Scaling AI: Containers, Hybrid Infrastructure, and the Governance Gap
Financial services institutions are embedding AI into core operations at pace. But the gap between ambition and execution is widening.
The 8th annual Nutanix Financial Services surveyed IT executives across the sector to understand how institutions are deploying AI, where containers fit into the infrastructure picture, and what's standing in the way of enterprise-wide scale.
Inside, you'll find data on how institutions are navigating the governance gap and the technical friction of running modern, cloud-native workloads across hybrid environments.
Building a Resilient Financial Services Infrastructure:
Digital Operational Resilience Act (DORA) and Beyond
Engineering the AI Trusted Stack for Financial Services
How Financial Institutions Can Move from Pilot Purgatory to Enterprise Production AI Deployment
The Hybrid Multicloud Playbook for Financial Services
Digitally Transforming Operations, Business Growth and Innovation
Many organizations struggle to move from early pilots to production because of fragmented data, limited GPU access, siloed teams, and complex infrastructure decisions. These challenges can make it difficult to operationalize AI reliably across cloud, edge and on‑premises environments.
AI is acting as a primary catalyst for a container-first infrastructure shift, prompting institutions to accelerate the adoption of portable container environments to securely run and scale modern AI workloads across hybrid ecosystems.
Sovereignty Debt is the operational compromise made when financial institutions deploy container and AI workloads in the public cloud for immediate speed, despite having strict long-term data compliance needs, creating future compliance liabilities that must eventually be resolved by migrating those applications to on-premises or private cloud environments.
Successfully scaling AI is as much an organizational hurdle as a technical one, primarily limited by rigid process governance, internal cultural silos, workforce talent gaps, and legacy infrastructure readiness limitations.
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