Operating AI in Production

How financial services organizations see, trust, and stay accountable for AI at scale.

AI in financial services doesn't stop being a risk once it reaches production.

In fact, that's where the real challenge begins.

A model can drift without generating an error. An AI agent can make a confident but incorrect decision. A workflow can execute exactly as designed - and still produce the wrong business outcome.

By the time those issues become visible, the impact may already have reached a customer, a trader, a business process or a regulator.

For financial institutions "it's running" is no longer enough.

Join Dynatrace, AWS, and industry leaders for a private virtual roundtable exploring what it takes to operate AI with the same visibility, accountability and control expected of other critical systems.

In this Executive Conversation We'll Explore:

• AI accountability at scale - How organizations are approaching performance commitments and SLA accountability at the model and tenant level.
• Observability for autonomous agents - How teams can detect, investigate and respond when AI agents behave unexpectedly across high-stakes workflows.
• From model performance to business impact - Connecting technical AI behavior to customer outcomes, operational performance, risk, and compliance.
• Ownership and governance - Who is accountable for AI once it is deployed - and whether technology, risk, compliance and business leaders have the visibility they need to act.
• Operating AI with confidence - Moving beyond monitoring infrastructure to understanding what AI did, why it did it, and what it meant for the business.

The Questions Shaping the Discussion

1. How accountable is your AI? 

Financial institutions are increasingly expected to guarantee AI performance at the model and tenant level, yet many organizations lack a clear mechanism for monitoring or enforcing those commitments.

How are you approaching SLA accountability for AI in production - and where are the gaps?

2. What happens when an agent gets it wrong?

Autonomous agents can fail silently, particularly when they operate across complex, interconnected workflows.

How are you extending observability into agent-based systems, and what happens operationally when an agent behaves unexpectedly?

3. Who owns AI once it goes live?

AI accountability sits at the intersection of technology, risk, compliance and business leadership.

Who owns AI performance and conduct after deployment - and do they have the real-time visibility needed to act?

Join the Conversation

This is a peer-level discussion for financial services leaders navigating the operational, risk and governance challenges of AI at scale.

Come prepared to share how your organization is approaching AI accountability - and hear how others are tackling the same challenges.

Register your interest below.