AI Fast and Slow

Recently I had the pleasure of opening a panel discussion on "Governance and Risk Management for AI and ML Models" that featured three very experienced, senior executives from leading financial institutions: The discussion centered on the challenges wrought by the rapidly growing use of artificial intelligence (AI) across their enterprises. They shared some very interesting anecdotes and offered insights that speak to a core theme in any enterprise AI journey: Balancing the pressure to maximize value by rapidly deploying AI models with the necessity to limit the organization's risk exposure. For those who may not have the time to view the entire discussion – available here – I'd like to summarize a few highlights that I think are especially important. Before I start – If you're thinking that the panelists' observations are only relevant for financial services companies, hold on a bit. They made it clear from the outset of the discussion that much if not most of their risk management practices are driven by internal controls and business practices rather than by government or industry regulations.