AI centre of excellence bottlenecks: how should banks respond?

TL;DR: Bank AI centres of excellence often become bottlenecks as GenAI adoption outpaces oversight, leaving inconsistent guardrails, limited production visibility, and shadow AI risks across the portfolio, according to Lumenova AI. The durable fix is central observability and evaluation with self-service, use-case-specific controls.

Editorial analysis by NHI Mgmt Group, based on content published by Lumenova AI: “The AI Center of Excellence Bottleneck, and How Financial Institutions Can Avoid It”.

By the numbers:

• While roughly 32% had deployed AI/ML into production, only 12% described their AI/ML strategy as well-defined and resourced.
• Agentic AI was already active among 52% of financial services respondents.
• Only 5% of community and regional banks had launched a scaled, governed AI program.

Key questions

Q: What breaks when AI centres of excellence are forced to review every launch?

A: They stop scaling. Every launch becomes dependent on a small approval group, which slows delivery, encourages workarounds, and leaves the institution with fragmented visibility once teams bypass the process. The control failure is not the absence of policy. It is a review model that cannot keep pace with application growth.

Q: Why do central AI guardrails fail in production if they are not paired with observability?

A: Because guardrails only tell you what should happen, while observability tells you what actually happened.

Q: How can banks tell whether an AI CoE is working properly?

A: Look for short time-to-onboard, near-complete production inventory, active monitoring across applications, and a declining shadow AI footprint.

Practitioner guidance

• Centralise AI application inventory and observability Create a single, authoritative view of all production GenAI and agentic AI applications, including owners, last evaluation date, and monitoring status.
• Apply baseline guardrails automatically Use one governed baseline for safety, quality, and reliability controls, then allow per use case configuration only where the business need genuinely differs.
• Replace mandatory review gates for standard use cases Remove manual approval from low-risk launches once baseline controls and evaluation are in place, so the AI CoE can focus on higher-risk exceptions.

Bottom line: The central risk is not the existence of AI CoEs, but the tendency for them to become queues that the business routes around when adoption accelerates.

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