Protect AI Investments With Risk-Adjusted AI ROI Modeling

Join us for a practical discussion on risk-adjusted AI ROI: a methodology for bringing risk into the investment decision up front and evaluating the value that remains after the full cost of scaling AI is included. We’ll examine how to account for the costs traditional models miss, reduce duplicated effort and avoidable spend, improve utilization, and identify the conditions that help proven use cases move from pilot to production.

You’ll learn how to build a stronger AI business case, align Finance, Technology, and Risk on decision criteria, and use measurable evidence to determine when an initiative is ready to scale. This enables organizations to protect AI investments and increase the likelihood of sustained business outcomes.

Topics we’ll explore:

• The hidden costs AI business cases often miss, including governance, security, resilience, oversight, and operational controls
• The evidence organizations need to make informed AI investment and scale decisions, including measurable outcomes, operating assumptions, risk indicators, and control effectiveness
• How strong governance can improve the likelihood of AI project success, protect investment, and support responsible scale