Webinar Highlights: How to Scale Without Risk – The Responsible AI Playbook

Artificial intelligence is rapidly moving from experimentation to enterprise-wide deployment. For regulated financial institutions, the challenge is no longer whether to use AI, but how to scale it responsibly. Fenergo’s How to Scale Without Risk: The Responsible AI Playbook webinar brought together experts to examine how organisations can embed governance, strengthen oversight and build trust as AI adoption accelerates. The discussion explored regulatory momentum, operational accountability, model oversight and the structural controls required to ensure innovation remains defensible. 

Governance Must Be Embedded from the Start 

Emmett Carlin, VP of Privacy and Risk at Fenergo, opened by grounding the conversation in practical implementation. He explained that his focus is on ensuring that “governance is embedded by default and that's whether we're embedding AI into the platform for clients or deploying it across the organization.” He reinforced that governance frameworks must be workable in real-world environments, stating, “For me, governance has to be practical and scalable.” 

Positioning governance as an enabler of responsible AI adoption rather than a constraint, Carlin addressed the tension many organisations face when scaling emerging technologies. He emphasised that effective oversight should create the conditions for confident deployment. This balance between control and progress remained central throughout the discussion. 

From Adoption to Accountability 

Mark Kettles, Director of Product Marketing at Fenergo, highlighted how AI is becomes more deeply integrated into regulated workflows. He observed:

“As AI becomes more embedded in regulated workflows, the challenge isn't whether to adopt it, but how to do so in a way that is transparent and trusted by regulators.” He added, “That's where AI governance moves from theory to practice.” 

The conversation made clear that transparency, documentation and oversight are no longer optional enhancements. They are structural requirements for sustainable AI deployment. 

Regulators Are Moving Quickly 

Lee Bates, Partner and Global Risk AI Leader at PwC, addressed the accelerating pace of regulatory change. With frameworks such as the EU AI Act introducing defined risk categories and obligations, institutions must understand how their AI use cases are classified and what controls are required. As Bates explained, “High risk AI requires governance, explainability and control. The panel cautioned against approaching governance purely as a compliance formality. 

Bates warned that treating “AI governance as a policy exercise risk slowing innovation.” Instead, controls must be operationalised directly within systems and processes to enable confident scaling. 

Trust Is Earned Through Process 

Eric Alter, AI Realist and Founder of EAAI Consulting, brought a practitioner’s perspective to the discussion, focusing on credibility and operational discipline. He stated, “You don't get trusted outcomes without trusted processes.” 

This sentiment resonated across the panel. Trust with regulators, customers and internal stakeholders depends on evidence including clear documentation, risk-tiering, and ongoing monitoring. Bates reinforced this point by urging organisations to “build trust with regulators and stakeholders.” Trust is not achieved through intention; it is demonstrated through defensible governance. 

Risk Tiering and Continuous Monitoring 

The discussion also highlighted the importance of risk classification. Not all AI systems carry the same exposure, and governance must be proportionate. Continuous monitoring protects against model drift, performance degradation and unintended bias. Without it, even well-designed systems can become vulnerable over time.

The conversation also returned to the importance of documentation and visibility. Organisations must understand where AI is deployed, what data it relies on and how outputs are validated. 

Governance Built into the Operating Model 

Throughout the session, the panel emphasised that AI governance must be systemic rather than reactive. 

Bates summarised this forward-looking approach, advising organisations to “Have governance built in by default.” Embedding governance into development lifecycles, approval processes and oversight structures ensures that risk management evolves alongside innovation. 

Mark Kettles framed the broader challenge facing organisations today, explaining, “At its core, our mission is about enabling growth without compromising control, which is exactly the tension many organizations face with AI today.” Resolving that tension requires more than high-level principles. It demands cross-functional collaboration, clear accountability and integrated control frameworks. 

For a deeper dive into the regulatory insights and expert perspectives shared during the session, watch the ‘How to Scale Without Risk: The Responsible AI Playbook’ webinar on demand here.