AI has moved beyond experimentation in financial services, particularly across trading, fraud detection, risk management and compliance. But while many firms can run successful pilots, turning them into reliable, large-scale operations is proving much harder.

The challenge is often structural. Real-world AI use cases have very different requirements around data, latency, governance and infrastructure. As workloads scale, environments built for individual pilots can struggle with the demands of larger-scale deployment.

For financial services leaders, the question is whether to continue with isolated pilots or build a platform strategy designed for scale. A more consistent foundation can support AI across teams and regions, while meeting the speed, governance and data residency requirements the sector demands.

In this executive briefing, you'll explore:

Why scaling AI in regulated financial services environments is proving difficult
How fragmented infrastructure, governance and data strategies can make AI harder to scale
Why firms are moving from piecemeal experimentation to platform-led AI strategies
How private AI environments can support sensitive and regulated workloads
The four elements of a disciplined AI strategy: trusted data, high-performance infrastructure, geographic proximity and sovereignty, and flexible environments that can scale with demand
How Digital Realty and Options can support the move from isolated pilots to production-grade AI deployment

Atlanta: Building what’s next