Keeping pace with the AI boom: A governance playbook for midmarket banks

Talking points

• As artificial intelligence (AI) accelerates in midmarket banks, governance and controls must evolve to unlock AI’s full potential.
• Banks can add on to existing governance frameworks, but they need AI-specific monitoring, accountability, and controls.
• Banks that focus on governance—not just compliance—will be positioned to scale AI safely.

Midmarket banks are on the verge of an AI revolution. As AI moves from pilots to day-to-day banking, access is quickly expanding: 60% of professionals now have generative AI (GenAI) and agentic AI tools, up 50% from last year, according to Deloitte’s State of AI in the Enterprise survey.¹ AI use cases are spreading rapidly across midmarket banks, from modernizing back-office systems to enhancing customer service, sales, and marketing.

These AI banking applications have delivered efficiency and productivity gains. But, while cost reduction matters, efficiency is becoming table stakes. The bigger opportunity is differentiation—using AI to drive growth, improve customer experience, and strengthen competitiveness.² Yet those prospects will remain limited and risky without AI governance. Ahead, we’ll explore AI and regulatory compliance for midmarket banks and how governance and controls can unlock AI’s potential.

The current state of AI risk management and governance

Let’s start with the elephant in the room: AI governance has fallen behind AI deployment.³ As with earlier innovation waves, AI adoption is moving faster than the guardrails around it. Our AI survey found that only 21% of organizations have a mature governance model for autonomous AI agents.⁴

Fortunately, that’s beginning to change. New AI governance frameworks, including COSO, ISO/IEC 42001, and the NIST AI Risk Management Framework, are emerging to help organizations build controls equal to AI’s speed, autonomy, and complexity. We’re also seeing more banks establish AI governance committees. 

Repurposing risk and governance frameworks

Banks have strong risk management and model governance capabilities, but those frameworks were designed for technologies that are easier to monitor than AI. AI introduces risks that these traditional control environments weren’t built to address: model drift, bias accumulation, confident but inaccurate outputs, and always-on systems that change rapidly. 

The good news: Banks do not need to start from scratch. Existing governance and enterprise risk management structures can provide the foundation, but they must evolve to support continuous monitoring, faster escalation, and AI-specific controls.

What effective AI governance requires

AI governance is more than an AI use policy. It demands clear standards for validation, monitoring, residual risk assessment, and alignment to the bank’s risk appetite and control environment. It also requires clear roles and collaboration across the business, risk and compliance, technology, legal, and internal audit functions.

When those groups align, governance is more likely to enable innovation than slow it down. The goal is not to create a parallel bureaucracy around AI, but to embed oversight into how use cases are designed, approved, deployed, and monitored.

Owning AI risk

Most banks rely on third-party AI and adapt it to their environments. But even when the technology comes from a vendor, accountability does not. Third-party AI risk management is still evolving, so vendor controls alone are not enough to manage reputational, regulatory, operational, and customer risk. AI risk remains the bank’s risk. The business should own use and outcomes, risk and compliance should provide oversight, and internal audit should deliver independent assurance.

AI governance fuels differentiation

Many banks are focused on AI’s efficiency. But its greater potential may lie in enabling stronger customer experiences, differentiation, and growth. Examples from the Deloitte AI Institute’s Financial Services AI Dossier illustrate the point:⁵

• Hyper-personalized sales and marketing: Sales and marketing teams can create campaigns that use AI agents to provide highly personalized, regulatory-compliant sales and marketing content at scale.
• Virtual customer service assistant: Powered by AI, these assistants can cater to banking customers’ everyday needs. They can quickly provide a wealth of information, improving customer experience, increasing engagement with products and services, and efficiently escalating requests to the right human support teams. 
• GenAI-enabled virtual banks: Conversational GenAI in immersive virtual reality environments can make banking interactions more convenient and tailored, while extending service capacity of human staff.

What do these use cases have in common? They are customer-facing, data-intensive, and high risk. To scale them safely, banks need strong governance, controls, and accountability—especially around compliance, model behavior, and data security. Organizations that treat governance as a strategic capability will be better positioned to move AI from pilot to production and capture value.

Next steps

Drawing on Deloitte’s governance experience and methodology, these practical steps can help midmarket banks strengthen their AI governance models:

• Consult with advisers and push for information to lay the groundwork for a clear AI governance framework that includes the policies you need. 
• Update existing governance frameworks to address AI.
• Establish cross-functional AI governance with defined roles, accountability, controls, and escalation protocols.
• Maintain an AI inventory that maps in-scope use cases to business processes, assertions, and controls.
• Review AI use cases before deployment.
• Identify AI-specific risks and design controls proactively.
• Assume ownership of AI risk, regardless of vendor involvement.

Deloitte’s Audit & Assurance AI leadership

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What role can Deloitte play?

Deloitte can advise midmarket banks on navigating the challenges of fast-moving AI. We offer a wide range of AI-related services, from education and workforce upskilling in our AI academies to AI risk management, governance, control design, monitoring, and testing. To learn more, download our State of AI in the Enterprise report, visit our services page, contact your Deloitte representative, or reach out to us directly. 

Endnotes

1. Jim Rowan et al., State of AI in the Enterprise: The untapped edge, Deloitte, January 2026.

2. Ibid.

3. Ibid.

4. Ibid.

5. Deloitte, The Financial Services AI Dossier, Deloitte AI Institute™, December 2025. 

The services described herein are illustrative in nature and are intended to demonstrate our experience and capabilities in these areas; however, due to independence restrictions that may apply to audit clients (including affiliates) of Deloitte & Touche LLP, we may be unable to provide certain services based on individual facts and circumstances.

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