements to life-or-death impact.

A grounded discussion on what it actually takes to make AI work in cancer care.

In the final part of this three-part series, Edgar Gallo, Chief Data Officer at Daimler Truck North America, shifts the conversation from AI use cases to what it actually takes to scale AI inside an enterprise.

The discussion explores the human and operational realities of AI adoption, including how leaders build trust, design ownership into the process, and create safe environments for responsible use.

In conversation with Susan Wilson of Alation, Gallo also shares why adoption matters more than model sophistication, and how the next phase of AI will depend on interoperability across enterprise ecosystems.

This episode offers a practical perspective on what separates AI experiments from real, sustained impact.

In this episode, Katrin Botzen, Corporate Director of Global Data and Analytics at Henkel, shares how the company applies AI across R&D, supply chain, and finance to solve real business problems.

The discussion explores:

🔷Why starting with business problems is critical for AI success

🔷How fragmented data limits decision-making

🔷The role of data architecture and governance in scaling AI

🔷Real-world use cases, from R&D optimization to finance insights

In conversation with Julian Schirmer of OAO.

This episode offers a grounded view of enterprise AI, focusing on execution, not just ambition.

In this episode, Sanjay Sankolli, Chief Architect for AI and Data at Truist, joins Karan Jain, Founder and CEO of NayaOne, to explore why enterprise AI often stalls before delivering real value.

The conversation focuses on the structural challenges behind AI adoption, including weak data foundations, misaligned operating models, and the complexity of scaling in regulated environments.

You’ll hear:

🔹Why AI must be treated as an operating model shift, not just a technology upgrade

🔹What changes when moving from pilot to production

🔹Why the “last mile” is where most AI initiatives break down

🔹How regulatory uncertainty is influencing enterprise decision-making

🔹Why multi-vendor evaluation is becoming the new norm

This is Part 1 of a 4-part series on operationalizing AI in the enterprise.

AI might be the headline, but execution is the story.

In the final part of this three-part series, Philippe Rambach gets practical about what it takes to make AI work inside a large enterprise.

This episode explores:

🔸Why most of AI transformation is really change management

🔸What goes wrong when AI is introduced as a new tool

🔸How embedding AI into existing workflows drives adoption

🔸Why training 140,000 employees is not optional

🔸And how AI leaders spend their time recalibrating expectations

“If you need 100% accuracy, let’s not waste time.”

A grounded conversation on what it takes to move AI from experimentation to real business impact.

Featuring Rambach in conversation with Dr. Julian Schirmer, Co-Founder at OAO.

In this episode, Edgar Gallo, Chief Data Officer at Daimler Truck North America, shares how agentic AI is being applied to real-world supply chain challenges.

The discussion with Susan Wilson of Alation, focuses on how AI agents can reduce repetitive work, support planners, and improve responsiveness in complex manufacturing environments.

Gallo explains how breaking work into decision-making and execution enables AI to handle structured tasks while preserving human judgment where it matters most.

The conversation also explores how trust is built, not through technology alone, but when AI and human reasoning consistently arrive at the same outcomes.

This episode offers a grounded perspective on what it takes to move from AI experimentation to real operational value.

As organizations invest heavily in data, analytics, and AI, one challenge continues to hold many initiatives back: adoption.

In this episode, Mars’ Ujjwal Sehgal, Global Head of People Analytics, and Rachel Belino, HR Data Officer, discuss how the company is tackling this issue across its global workforce analytics initiatives.

Speaking with Shachin Prabhat of Tiger Analytics, they explore the barriers that often prevent analytics solutions from gaining traction, including complex dashboards, the lack of trust in data, and the challenge of delivering insights at the right moment.

They also explain how Mars approaches governance, protects sensitive workforce data, and builds the data foundations needed for AI-ready analytics environments.

The conversation offers practical lessons for data and analytics leaders working to turn analytics investments into real business adoption.