Banks are increasing AI investment year over year, with programs moving from early pilots into enterprise operations ranging from back office efficiency through to customer facing front end innovations. Amidst this flurry, most institutions are budgeting mainly against what vendors charge, but those charges represent only 10% to 20% of what AI programs cost once they are running at scale. The engineering work, compliance reviews, human oversight, and exception handling that sit underneath account for the rest. Understanding these hidden costs is critical to build a credible business case, finding the right ROI and SLA metrics, and building the functionality that helps both your organization and your customers.
Join Datos Insights Executive Advisor Gilles Ubaghs to break down the true economics of enterprise AI:
- The hidden cost structure: Why vendor charges represent only 10% to 20% of AI program costs, and where the remaining investment goes
- Business metrics that matter: How to measure AI performance using true operational KPIs (cost per transaction processed, man-hours saved in manual tasks,cost per customer onboarded) instead of technical metrics that don’t resonate with business leaders.
- Building an AI business case: A framework for calculating total program cost, setting realistic expectations within your FI that align with real pain points and opportunities, and not another pie-in the sky proof-of-concept that fails once it hits market reality.
- Client and market readiness: How today’s FI customers and market leaders are already using the latest AI tools to improve their banking and payments functions, and what lessons can be learned when planning , budgeting and implementing your AI initiatives.
