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

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

A digital-only bank can’t afford to retrofit security. Ally Financial builds it in from the start.

In this episode of Technovation, Peter High speaks with Spencer Cremers, Chief Information Security Officer at Ally Financial, about designing security and resiliency into cloud infrastructure by default — and what that requires organizationally.

Cremers spent 20+ years at Ally progressing through divisional CIO and engineering roles before becoming CISO, including a stretch as interim Chief Information and Data Officer. That delivery background shapes how he positions security: not as a gatekeeper handing down requirements late in the cycle, but as a design partner embedded from the start.

Key Highlights:

Why Ally’s cloud environment uses templatized resiliency patterns so application teams inherit security standards automatically

How a three-bucket framework separates AI-in-defenses, AI-enabled external threats, and governing Ally’s own AI use

What Cremers learned running a three-day, CEO-involved crisis simulation with concurrent threat vectors

Why a CISO with a delivery background can dismantle the adversarial dynamic between security and engineering

How Cremers managed the interim CIDO role without putting the organization into a holding pattern

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

 

What separates a great founder from a great company? For XYZ Venture Capital Founder and Managing Partner Ross Fubini, execution is only part of the equation. The company also needs to find its “wave.”

In this episode of Technoventure, Fubini joins Peter High to unpack the investment philosophy behind XYZ’s bets on founder networks, defense technology, and AI. Drawing on his experience as an engineer, entrepreneur, executive coach, and investor, he explores:

Why XYZ built an early thesis around the Palantir talent network

What Fubini saw in Anduril before other investors embraced defense technology

Why exceptional execution still needs a market “wave”

How AI changes software development, defensibility, and venture investing

Why forward-deployed engineers are becoming increasingly important

Fubini also shares how he personally uses AI to research, challenge investment theses, and sharpen his thinking.