Section 2 — Demo 2 · Step 1 of 5 · Implementation

The infrastructure beneath AI
is where most programmes silently fail.

The AI strategy is funded. The infrastructure to run it is not fit for purpose.

70%
Legacy IT budgets consumed by maintenance
70% of UK bank IT spend goes to maintaining legacy systems — leaving 30% for everything else, including AI.
18 mo.
Average pilot-to-production timeline
Nearly 3× longer than comparable deployments at tech-native firms. The bottleneck is almost never the model.
60%
AI proofs-of-concept never reach production
The pilot cemetery is well documented. Infrastructure fragmentation and unclear ownership are the most common causes of death.
The firms pulling ahead on AI aren't the ones with the best models — they're the ones with infrastructure that can actually run them at scale.

What follows is what purpose-built AI infrastructure looks like in practice.
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Section 2 — Demo 2 · Step 2 of 5

Nutanix — Enterprise AI Infrastructure Platform

The platform layer that lets financial institutions run AI at production scale — securely, on-premise or hybrid.

NUTANIX
Enterprise Cloud Platform
AI-Ready Infrastructure
for Financial Services
"Run any AI workload — on-premise, hybrid, or multi-cloud — with enterprise security and compliance built in."
Unified compute, storage and networking — simplified at scale
GPU workload orchestration for AI model training and inference
On-premise deployment — data sovereignty and regulatory compliance
5–10× faster deployment vs. traditional infrastructure
Integrated data security — encryption, RBAC, audit trail
Hybrid and multi-cloud flexibility without vendor lock-in
🚀
Speed to Production
AI workloads deployed in days, not months. Pre-validated reference architectures for financial services eliminate the integration cliff between pilot and production.
🔒
Data Sovereignty
Full on-premise or private cloud deployment. Your data never leaves your infrastructure — meeting FCA, PRA, ECB, and EU AI Act requirements from day one.
📉
Cost Predictability
Eliminate the LLM API cost explosion at scale. Fixed infrastructure costs replace variable cloud spend — making the business case actually hold at volume.
🔗
No Rip-and-Replace
Layered on top of existing environments. Integrates with VMware, AWS, Azure, and on-premise data centres. No full migration required to get started.
▶ LIVE DEMO
Nutanix — AI Infrastructure in Practice
Enterprise AI Platform · Financial Services · On-Premise Deployment
AI workload deployment — end to end
On-premise infrastructure — data never leaves the institution
Pilot to production in days, not months
Built for financial services compliance requirements
Section 2 — Demo 2 · Step 4 of 5

What changes when the infrastructure is right

Measured outcomes across Nutanix financial services deployments.

5–10×
Faster deployment
AI workloads go from approved to running in days. Pre-validated financial services architecture eliminates the integration cliff that kills most pilots.
60–70%
Infrastructure cost reduction
Consolidation from fragmented legacy stacks to a unified platform. Fewer vendors, fewer failure points, predictable spend at AI scale.
100%
Data sovereignty maintained
Every AI workload runs within the institution's own perimeter. No data leaves to third-party cloud. FCA, PRA, and EU AI Act aligned from day one.
Section 2 — Closing Challenge
The infrastructure gap
is a strategic choice.

Most AI programmes stall not because the strategy is wrong — but because the platform beneath it cannot support production workloads at scale.

The institutions moving fastest have made infrastructure a first-order decision, not an afterthought.

A
What is your current infrastructure posture — can it run AI workloads at the volume your strategy requires, today?
B
Where does your data actually live — and have you stress-tested your sovereignty and compliance position under the EU AI Act?
C
Who owns the infrastructure decision for AI in your organisation — and are they in the room when the AI strategy is set?