Reggie Townsend: Making Responsible AI Irresistible

Responsible AI will not scale because leaders believe in it. It will scale when doing the right thing becomes easier than working around it.

In this episode of Scouting for Growth, Sabine VanderLinden welcomes back Reggie Townsend, Vice President of AI Ethics, Governance and Social Impact at SAS, for a timely conversation on how to make responsible AI irresistible inside the enterprise.

This is not a theoretical discussion about principles. It is about what happens when AI governance meets real deadlines, board accountability, product design, agentic AI, regulatory pressure and enterprise adoption.

The timing matters. The EU AI Act has moved from future regulation to implementation reality. It entered into force on 1 August 2024, with prohibited AI practices and AI literacy obligations applying from February 2025, general-purpose AI rules from August 2025, and phased timelines for high-risk AI systems now shaped by the 2026 AI omnibus political agreement. Waiting for perfect certainty is no longer a strategy.

Reggie challenges a common misconception: AI governance is not a brake on innovation. Done well, it is the architecture that allows innovation to move faster, with confidence.

The episode explores five major shifts shaping responsible AI in regulated industries:

From principles to practice: AI ethics must be designed into workflows, not attached after decisions have already been made.

From model governance to use-case governance: Auditing models alone is not enough. Real AI risk often emerges from context, deployment, decision impact and human behaviour.

From compliance cost to growth driver: Trust can accelerate adoption, reduce hesitation and unlock value from AI systems that teams actually feel confident using.

From human-in-the-loop to human judgement at scale: Agentic AI forces leaders to ask whether humans remain meaningfully accountable when thousands of automated actions happen at speed.

From board oversight to board fluency: Directors need more than AI policy awareness. They need visibility into use cases, ownership, controls, performance, risk and value creation.

A central theme is the AI trust deficit. When organisations lack confidence in how AI is governed, pilots stall, procurement slows, approvals become risk-averse and teams are left unsure when AI is safe enough to scale.