Why Community Bank AI Projects Stall: Preparing for Risk, Compliance and Production

AI Adoption is Rising: Production is Still the problem

Wolf & Company's 2026 survey of community banks between $750 million and $25 billion in assets found that every institution surveyed had adopted AI in some form. Yet only 25 percent had moved their proofs of concept into production, and only 5 percent had launched a scaled, governed AI program. The sample was small, 20 respondents from 17 banks, but the pattern matches what larger studies report across industries.

Wolf & Company's 2026 survey of community banks between $750 million and $25 billion in assets found that every institution surveyed had adopted AI in some form. Yet, only 25 percent had moved their proofs of concept into production, and only 5 percent had launched a scaled, governed AI program. The sample was small, 20 respondents from 17 banks, but the pattern matches what larger studies report across industries.

Deloitte's 2026 State of AI in the Enterprise report found that only 25% of companies had moved 40% or more of their AI pilots into production. So, community banks are not alone struggling with AI implementation and AI adoption.

What is different is the environment in which they make the transition. A banking AI project does not simply need to demonstrate that a model works. Before it can become a production system, the bank has to be comfortable with how the technology handles data, how vendors are managed, how outputs are monitored, who is accountable, and what happens when something goes wrong. That is where many AI projects in banking begin to slow down.

The Real Barriers to AI in Banking Are Risk Barriers

When the surveyed community banks identified their biggest obstacles, the list looked less like a technology roadmap and more like a risk committee agenda. Security and data leakage were the leading concern at 55%. Limited internal expertise followed at 50%. Third-party vendor risk and data quality were each cited by 40%, while 35% identified a lack of governance or an operating model as a barrier.

These are not arguments against AI. They are the questions that every serious AI implementation in banking eventually has to answer. The problem is timing. Projects often encounter these questions late after the proof of concept has worked, after the business wants to scale it, and sometimes only when someone asks whether the system can use real customer or nonpublic information.

By then, fixing the underlying issues can mean redesigning the solution, revisiting vendors, changing access controls, or rebuilding documentation. A better approach is to treat AI risk management as part of the implementation from day one.

Internal Expertise Is a Risk Factor, Too

Half of the surveyed banks identified limited internal expertise as a barrier. For a community bank, this challenge is especially practical. A small technology team may already be responsible for core systems, cybersecurity, data management, vendor oversight, and day-to-day operations. Asking the same team to design an AI solution, establish its governance framework, document it for examiners, validate its performance, and support it in production can quickly become unrealistic.

That is not a failure of ambition. It is a capacity issue. And effective AI adoption in financial services requires acknowledging that capacity early.

The Regulatory Questions Are Already Taking Shape

For institutions regulated in New York, the expectations around AI-related cybersecurity risks are already documented. In October 2024, the New York State Department of Financial Services issued guidance addressing cybersecurity risks arising from artificial intelligence. The guidance stated that it created no new requirements beyond the existing cybersecurity regulation, Part 500, but clarified how those requirements apply to AI.

The guidance includes areas such as risk assessments that account for deepfakes and other AI threats, third-party service-provider due diligence before access to systems or nonpublic information, multifactor authentication, training addressing AI-enabled social engineering, and appropriate disposal of nonpublic information. For banks exploring AI in community banking, the takeaway is important: cybersecurity and AI governance should not be treated as separate conversations.

The AI project has to fit into the institution's existing risk and security framework. Model risk management has also evolved. In April 2026, the Federal Reserve, OCC, and FDIC jointly issued revised model risk guidance, SR 26-2, superseding the SR 11-7 guidance that banks had worked under since 2011. The new guidance takes a risk-based approach tailored to an institution's model risk profile and states that it is most relevant to banking organizations with more than $30 billion in total assets.

For community banks, that does not mean the discipline can be ignored. It means the approach can be proportionate.

Instead of copying a large-bank model risk management program wholesale, a community bank can focus documentation, validation, monitoring, and oversight on the models and AI systems it actually uses.

Build for the Risk Review from Week One

A common mistake in AI implementation in banking section is treating risk review as a final approval stage. A better approach is to design the project around the questions risk, compliance, security, and business stakeholders are likely to ask from the beginning.

A production-ready banking AI project should establish several fundamentals early:

• A clear data map: Identify what customer, financial, and non public information the system touches, where that information is stored, and who has access to it.
• Third-party vendor due diligence: Document the risks associated with every external model, platform, or service before production data is shared.
• Human review points: Define where employees must approve, correct, or override AI-generated outputs, particularly when the output could affect a customer, loan, or compliance filing.
• AI monitoring: Establish how accuracy, errors, drift, and performance will be monitored after deployment and who is responsible for responding.
• Defined stop criteria: Agree with risk and compliance on the conditions under which an AI system should be paused, restricted, or rolled back.

These controls do not have to slow a project down. Done correctly, they prevent the much more expensive scenario in which a successful AI proof of concept reaches the final review stage and then must go back to the drawing board.

AI Governance Is a Team Sport

Technology alone cannot solve the governance challenge. The strongest AI governance frameworks for banks bring the right stakeholders into the process early. That includes risk, compliance, information security, technology, and the business owner responsible for the process being improved. Each stakeholder brings a different question.

Security wants to understand access and exposure. Compliance needs to understand controls and regulatory implications. Risk needs to understand failure scenarios and accountability. The business owner needs to know whether the system improves the process.

Those conversations are far easier to resolve while the solution is being designed than after the technology has already been built. For community banks, this is one of the simplest ways to improve the path from AI proof of concept to production.

Start With Lower-Risk, Measurable Banking Use Cases

The same Wolf & Company survey provides a useful indication of where community banks are already focusing their AI efforts. Three-quarters of respondents were exploring or investing in internal process automation. Sixty percent were focused on data and reporting automation, compared with 35% for customer service and 20% for marketing and personalization. That distribution makes sense.

The best early AI use cases for community banks are often processes where the value is measurable, the workflow is already understood, and human oversight can remain firmly in place. Think reconciliations, exception handling, regulatory reporting preparation, document review, and other repetitive internal processes. These workflows offer several advantages.

The bank already knows what a good outcome looks like. The process usually has measurable inputs and outputs. Employees understand the exceptions. And the initial deployment can improve internal operations without immediately placing AI in the middle of a high-impact customer decision. That makes internal banking process automation a practical starting point for building confidence in AI.

From AI Pilot to Production Requires More Than a Working Model

A successful AI proof of concept answers one question:

Can this technology work? Production requires the bank to answer several more:

Can we secure it? Can we govern it? Can we monitor it? Can we explain how it works and who is accountable? Can we manage the vendor risk? Can we control the data? Can we safely operate it at scale?

That distinction is why AI adoption in banking should not be measured only by the number of pilots launched. The more meaningful measure is how many useful, governed AI systems reach production. For community banks, the opportunity is not to build the largest AI program. It is to build an AI program that can withstand the questions that come with operating in a regulated environment.

Maganti IT works with regional financial institutions across the Northeast to take selected processes from proof of concept to production in 30 to 45 days, with security, vendor risk, and human review designed into the implementation from the start. Maganti IT is ISO 27001 and SOC 2 certified, providing an established foundation for conversations around security and controls.