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

The binding constraint on AI adoption is not access to tools. It is the organizational fear and mindset shift required to reimagine work entirely.

In this episode of Technovation, Peter High speaks with Kristie Grinnell, EVP and Chief Information Officer at TD SYNNEX, about building company-wide AI fluency across a global workforce of nearly 30,000 employees.

Grinnell joined TD SYNNEX roughly two years ago and holds executive sponsorship of its AI fluency strategy alongside full IT operational scope. She describes how TD SYNNEX structured fluency at three tiers, required every ELT member to produce an AI plan, and built Fluidworks.ai as an internal factory to scale governed AI across the organization.

Key Highlights:

Why AI adoption fails as a change management problem before it fails as a technology problem

How a tiered fluency certification gives every employee a concrete path to AI readiness

Why every AI agent at TD SYNNEX must carry a registered identity and a named human owner

How agentic SOC triage turned tier-1 analysts into threat hunters

Why TD SYNNEX policy holds employees accountable for the AI outputs they use

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

Healthcare has accumulated friction that patients have come to accept as normal. Novant Health’s Chief AI Officer is systematically dismantling it.

In this episode of Technovation, Peter High speaks with Vijay Sankararaman, Chief AI Officer at Novant Health, about deploying AI across patient experience, clinical operations, and proactive population outreach at a system with 22-plus hospitals and 8 million annual patient encounters.

Sankararaman came to healthcare from retail product and engineering leadership at Walmart and Lowe’s, and he brought a problem-first discipline with him: every AI initiative must justify the problem it solves before the technology is considered. His team has deployed a 24/7 surgical companion, a lung-cancer screening outreach program that contacted more than half of 20,000 at-risk patients in a fraction of the time human call programs managed, and a mammography scheduling agent that drove call abandonment to near zero.

 

Key Highlights:

Why the hardest part of a CAIO’s job is saying no to AI proposals

How a conversational AI agent reached thousands of at-risk lung-cancer patients human callers could not

What 40% after-hours engagement reveals about suppressed patient demand

Why the CEO’s frame on AI (technology feature vs. business reimagination) determines whether a CAIO role makes sense

How Novant’s build/buy/co-develop architecture avoids vendor lock-in without slowing deployment

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

Most generative AI programs stall at the pilot stage. Zoetis reached 95% computer-based 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