Technovation with Peter High (CIO, CTO, CDO, CXO Interviews)

Most generative AI programs stall at the pilot stage. Zoetis reached 95% colleague adoption.

In this episode of Technovation, Peter High speaks with Keith Sarbaugh, Chief Digital and Technology Officer at Zoetis, about how the world’s leading animal health company built an AI platform that colleagues actually use, and how Sarbaugh measures whether any of it is working.

Sarbaugh spent over two decades in human pharmaceuticals at companies including Eli Lilly and Biogen before joining Zoetis in 2023. He now oversees IT, AI, data, cybersecurity, and global customer experience, and describes the role as more hybrid business-technology leader than traditional CIO.

Key Highlights:

Why Zoetis built a model-agnostic platform (Zen AI) with 10–11 LLMs rather than standardizing on one

How a 350-person global Change Champion Network drove adoption faster than any top-down mandate

What Sarbaugh’s Business Value Realization Framework requires teams to define before a project begins

Where agentic AI earns its place: rules-based, high-value decisions like supply chain demand forecasting

Why aggregating all customer touchpoints at 10x prior volume revealed service failures that NPS never could

This episode is sponsored by Tines. This episode is presented by Tines: Freedom to build. Complete security. No compromises. Learn more at tines.io

The next generation of technology giants may not be built in software. They may be built in factories.

In this episode of Technoventure, Peter High speaks with Lior Susan, Founder and Managing Partner of Eclipse, about why AI, robotics, and industrial innovation are transforming the economics of American manufacturing.

Lior explains Eclipse’s five-forces framework for building industrial companies and why physical industries represent a significant opportunity for venture capital.

They explore:

How AI and robotics enable competitive domestic manufacturing

Why industrial startups need operating expertise beyond capital

The infrastructure opportunities emerging from AI’s energy and compute demands

Why humanoid robotics may be getting ahead of practical applications

How policy and national security are shaping industrial investment

Listen to discover why Lior believes this is an extraordinary moment to build companies in the physical world.

Mastercard processes billions of transactions, and fraud doesn’t wait for rules-based systems to catch up. In this episode of Technovation, Peter High speaks with Nirav Mehta, Chief Technology Officer at Mastercard Services, about how the company uses AI to score transactions in the milliseconds between a card tap and approval, and what it takes to build that kind of infrastructure at scale.

Nirav leads engineering, analytics, and decisioning for Mastercard’s $14B Services business, which spans fraud prevention, threat intelligence, data commercialization, and emerging agentic commerce. He explains how Decision Intelligence Pro prevented roughly $13B in fraud in 2026 alone before the holiday season, why the company treats data unification as a use-case-first problem rather than an architecture mandate, and how agentic commerce today resembles the early web, competing standards, good intent, unresolved identity questions. He also makes the case that senior technology leaders need to stay hands-on, not just well-read.

Key Highlights:

Decision Intelligence Pro stopped approximately $13 billion in fraud in 2026 year-to-date by applying AI models in the milliseconds between card tap and transaction approval

Mastercard’s Data Commercialization Platform reduced location-data staleness from 8-10 weeks to hours and cut processing time from multiple weeks to under 10 minutes

Agentic commerce is still in early-standards chaos, Mehta compares it to the web in its first years, and Mastercard’s bet is that its payments trust transfers directly to machine-to-machine contexts

GenAI is compressing engineering team size: a two-person pod plus ten AI agents is already viable for smaller programs, and the scarce skill is now systems thinking, not coding ability

Mehta makes the case for hands-on experimentation over passive learning, logging 1-1.5 hours of personal vibe coding daily since December 2025

This episode is sponsored by Tines. Freedom to build. Complete security. No compromises. Learn more at tines.io

Proving AI works was never the hard part. The hard part is industrializing it. In this episode of Technovation, Peter High speaks with Dilip Venkatachari, Chief Information and Technology Officer at U.S. Bank, about what it actually takes to move from scattered AI experiments to a common platform with reusable components and measurable outcomes. Drawing from eight years leading technology at one of the country’s largest banks, Venkatachari explains how U.S. Bank treats model risk management and regulatory requirements as design parameters rather than obstacles, why the real technical challenge is plumbing rather than models, and how an internal AI marketplace uses social incentives to drive reuse without mandating it.

Key Highlights:

Why industrialization, not proof-of-concept, is the defining AI challenge for large enterprises

How U.S. Bank treats regulatory requirements as design inputs rather than blockers to AI deployment

Why the best AI system creates no value if people don’t change their workflows

How an internal AI marketplace and citation-style reuse incentives prevent redundant technology stacks

Why AI is emerging as an unexpected solution to the COBOL skills retirement crisis

How frontier model companies are shifting from LLM providers to enterprise deployment partners

This episode is sponsored by Tines. This episode is presented by Tines: Freedom to build. Complete security. No compromises. Learn more at tines.io

AI is reshaping every stage of pharmaceutical distribution, but only if the organization is built to absorb it.

In this episode of Technovation, Peter High speaks with Pawan Verma, Executive Vice President and Chief Data and Information Officer at Cencora, about leading technology and data strategy at one of the world’s largest healthcare companies ($320B+ in revenue, 5 million pharmaceuticals moving through its network daily).

Verma joined Cencora in 2024 after serving as Global CIO at MetLife, with earlier leadership at Foot Locker, Target, and Verizon, a cross-industry arc that shapes how he thinks about patient access, workforce transformation, and what AI can and cannot replace in leadership.

Key Highlights:

Why “centered-out” change management outperforms both top-down mandates and bottoms-up experimentation

How Cencora evaluates AI investments against a four-bucket prioritization model, including long-term sustaining value

Where AI is shortening the pharmaceutical chain: virtual clinical cohorts, faster drug discovery, and real-time inventory optimization

Why pharmaceutical supply chains demand a different standard of precision than conventional logistics

What AI cannot replace: the ability to unlearn, reframe on new emotional data, and hold authentic human connection

This episode is sponsored by Tines. This episode is presented by Tines: Freedom to build. Complete security. No compromises. Learn more at tines.io