AI is one of the most talked-about subjects in banking, and it's driving one of the biggest transformations the financial sector has seen in a decade. But as more banks and credit unions move past the experimentation stage, the real challenge is making agentic AI actually work in production and reach the bottom line.
Moving from pilot to production is just one piece of the puzzle. There are five top challenges banks and credit union leaders face when it comes to AI.
How much autonomy should an AI have, and where should you draw the line?
How much governance is enough without slowing progress down?
Should accountability for AI workload always sit with a human? How does this look in practice?
How do you retain customer trust when AI is moving their money?
What does AI success look like? How do you prove ROI?
On AI accountabilityThe senior-most person in the organization who's responsible for that use case in production, they have to accept any residual risk that comes from that use case.
Prag Sharma Director, Future of Finance, Citi
of financial institutions already have agentic AI live in some form, from limited internal use to full production at scale.
of AI projects still fail to deliver lasting value, and the gap almost always comes down to the operating model built around the AI.
of industry leaders would let an AI agent move money from their own account with no human involved at all.
of AI value being reported today comes from operational efficiency, more than double what's landing in employee productivity, and over 15 times what's landing in new revenue.
Hear how banking leaders are approaching agentic AI, from autonomy to accountability.
Building AI models that can scale is costly, and proving that they can be economically sustainable is a budget pressure challenging C-suite leaders across the banking world.
Justifying the use of AI at scale while balancing risk is becoming a critical part of the business case. Infrastructure and compute costs, data quality, model monitoring, third-party dependencies and regulatory challenges can all affect the return on investment. At the same time, banks need clear accountability for AI-driven decisions: who owns the model, who can challenge its output, and where should human intervention remain part of the process?
Having the answers to these questions before deploying AI at scale, while building governance into the strategy from the outset, will be essential to sustainable adoption.
The AI Frontier theme at Banking Transformation Summit USA covers what comes next for AI in banking, from autonomous systems and AI-powered decision-making to governance, fraud prevention and compliance.
The banking agenda sessions will focus on moving GenAI beyond the pilot stage, with case studies showcasing key learnings from the banks and credit unions that have implemented the technology successfully.
On how to get it rightEarly 2024, it would be about a minute where an AI agent could work until somebody would have to intervene and correct it. Now we're talking about hours, maybe fifteen hours. You delegate work overnight and when you wake up, the work is completed.
Yannis Psorakis Cognition AI
Join the banks and credit unions moving AI past the pilot stage at Banking Transformation Summit USA.