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

Semiconductor development cycles run a decade or longer, but markets shift faster than any company can predict. Ahmad Bahai, Senior Vice President and Chief Technology Officer at Texas Instruments, explains how TI navigates that gap by betting on directional technology vectors rather than specific end markets. TI’s high-voltage program, started over a decade ago with smart grid in mind, eventually served electric vehicles and then became core infrastructure for AI data centers. In this episode of Technovation, Peter High speaks with Ahmad Bahai, SVP and CTO at Texas Instruments, about long-horizon R&D, the power delivery bottleneck in AI infrastructure, and why transistor scaling is now one of several co-equal innovation vectors. Bahai also reflects on the Bell Labs model, TI’s $60B-plus US fab expansion, and the case for a “virtual Bell Labs” that bridges academic curiosity and commercial urgency.

Key Highlights:

Why TI embeds Kilby Labs researchers inside business units from the start, so innovations never expire on a shelf

How TI’s high-voltage program moved from smart grid to EV to AI data centers without changing the underlying technology platform

Why stepping voltage from 400V at the rack down to sub-1V at the processor is the overlooked bottleneck in AI infrastructure

Why transistor scaling is now one innovation vector among several, with materials and packaging becoming co-equal drivers

Why building fabs during a market trough is the only way to have capacity ready when the next peak arrives

A 70-year-old manufacturer’s proprietary test data may be a more defensible AI moat than software capability alone. In this episode of Technovation, Peter High speaks with Udit Mehta, Chief Technology Officer at Simpson Strong-Tie, about building an AI strategy on a foundation of structural engineering expertise. Mehta joined in 2024 from Carrier and Siemens, and has spent two years modernizing the company’s infrastructure, upskilling 1,200-plus employees on AI, and launching an integrated cloud-native software portfolio for component manufacturers. He describes how Simpson’s combination of hardware, software, services, and proprietary test data creates a position pure-play software entrants cannot match, and why AI must augment engineering judgment rather than replace it.

Key Highlights:

Why Simpson’s hardware-plus-software bundling creates an AI moat pure-play entrants structurally cannot replicate

How AI tools assist structural engineers without removing human judgment from high-stakes decisions

Why workflow embeddedness, not technical feasibility, is the real adoption gate in construction

How a three-pillar AI strategy spans individual productivity, enterprise value, and customer growth in parallel

Why business owners, not AI teams, must remain accountable for every AI-enabled workflow outcome

This podcast episode is sponsored by Blitzy. Autonomous software development at enterprise scale.

Most companies can prove an AI use case works. Scaling it is where they stall. Shweta Gummidipudi, CIO at Twilio, has spent her first year building the infrastructure layer that makes scale possible: a single model gateway for routing and cost control, a shared library of agents and connectors, and inference budgets that keep business-unit builds inside enterprise guardrails. She also addresses two problems most governance conversations skip: agentic identity, which the industry has not solved, and agent sprawl, which she maps directly to the shadow IT problem CIOs spent the last decade managing. Alongside those structural challenges, she describes Twilio's Customer Zero program, where IT runs Twilio's own products in live workflows and converts that experience into reference architectures that enterprise customers adopt faster than vendor documentation alone.

Key Highlights:

Why a single model gateway is the prerequisite for moving from pilots to scale

How Twilio structured a three-tier access model to prevent agent sprawl without blocking experimentation

Why agent identity is categorically different from human identity and what that means for security

How the Customer Zero program turns internal practitioner experience into customer reference architectures

Why career-leveling guides need to be rebuilt from scratch rather than retitled for the AI era

AI efficiency is not the destination. At Sunlife, it is the means to something harder to automate: meaningful human connection.

In this episode of Technovation, Peter High speaks with Laura Money, Executive Vice President and Chief Information and Technology Innovation Officer at Sunlife, about building AI around people rather than processes.

Money joined Sunlife in 2020 with a mandate to connect technology strategy directly to business strategy. She describes a four-year AI adoption journey, from an internal GenAI chatbot in early 2023 to agentic systems governed by a shared control plane built with banking and telecom peers. Her core argument: adoption, not technology, is the real bottleneck, and AI’s value in financial services depends on what it frees people to do, not what it replaces.

Key insights include:

Designing AI agents around people and human decision-making

Creating an “empathy dividend” by reducing administrative work

Using AI to accelerate enterprise architecture and advisor workflows

Evolving agile teams and technology operating models

Collaborating on agent governance through an industry consortium

This episode is sponsored by Tines: Freedom to build. Complete security. No compromises.

This episode is also sponsored by Blitzy: autonomous software development at enterprise scale.

 

AI layered onto a broken process doesn’t fix the process; it just runs it faster. Prakash Kota spent his first 18 months at UKG consolidating two legacy companies into unified CRM, ERP, and data systems before any AI conversation began. Now, as Chief Transformation Officer, he is shifting the question from “what tools can we deploy?” to “what outcome are we trying to reach?” That discipline extends to the customer funnel, where AI-assembled signals travel with a prospect through every handoff, and to sales reps, where an autonomous assistant pushes pre-meeting briefs before a rep has to ask, reclaiming the 40–50% of call-prep time currently spent on research.

Key Highlights:

Why redesigning the process must come before choosing the technology

How UKG unified two legacy companies’ systems as the foundation for AI-era work

The shift from AI as a tool you pull from to an autonomous teammate that pushes the right information to you

How AI-enriched signals travel with a prospect through every funnel handoff, making customers feel individually known

Why UKG’s CEO holds a weekly cockpit review of the top 10 transformation programs, measured at the business-outcome level

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