From AI Pilot Purgatory To Real ROI With Bill Briggs Of Deloitte

Feb 16, 2026

Bill Briggs, CTO at Deloitte with nearly 30 years turning tech into business value. He explains why AI pilots stall, the danger of layering AI on broken workflows, and how to build an innovation flywheel that puts AI into teams' hands. He also covers governance for agentic systems, prioritizing technical debt, and practical infrastructure myths, all with down-to-earth examples and a human touch.

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ANECDOTE

Thirty-Year Deloitte Journey

Bill Briggs started at Deloitte as a fresh computer engineering graduate and stayed 30 years.
He now oversees emerging tech, investments, and talent as Deloitte's CTO.

INSIGHT

AI Can Weaponize Inefficiency

AI layered onto broken workflows amplifies inefficiency rather than fixing it.
Bill Briggs warns this can increase complexity and compute costs, causing disillusionment.

ADVICE

Democratize AI, Don’t Isolate It

Give AI tools to broad teams and embed innovation into everyday roles rather than siloing it.
Empower frontline staff to contribute so trust and practical knowledge scale across the organisation.

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In this episode, I'm joined by Bill Briggs, CTO at Deloitte, for a straight-talking conversation about why so many organizations get stuck in what he calls "pilot purgatory," and what it takes to move from impressive demos to measurable outcomes.

Bill has spent nearly three decades helping leaders translate the "what" of new technology into the "so what," and the "now what," and he brings that lens to everything from GenAI to agentic systems, core modernization, and the messy reality of technical debt.

We start with a moment of real-world context, Bill calling in from San Francisco with Super Bowl week chaos nearby, and the funny way Waymo selfies quickly turn into "oh, another Waymo" once the novelty fades. That same pattern shows up in enterprise tech, where shiny tools can grab attention fast, while the harder work, data foundations, APIs, governance, and process redesign, gets pushed to the side.

Bill breaks down why layering AI on top of old workflows can backfire, including the idea that you can "weaponize inefficiency" and end up paying for it twice, once in complexity and again in compute costs. From there, we get into his "innovation flywheel" view, where progress depends on getting AI into the hands of everyday teams, building trust beyond the C-suite, and embedding guardrails into engineering pipelines so safety and discipline do not rely on wishful thinking.

We also dig into technical debt with a framing I suspect will stick with a lot of listeners. Bill explains three types, malfeasance, misfeasance, and non-feasance, and why most debt comes from understandable trade-offs, not bad intent. It leads into a practical discussion on how to prioritize modernization without falling for simplistic "cloud good, mainframe bad" narratives.

We finish with a myth-busting riff on infrastructure choices, a quick look at what he sees coming next in physical AI and robotics, and a human ending that somehow lands on Beach Boys songs and pinball machines, because tech leadership is still leadership, and leaders are still people.

So after hearing Bill's take, where do you think your organization is right now, measurable outcomes, success theater, or somewhere in between, and what would you change first, and please share your thoughts?

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