The Data & AI Chief

For most data leaders, governance feels like the thing standing between them and progress. Bharathi Rajan, Vice President of Digital, AI, and Data at Swire Coca-Cola, joins Cindi Howson to share how she built the data foundation powering a $3 billion supply chain operation. She breaks down how to turn data quality into a business accelerator, get ahead of demand signals, and build the foundation every AI initiative actually depends on.

Key Moments:

Building a Data Foundation Across a $3B Supply Chain (06:39): The starting point wasn't strategy. It was figuring out where the data was, who had it, and how to get it into the hands of actual decision-makers.

Reframing Data Governance (10:22): Governance is the foundation that makes better decisions possible. Bharathi shares how education and relationship-building drove the mindset shift at Swire Coca-Cola.

AI as an Enablement Factor (11:50): Bharathi reframes AI as a tool for operational efficiency and more impactful work, not a headcount threat.

Building the Plant of the Future (14:41): A new $475 million Colorado facility gives Swire Coca-Cola a rare chance to design data and AI infrastructure from scratch.

The Skills That Will Matter Most in an AI World (17:58): Bharathi breaks down what she tells young people and aspiring data leaders about building a career that AI can't replace.

Key Quotes:

“Governance is not red tape. It is important. If you want to make decisions with the right data, you need to have governance. You have to have that quality data flowing in.” - Bharathi Rajan

“If you're passionate about something and then you know how to use technology to enable that, that makes the difference.” - Bharathi Rajan

“AI, it's an enablement factor for us internally as to how can I help the enterprise really grow, create operational efficiencies, but also have people do more impactful work.” - Bharathi Rajan

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Guest Bio

Bharathi Rajan is a results-driven and experienced Chief Data Officer and AI strategist.  In her current role as Vice President Digital, AI & Data at Swire Coca-Cola, USA, Bharathi has saved multi-millions by adopting new tech stack and bringing in capabilities critical for Enterprise growth and performance. While consistently leading innovation and enabling AI & data literacy, Bharathi consecutively drives data and systems architecture confluence across the enterprise.

Prior to joining Swire Coca-Cola, USA, Bharathi operated as a Senior Director of Operations in Data and Infrastructre for three years, focusing on Enterprise Reporting and Analysis (ERA), where she was specifically hired for her distinctive capabilities in data strategy, cloud migration, and creative efficiencies. 

Bharathi is currently ranked #3 DataIQ100 North America for 2026. She has attended several panel discussions, in Emory University, Women in Tech, AI & Manufacturing and DataIQ. In 2026, Bharathi delivered a keynote on Data Leader as the Transformation Architect at the DataIQ summit. Additionally, Bharathi has spoken at various summits like Microsoft Ignite, Databricks, AI&Data summit and Snowflake summit over the years. Bharathi is also a mentor with WLDA Ventures and Women Tech Council.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

What happens when a software company building AI tools for HR teams uses those same tools to transform itself? Josh McKenzie, Chief Technology Officer at ELMO Software Group, shares how his team rebuilt their entire software development lifecycle around AI agents and redrew the boundaries of every engineering role. He breaks down how to lead that shift without losing people's trust, why domain expertise is the real SaaS moat, and how the right analytics partner unlocks decisions HR teams have never been able to make before.

Key Moments:

The SaaS Moat: What AI Can't Erode (06:37): Josh argues SaaS value runs deeper than software. Accountability, compliance, and domain expertise keep purpose-built platforms irreplaceable.

How ELMO's AI Journey Started (10:23): ELMO started by mapping every role against AI impact. Turning that lens on their own engineering team set the full transformation in motion.

Why ELMO Chose ThoughtSpot Over Building Its Own Analytics (18:42): A homegrown tool requiring too much user expertise led ELMO to look elsewhere. ThoughtSpot Spotter and natural language capabilities closed the gap.

Why HR Teams Are the Most Underserved (20:21): Payroll here, benchmarking data there, performance data somewhere else. HR teams have been drowning in spreadsheet hell for years. Josh explains how AI finally closes that gap.

From Engineer to CTO: Build a Team of Complements (24:17): Josh reflects on the mindset shift that defined his path to the C-suite. Great leadership means building a team whose strengths cover your blind spots.

Key Quotes:

“ ThoughtSpot was particularly interesting for us… The big thing for us was the Spotter product. Allowing users to bridge that data analyst gap was really important. So, that product has yielded really, really great results for us.” - Josh McKenzie

“I think it's really important that we instill a culture where it's okay to fail, and it's okay to make a mistake. You want to be vocal about your mistakes so others don't repeat the same mistake.” - Josh McKenzie

“My belief is you want to focus on your secret sauce. So, what is the thing that makes your business super successful? And for us, that's where we came to look at ThoughtSpot. It has a really nice visual user interface and allows you to create some great dashboards.” - Josh McKenzie

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Guest Bio

As the Chief Technology Officer, Josh McKenzie is responsible for both technical strategy and delivery (build, release and operation) of the ELMO product suite. Josh has a proven track record of successfully leading technology teams and implementing transformative strategies that enhance efficiency, drive growth, and elevate overall technological capabilities.

Josh has 20 years of experience in technology, primarily in FinTech. Before joining ELMO in 2024, Josh held executive and senior positions at Lendi Group, OFX, ASX and Westpac. Josh holds a Bachelor of Computer Science from the University of Newcastle and an MBA from the University of Sydney.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Explore how a global supply chain company turned its data platform into a customer-facing product designed to operate at the speed of disruption. Boris Rabkin, Chief Information Officer at Ligentia, shares how the company executed that shift through a deliberate phased approach and a partnership with ThoughtSpot. He breaks down how to build a data foundation that scales, what it takes to embed analytics where decisions happen, and how to structure AI ownership and governance across a global regulatory environment.

Key Moments:

From Reactive to Proactive with Agentic AI (04:46): Supply chain disruption response has changed from slow email chains and fragmented data to agentic systems that flag issues and test decisions in real time. Boris illustrates how Ligentia navigated that shift firsthand.

Embedding Analytics Into the Customer Platform (09:00): Boris explains why bolting analytics onto a separate tool creates friction and why embedding intelligence directly into the existing customer platform is the better call.

How to Phase a Data Transformation That Sticks (12:12): Boris outlines three phases: stabilize the foundation, standardize definitions, then build a usable experience. Skipping the plumbing is where most transformations fail.

Where AI Ownership Really Belongs in the Enterprise (14:03): Understand why AI ownership should sit where value is created. Learn how centralized governance ensures data accuracy and security across the organization.

What the Asyad Acquisition Unlocks for Ligentia (22:49): Boris shares how the new investment opens doors to scale the platform globally, automate logistics workflows, and monetize data beyond services.

Key Quotes:

“ We wanted to control the brand experience, the same login for our customers. Removing the friction and having the experience of being in one trusted platform for making those decisions… This is where [ThoughtSpot] came in.”  - Boris Rabkin

“I think AI should be owned where value is created. It shouldn't be a centralized  function inside a lab. If it's not close to the product and the people that are using it, AI won't create the value.” - Boris Rabkin

“Speed is one thing, but confidence in the data is something that really drives decisions.” - Boris Rabkin

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Guest Bio

Boris Rabkin is the Chief Information Officer at Ligentia. As a Chief Information Officer and Board Member, he brings a distinctive blend of strategic vision and execution capabilities to drive business growth and operational excellence through digital transformation. With extensive experience leading global teams and technology initiatives, Boris is driven by a passion for leveraging data, AI, and automation to build scalable, secure, and resilient enterprises that deliver lasting value.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Understand how to close the gap between AI experimentation and enterprise production. Shub Agarwal, Founder of the AI Trust Lab at USC and author of Successful AI Product Creation: A Nine-Step Framework, shares his AI product management framework for taking enterprise AI strategy from demo to production, drawing on two decades of product leadership at Amazon and Fortune 50 firms. He breaks down why experimentation must tie directly to business OKRs, the four mindset shifts leaders need to scale AI responsibly, and how the AI Trust Lab is building a benchmark evaluation framework for AI model trust and governance.

Key Moments:

Why 80% of AI Projects Never Reach Production (02:13): Shub traces the root cause of stalled AI programs to a missing system for moving from demo to deployment. Most teams have no repeatable path to production.

Shub's Nine-Step Framework for Building AI Products (06:00): Most AI projects start with a cool model instead of a painful problem. Shub walks through the three phases of his framework: discovery, execution, and excellence.

The Case Against "Fix Your Data First" (12:41): Conventional wisdom says clean your data before building AI. Shub challenges that, arguing modern LLMs offer far more flexibility with imperfect data.

Four Mindset Shifts for Scaling Enterprise AI (16:35): Shub outlines the four shifts separating organizations that scale AI from those that stall, from measuring AI performance differently to embedding trust from day one.

Inside Shub's AI Trust Lab at USC (23:54): Major foundation models are already being benchmarked on trust and safety. Shub explains the lab's mission to build a standardized evaluation framework for AI model governance.

Why Enterprise AI Governance Needs Multiple Disciplines (28:36): AI models can be sycophantic, manipulative, or lack candor. Shub argues that building trustworthy AI demands an interdisciplinary approach.

Key Quotes:

“I think the fundamental problem that organizations are facing today… is not that they have a lack of experimentation in the demo aspect. The challenge is they don't know how to take those demos to production, and that is where I saw the gap.” - Shub Agarwal

“I do think data is the fuel for AI… But I think today organizations are crippled by this ‘fix your data, and then we'll build AI’, and they never build AI. They never build use cases that are adding value.” - Shub Agarwal

“There's no FICO scores for models, so I decided to create one. I built this lab… bringing the computer scientists, the researchers, the applied AI researchers, the policy, and the communication people together to think of what is trust, define it, and ultimately measure and evaluate it.” - Shub Agarwal

Mentions

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Guest Bio

Shub Agarwal is an associate professor of professional practice at the University of Southern California, an industry executive, and an advisor to start-ups and academic institutions. He holds an MBA from the University of California, Los Angeles (UCLA), and an MS from Carnegie Mellon University (CMU). He is the author of two books: Solve Catch-22 of Product Management and Successful AI Product Creation: A 9-Step Framework. He has made significant contributions to the fields of artificial intelligence and machine learning, holding several U.S. and global patents for his work, and is also a published author of several technical research papers.

With around two decades of extensive experience in product management and leadership, his journey has been marked by a relentless pursuit of leveraging AI technologies to create impactful products that redefine industry standards. His industry experience includes leadership roles at Amazon, Silicon Valley start-ups, and other Fortune 50 firms.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

Learn what happens when the executive accountable for data strategy is also the executive accountable for the business results that depend on it. Saugata Saha, President of S&P Global Market Intelligence and Chief Enterprise Data Officer at S&P Global, shares how he manages one of the world's largest financial data estates while driving business outcomes across public and private markets. He breaks down the four pillars of S&P Global's data strategy, the federated organizational model that connects data teams to business value, and why capturing ROI from AI requires deliberate workflow transformation.

Key Moments

Why Data Strategy Must Follow Business Strategy (04:57): Saugata challenges the idea that data and business strategy can run in parallel. Market trends, customer pain points, and existing capabilities must come first.

Building an AI-Ready Financial Data Estate (15:10): Scale alone does not create intelligence. Saugata explains why semantic layers and graph databases are the hard work behind connected financial data.

How AI Compresses Post-Acquisition Data Integration (18:29): Manual reconciliation of millions of records is no longer the only path. Discover how AI entity matching accelerated post-acquisition integration.

The Federated Model That Connects Data to Value (22:49): Most large organizations either over-centralize data teams or leave them too embedded to scale. Saugata outlines the federated model that actually bridges both.

Rethinking AI Productivity: From Marginal to Transformative (28:29): Most AI programs stop at training and tooling. Saugata explains why deliberately redesigning workflows is the missing step between AI investment and real ROI.

Key Quotes

“Data strategy and business strategy have to be very tightly connected. And if they're not, that's when value capture does not happen. In fact, I would go so far as to say data strategy actually follows from business strategy.” - Saugata Saha

“Stop treating data as an afterthought or byproduct, but start thinking about data as a key ingredient for value creation and competitive advantage.” - Saugata Saha

“We don't want everybody to become 10% more productive, because that's a little squishy. We want 10% of the people to become a hundred percent more productive so they can do other things.” - Saugata Saha

“If a company can really use data at scale for better decision making, better client service, [and] better outcomes, that creates a lasting edge over the competition.” - Saugata Saha

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Guest Bios 

Saugata serves as President of S&P Global Market Intelligence, leading the division's efforts to deliver essential insights and intelligence to clients worldwide. He is also S&P Global’s Chief Enterprise Data Officer, responsible for driving innovation and excellence in the company’s enterprise data strategy. Saugata is a member of S&P Global’s Executive Leadership Team, contributing to the strategic direction and growth of the organization.

Before joining S&P Global, Saugata was a consultant at McKinsey & Company’s New York office, where he advised clients on strategy, mergers and acquisitions, corporate finance, and operational improvements across various industries, with a strong focus on financial services.

Hear more from Cindi Howson here. Sponsored by ThoughtSpot.