In this episode of The Bridgecast, host Scott Kinka welcomes Reggie Townsend, Vice President of AI Ethics, Governance, and Social Impact at SAS, for a wide-ranging conversation about what it truly means to build AI that organizations — and society — can trust.

Reggie brings something rare to the AI ethics conversation: a commercial backbone. After years at Motorola, IBM, and Sun Microsystems before joining SAS eleven years ago, he approaches responsible AI not as an academic exercise but as a practical business imperative. His core thesis? Doing well and doing good are not mutually exclusive — and organizations that treat ethics as purely defensive are leaving enormous strategic value on the table.

Reggie Townsend is the Vice President of AI Ethics, Governance, and Social Impact at SAS, a data and AI company with 50 years in business. He previously served on the National AI Advisory Committee, advising the U.S. Department of Commerce, and currently sits on the board of Equal AI. With a career spanning Motorola, IBM, and Sun Microsystems before SAS, Reggie brings a practically grounded lens to AI ethics — one that insists organizations can simultaneously drive profit and protect people. He leads teams across ethics, governance, regulatory strategy, accessibility, and social impact at one of the world's most quietly consequential AI companies.

At SAS, the term isn't "responsible AI" — it's "trustworthy AI." And Reggie explains why that distinction matters far more than it first appears. "No one is going to sign up for irresponsible," he says — which means the term has become politically loaded before the real conversation even starts, entangled in doomer narratives and Luddite debates. Trustworthy AI, by contrast, cuts straight to the question that actually matters: will people invest their trust in this technology? This framing is backed by governments worldwide — the EU, the UN, and both recent U.S. administrations have all gravitated toward it. More importantly, it reframes the business conversation entirely. Trust isn't a constraint on AI deployment. It's the precondition for adoption at scale. Organizations that are still fighting over whether their AI is "responsible" may be having the wrong conversation entirely.

One of the sharpest observations in this episode concerns what happens when organizations race to maximize AI-driven output at the expense of building their talent pipeline. Reggie warns that senior employees who become heavy AI users can now produce at levels that make junior hiring seem unnecessary — but then asks, "What happens when those senior people leave?" The institutional knowledge disappears. And there's no pipeline to replace it. He extends this to the macro level: if GDP is just a measure of output, AI agents producing more may look like a win. But if the people who used to produce are left behind, "we will have missed the boat by a long shot." The implication for leaders is direct: AI workforce strategy isn't just about productivity gains. It's about what kind of organization you intend to be five to ten years from now — and whether you'll have the human foundation to sustain it.

When businesses ask what good AI governance looks like in practice, Reggie doesn't point to a policy template. He points to SAS's "quad" framework: Culture, Operations, Regulations, and Oversight. Culture asks how normative behaviors in the organization need to shift to absorb AI disruption — and what the psychological impact on employees will be. Operations addresses workflows and how work will actually get done going forward. Regulations covers both compliance obligations and aspirational standards in a currently fragmented global landscape. Oversight asks where accountability sits, who monitors AI decisions, and how fast the organization should allow change to move. His advice for getting started: "Don't go try to solve for cancer." Instead, identify repeatable, laborious tasks — what he calls "pearls" — AI-ify those first, build early wins, develop the organizational muscle, and then expand. It's a framework that respects both business reality and human complexity, and it's one any organization can start using today.