Five moves banks can make to accelerate AI from pilot to production

Here are five practical moves that help financial institutions turn AI from an experiment into an operational capability.

Originally published in BankDirector
Dan O’Malley
CEO, Engageware

The banking industry’s artificial intelligence (AI) problem isn’t that it doesn’t work. It’s that banks don’t know how to make it work safely, consistently and at scale. This explains why financial institutions have a growing collection of AI proofs of concept but little measurable adoption. The obstacle is not technology, it’s trust: employees hesitate to rely on AI outputs, customers demand reliable answers quickly across any channel and risk teams won’t approve systems they cannot explain. In banking, move fast and break things simply doesn’t work. 

Regulators aren’t waiting on the sidelines. Supervisory guidance around model risk management now explicitly requires explainability, auditability and clear oversight. For boards, the conversation has shifted from, “Should we experiment with AI?” to “How do we ensure our AI operates within established governance frameworks?” 

Below are five practical moves institutions can make to move AI from pilot to scale. 

1. Start with low-risk, high-frequency workflows.

AI succeeds fastest where risk is manageable and repetition is high — internal policy lookups, employee onboarding questions, product FAQs and routine service guidance. 

Consider a branch employee fielding a question about certificate of deposit early withdrawal penalties. Instead of searching documents, the employee queries the AI and receives an immediate, cited response in 15 seconds instead of 5 minutes. 

Many institutions begin here because they allow measurable productivity gains without immediate customer-facing risk. The most effective approach creates a single, governed source of institutional knowledge serving all channels, ensuring consistency when policies change. When these changes happen, updates propagate everywhere simultaneously, ensuring consistency across AI agents, call centers, websites and branches. 

Boards should ask management to define an AI on-ramp, or a short list of workflows that are frequent, measurable and reversible. 

2. Keep humans in the loop, but design it carefully and clearly.

Human in the loop only works if escalation paths are clearly defined. Effective rollouts include structured workflows. AI proposes an answer with source attribution, and the system escalates to a human agent when the situation requires judgment or the user requests assistance. Require clear escalation rules so employees know when to escalate, but don’t allow AI answers to reach customers without defined fallbacks. 

3. Make compliance visible, not implied.

In banking, compliance must be observable. Risk and audit teams will ask questions. Where did the answer come from? Which policy version was used? Can the response be reproduced? 

Many institutions ground AI in governed institutional knowledge rather than open-ended generation. This aligns with supervisory expectations around model risk management and reduces channel drift when different channels provide inconsistent answers to the same question. Directors should insist on traceability, auditability and controlled content. 

4. Make the value visible to drive adoption.

Staff adoption increases when employees experience improvement firsthand, like searching a policy and receiving a cited answer in seconds rather than minutes. Make adoption measurable by tracking: 

• Usage by role and location.
• Common questions and information gaps.
• Service metrics before and after implementation. 

Boards need dashboards measuring operational outcomes not technical activity. 

5. Treat AI as an operating capability.

Sustainable AI programs operate as ongoing capabilities with clear ownership and defined roles: 

• A business owner accountable for service outcomes. 
• A risk owner responsible for oversight and compliance. 
• A content owner responsible for maintaining accurate institutional knowledge. 
• A learning owner responsible for training and adoption. 

Questions Directors Increasingly Ask About AI

When AI initiatives come before the board, be prepared to answer these questions: 

1. Which customer or employee workflows will improve in the next 90 days, and how will success be measured?
2. What controls ensure AI responses are explainable and auditable?
3. Who owns the institutional knowledge that AI draws from, and how do we ensure it stays current and consistent across all channels?
4. What escalation process exists when AI cannot confidently answer a question?
5. How is the bank managing model and vendor risk as AI capabilities evolve?

The question for bank boards in 2026 is not whether AI belongs in their operations — that debate is over. The question is whether their institution will deploy AI responsibly at scale or remain stuck running pilots while competitors pull ahead. The five moves outlined here separate the leaders from the laggards. 

Sources

• U.S. Treasury, Artificial Intelligence in Financial Services – https://home.treasury.gov/system/files/136/Artificial-Intelligence-in-Financial-Services.pdf
• National Institute of Standards and Technology, AI Risk Management Framework – https://www.nist.gov/itl/ai-risk-management-framework
• Federal Reserve, SR 11-7 Model Risk Management Guidance – https://www.federalreserve.gov/supervisionreg/srletters/sr1107.htm
• FDIC Risk Management Manual: Model Risk Management – https://www.fdic.gov/risk-management-manual-examination-policies/rc-mrm.pdf 
• U.S. Government Accountability Office, Artificial Intelligence: Use and Oversight in Financial Services – https://www.gao.gov/assets/gao-25-107197.pdf