Five Barriers to AI Adoption for Community Banks
Our Take on How to Overcome Each Hurdle
TL;DR
Community banks know AI adoption matters. The demand signal is clear and getting louder every quarter. But knowing it matters and being able to act on it are two different problems. The OCC's recent Request for Information on core service provider relationships, combined with CCG Catalyst's analysis of the responses, identified five structural barriers that keep community banks from closing the AI gap. None of them are about willingness. All of them are about data access, infrastructure, and the realities of operating with a lean team inside a concentrated vendor ecosystem. Here's what those barriers look like in practice and what community bank operations leaders can do about each one.
The Context: A Widening Gap That Isn't Going Away
In late 2025, the OCC published a Request for Information on community banks' engagement with core service providers and other essential third-party providers. The RFI stemmed from an earlier round of comments on community bank digitalization, where the OCC received 22 responses from banks, industry groups, and technology providers. The comments were remarkably consistent: banks described reduced bargaining power, difficulty integrating new technology with legacy platforms, and a growing gap between the capabilities available to large institutions and what community banks can actually access.
A Kansas City Fed research briefing cited in the OCC's RFI documented the market structure: three core service providers serve over 70% of depository institutions. That concentration shapes nearly every technology decision a community bank makes, including whether and how AI gets adopted.
CCG Catalyst's Paul Schaus published a nine-part analysis recently around the OCC RFI responses earlier this year. His third installment focused specifically on AI adoption for community banks and named five barriers that prevent meaningful progress. Those five barriers are worth examining closely, because each one has a practical workaround if you know where to look.
Barrier 1: Your Data Is Trapped Behind Proprietary Walls
AI tools need accessible, structured data to function. CFIs have data spread across a core banking system, loan origination system, CRM, compliance platforms, and any number of ancillary tools. Those systems use proprietary formats, incompatible APIs, and restrictive data policies that make extraction difficult even when the bank owns the data outright.
Schaus's analysis put it directly: the bank owns the data, but the provider controls the pipes. When the OCC published its RFI, responses from both banks and fintechs converged on the same diagnosis. As PYMNTS reported in February 2026, both sides called for standardized APIs, clearer data rights, and proportional supervision. One community bank described how a routine vendor integration turned into a multi-year, six-figure project because the core provider controlled the timeline and the pricing.
How to work around it: You can't wait for your core provider to open the pipes. Here's A few ways to start making progress now:
Audit what access you already have. Most cores offer scheduled file exports, SFTP drops, or report generation that banks underutilize. Map which systems can push data out, in what format, and on what schedule.
Document your most painful data flows before automating anything. If your team pulls from six systems to build one report, map it step by step. Usually two or three sources account for 80% of the pain. Narrowing scope makes any next step more manageable.
Consider overlay tools or services that sit alongside existing systems and extract data through whatever access points are available - secure file drops, email inboxes, even PDFs. These don't require your core provider's cooperation or development timeline.
Example: A COO at a $650M community bank needs to pull loan data from three systems for quarterly reporting. Right now, a staff member spends two days downloading, reformatting in Excel, and reconciling manually. A standardized export template could cut that time in half. An automated overlay that ingests from all three sources could compress it from days to hours.
Barrier 2: The Tools Aren't Built for Your Size
When core providers do offer AI capabilities, they're typically designed for enterprise-scale clients. The ABA's 2024 Core Platforms Survey, referenced in the OCC's RFI, reported overall provider satisfaction at just 3.19 out of 5, with innovation capabilities scoring even lower. Providers build products for their largest customers because that's where the revenue concentration is. A $500M institution doesn't have the IT staff to configure, validate, and maintain a platform designed for a $50B regional bank. Schaus noted that community banks rarely have in-house data science expertise, and providers that offer AI solutions frequently build them for enterprise-scale clients. Smaller banks are left with tools that are too complex or too expensive to use effectively, and insufficient support to bridge the gap.
How to work around it: Shift the question from "which AI tool should we buy" to "which operational outcome do we need." You might not need a enterprise grade document intelligence platform. You might just need loan packages classified and data extracted or ACH files matched against GL feeds by 10 AM.
Start with your highest-volume, lowest-judgment workflows. Loan document sorting, invoice matching, data entry from standardized forms. These are automation candidates regardless of which path you take.
Evaluate lightweight, modular tools before enterprise platforms. Several vendors offer focused solutions for specific tasks like document extraction or reconciliation with transparent pricing, short implementation timelines, and the ability to test on your actual data before committing.
For processes where you lack internal capacity to manage a tool, consider buying the outcome instead of the software. When you buy a tool, you own implementation, integration, and QA. When you buy an outcome, someone else owns that stack and you receive clean results.
Barrier 3: Your Team Can't Absorb Another Project
Community banks aren't going to hire data scientists. The operations team is already stretched across compliance, lending support, daily reconciliation, and exam prep. The OCC's earlier Digitalization RFI drew comments highlighting community banks' difficulties in attracting and retaining sufficient staff with the skills required for modernization projects, as well as budget limitations and concerns around managing existing technology debt.
This is the talent gap in community bank operations, and it's structural. The people qualified to evaluate, implement, and manage AI tools aren't looking for jobs at a $600M bank. And the existing staff, however capable, are already wearing three hats.
How to work around it: You don't need to hire a data scientist. You need to get smarter about where the time goes and who's best positioned to fix it.
Build AI literacy into your existing team. Free resources from the ICBA, your core provider's education programs, and vendor training can help ops staff understand what automation can and can't do. The person who knows the process best is the person who should evaluate whether it's a good automation candidate.
Designate an internal champion. One person in operations or compliance who owns evaluation of any automation initiative. Not a new hire - a responsibility shift for someone already close to the work.
For execution, decide whether you need to own the technology or just the outcome. Some workflows justify managing a tool internally. Others are better suited to an external service that handles the technology, process design, and human oversight as a package.
Example: A compliance officer at a $750M bank spends three weeks preparing for an exam, pulling data from five systems and reconciling it manually. One option: build a standardized internal checklist and template that cuts assembly time by a third. Another: move the data aggregation to an external service with audit-trail documentation built in. Same person, higher-value work, less operational risk.
Barrier 4: Your Provider's Roadmap Isn't Your Roadmap
CCG Catalyst documented a particularly telling case in its analysis: a community bank requested AI-enhanced credit decisioning from its core provider and was told the capability was "on the roadmap" with no committed timeline. Third-party alternatives were prohibitively expensive to integrate with the legacy core. The bank abandoned the initiative entirely, and fintech competitors captured the business.
This pattern repeats across the industry. The provider's innovation priorities are driven by their largest clients, their product strategy, and their own competitive pressures. A $600M community institution's wish list doesn't move the roadmap.
How to work around it: Stop treating every operational improvement as a core integration project. Not everything needs to run through your provider's stack.
Push your provider on what's already available. Core platforms often have features banks aren't using because they were never configured or trained on. Before assuming a capability doesn't exist, ask your account team what's available today for the specific use case.
Explore your provider's partner ecosystem. Most major cores have affiliated or certified third-party vendors with pre-integrated tools. Not a fix for everything, but often faster to deploy than building custom integrations from scratch.
For workflows that can't wait, bypass the core integration bottleneck entirely. Overlay approaches that extract data from system outputs (reports, file exports, documents) and deliver processed results back into your workflows don't require your provider's development resources or their timeline.
Barrier 5: Switching Isn't a Realistic Option
Contract terms, conversion costs, and operational risk make changing core providers a multi-year, high-stakes decision. CCG Catalyst's series on the OCC RFI documented the full scope of this problem: contract terms have lengthened to seven-to-ten year agreements, deconversion fees are designed to deter switching, and billing complexity means banks may not fully understand what they're paying for. The Kansas City Fed noted that many depository institutions still use legacy core systems up to 40 years old, coded in outdated programming languages.
Banks are effectively locked into their provider's pace of innovation. And when that pace is too slow for meaningful progress, the bank absorbs the consequences operationally.
How to work around it: Solving the AI adoption problem doesn't require solving the core provider problem. They're related but separable.
Negotiate harder on your existing contract. The OCC's RFI is generating regulatory attention on provider practices. That gives community banks more leverage in renewal conversations than they've had in years. Ask for data portability clauses, shorter renewal terms, and access to APIs that may already exist but aren't in your current agreement.
Join or form a peer consortium. Several industry groups and state banking associations are organizing collective bargaining around core provider contracts. Banks that negotiate individually against a provider serving 70% of the market have limited leverage. Banks that negotiate as a group have considerably more.
Separate your modernization strategy from your core strategy. Identify which operational improvements can be made without touching the core at all. A layer between your systems and your processes can handle the data work your core can't or won't do, while the larger provider relationship evolves on its own timeline.
Insist on proof before commitment. Rather than signing a multi-year contract based on roadmap promises, start with a scoped proof of concept on one workflow using real data. If it works, expand. If it doesn't, you've lost a few weeks, not years.
What's Blocking Progress?
Reading through the OCC RFI responses and CCG Catalyst's analysis, a pattern emerges. The barriers to AI adoption for community banks aren't really about AI at all. They're about data access, vendor concentration, staffing constraints, and an engagement model that assumes every bank has an enterprise technology team.
The banks that close the gap won't be the ones who wait for their core provider to innovate, or the ones who try to build internal AI capability from scratch. They'll be the ones who find a practical middle path: whether that's better leveraging existing tools, training their teams to identify automation candidates, joining peer consortiums for collective bargaining, or engaging outside partners who can work with messy data and prove value quickly. Usually it's some combination of all four.
Comptroller Gould said it directly when introducing the OCC's community bank reforms: regulatory burdens have cut the number of community banks in half over the last two decades. The operational burden of staying competitive in a technology-driven market is adding to that pressure. The banks that survive and grow will be the ones that find ways to modernize community bank operations without betting the institution on a single vendor's roadmap.
How Shore Solves Operational Challenges
Shore works with community financial institutions to identify the back-office bottlenecks that consume the most staff time, then automates or staffs around them so your team can get back to being bankers again. We deliver audit-ready results backed by SLAs, whether through fully automated AI-powered workflows, elastic staffing to fill capacity gaps, or a combination of both with human-in-the-loop quality assurance built in.
Our Outcome-as-a-Service model starts with a scoped operational assessment of your highest-friction processes. We prove the value on your actual data and only scale when you're satisfied with the results. If back-office efficiency and process automation is a priority for your board or strategic objectives, we'd welcome the chance to learn about your bank's operational challenges.
FAQ
Where should a community bank start with AI?
Start with back-office operations, not customer-facing tools. Identify the highest-volume, lowest-judgment workflows your team performs repeatedly: loan document sorting, daily reconciliation, data entry from standardized forms, or exam prep data gathering. These deliver the fastest, most measurable returns and carry the least regulatory complexity.
Can a community bank use AI without replacing its core banking system?
Yes. Overlay approaches work alongside your existing core, extracting data through file exports, SFTP drops, or document-level processing and delivering results back into your current workflows. You don't need your core provider to build an API or approve an integration to get started.
How much does AI automation cost for a community bank?
It depends on the approach. Lightweight modular tools for specific tasks like document extraction can start in the low five figures annually. Outcome-based managed services price by volume and workflow rather than software licenses. The more relevant question is how much your current manual process costs in staff hours, error rates, and exam risk, and whether the automation pays for itself within the first quarter.
What does the OCC say about community banks and AI?
The OCC's 2025 Request for Information on core service providers drew responses highlighting that community banks face significant barriers to AI adoption, including proprietary data formats, vendor dependency, talent gaps, and enterprise-priced tools that don't fit smaller institutions. The OCC has signaled support for proportionate risk management guidance and is revising model risk management expectations to ensure AI doesn't become exclusive to banks with large internal teams.
Do regulators require human oversight of AI in banking operations?
Regulators expect it, particularly for compliance-sensitive workflows like BSA/AML, KYC, and loan review. Any automation of these processes should include a documented human checkpoint where qualified reviewers handle exceptions. Full autonomy without human oversight is not viable in the current regulatory environment, regardless of how accurate the technology is.
How do I get my board comfortable with AI at our bank?
Start with a single, low-risk use case that delivers measurable results in 90 days or less. Boards respond to evidence, not strategy decks. A successful pilot on one workflow, with documented time savings, accuracy improvements, and audit-trail compliance, builds the case for expanding to additional processes far more effectively than a presentation about AI's potential.