Webinar Replay | From Compliance to Competitive Edge: Turning AI Governance into Business Value
Artificial intelligence is transforming financial services—but not without risks. In our recent webinar, From Compliance to Competitive Edge: Turning AI Governance into Business Value, Alastair Gill (Data Scientist at GFT) and David Asermely (VP of Global Business Development & Growth Strategy at ValidMind) explored how financial institutions can move beyond regulatory obligation and use AI governance as a lever for innovation and business growth.
Watch the replay of their discussion below:
Here are the key insights from the discussion:
1. AI Governance Is Not Just About Compliance—It’s a Strategic Asset
Too often, governance is viewed as a burden. Both speakers emphasized the need to flip this mindset: effective governance is a way to unlock trust, accelerate adoption, and capture business value—not just satisfy regulators.
2. Accountability Must Be Clearly Defined
Successful governance starts with accountability. AI oversight cannot fall to a single team; it requires an organizational structure that clearly assigns responsibility, authority, and ownership for ensuring AI is deployed responsibly.
3. Small Teams Face Big Challenges
Many organizations place AI governance responsibilities on small risk or compliance teams. Without the right tools and processes, these teams cannot scale oversight across the growing number of AI initiatives. Investment in enablement is critical.
4. Organizational Alignment Is Essential
AI governance touches multiple stakeholders: model risk management, data governance, fraud, cybersecurity, compliance, and business teams. Bringing these groups together is both a challenge and an opportunity to create cross-functional alignment.
5. The “Slow, Slow, Fast” Adoption Curve
AI adoption often appears sluggish at first, but this period builds critical institutional knowledge. Once organizations establish frameworks, governance practices, and risk-tiering structures, they can leap forward rapidly in adoption.
6. Governance Should Start at the Use Case Stage
Too many AI projects fail after months of investment because governance was not considered early enough. Embedding compliance and risk assessments into use case identification and prioritization ensures that only viable projects reach production.
7. Risk Tiering Is a Foundational Practice
Consistently classifying AI systems into low-, medium-, and high-risk categories enables organizations to allocate oversight appropriately. Tiering not only helps mitigate risk but also accelerates innovation by allowing “safe-to-fail” experimentation in low-risk areas.
8. Regulation Is Complex and Evolving—Adaptability Is Key
Unlike traditional model risk standards such as SR 11-7, AI regulation is fragmented and rapidly evolving. Firms must build dynamic governance structures that can adapt as new requirements emerge across jurisdictions.
Final Thought: Start Small, Learn Fast, Scale Responsibly
Both Alastair and David agreed on a pragmatic approach: begin with clear definitions and accountability, build institutional knowledge through iterative governance, and then scale confidently. The goal is not just compliance, but responsible innovation that turns AI governance into a competitive edge.
