Artificial intelligence (AI) presents a similar leadership challenge for financial institutions. Success tapping into the technology’s potential depends on earning buy-in from people who evaluate it through very different lenses. Executives want a business case. Compliance leaders want governance. IT wants security and integration. End users want confidence that AI will help them do their jobs rather than replace them.
Here’s how financial institution leaders can build AI support among stakeholders so your bank or credit union can survive and thrive in a rapidly changing industry.
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How financial institutions build internal buy-in for artificial intelligence (AI)
July 14, 2026
0 min read
Making the case for using AI at banks and credit unions
Build buy-in by defining the business problem first, tailoring messages to each stakeholder group, emphasizing human oversight, and documenting governance, controls, and success metrics.
Tips for getting internal AI support
Inside financial institutions and other organizations, a major obstacle to innovation is often getting people with different responsibilities, incentives, and concerns to agree that the change is worth making.
Apple faced an alignment challenge while developing the first iPhone. Many inside and outside Apple questioned whether people would ever accept typing on a sheet of glass instead of a smartphone keyboard. Steve Jobs argued that a software keyboard could adapt to the user, whereas physical keys permanently constrained what the device could become. But turning that vision into a successful product required buy-in from engineers, carrier partners, software developers, and consumers. As a result, the touchscreen became the foundation for an entirely new generation of mobile computing.
Related webinar, “Defensible AI in financial services: Operationalize AI safely and effectively.”
Watch webinarBuy-in starts with the problem, not the technology
Abrigo Chief Technology and Product Officer Ravi Nemalikanti says that for banks and credit unions, preparing institutions and their people to build trust and use AI wisely has become a major focus.
Nemalikanti
“This conversation about AI has truly shifted from if we are going to use the technology to how well we are going to use the technology and how well we are going to adopt AI into our day-to-day workforce,” he said during a recent webinar on AI. “It’s ‘How do we create leverage and get that adoption to really get going?’ and ‘How do we build that trust and get our organizations to be ready to consume AI and AI capabilities?’”
He and other Abrigo experts say leaders looking to develop buy-in for AI should begin by clearly defining the business problem before advocating for AI. Rather than positioning AI as a broad transformation, begin by tying AI strategy to specific problems it might help solve.
“Try to think of one friction point in your day-to-day—one workflow, one bottleneck—and see how AI might be able to… make it a little bit smoother,” Nemalikanti said.
Look for places where employees spend time on repeatable work that can be described, controlled, and reviewed. Examples Abrigo product teams have heard from institutions include:
- Staff spend too much time creating or updating documentation manually
- Policies, procedures, or customer information are hard to find quickly
- Similar work is handled differently across teams or branches
- Analysts perform repetitive reviews that limit time for higher-value judgment-based tasks
- Teams must manage growing workloads without adding staff
AI buy-in is easier when stakeholders can see a low-risk, measurable first AI use case and the data and controls that will be applied.
How should you tailor the message to each stakeholder group?
Because AI can be adopted and scaled across the bank or credit union, leaders looking to encourage stakeholders to embrace a different way of solving business problems must frame their communications for each group. Each audience may have a different motivation and see different risks, so messaging should speak to their specific interests and questions.
Senior leadership: How does AI support business priorities?
Executives need to understand how AI supports the institution’s broader goals. Frame the discussion around practical outcomes such as improving efficiency, increasing consistency, and making better use of employee time.
Boards: How will we oversee and defend AI use?
Board members do not need a technical deep dive, but they do need confidence that AI use cases can be explained, monitored, and governed. Focus the conversation on transparency, accountability, human review, and how the institution will document and defend its approach.
Compliance and audit teams: How will we meet regulatory and internal controls?
Compliance and audit teams need to review AI plans early in the process—before decisions are made. These teams can help clarify expectations for documentation, data use, review processes, testing, and ongoing monitoring.
IT teams: What are the privacy and security implications?
Addressing AI-related information security and privacy concerns is foundational to creating buy-in for AI, just as it is when launching any new technology. Infosec teams need information about the model and the data that will be provided to it so that they can focus on security and data privacy protocols.
Department leaders/peers: How will this help my team now?
Department leaders can more easily support AI when they see how it can help their teams solve real problems. Ask where teams are losing time, duplicating effort, or struggling to keep up with volume. When possible, provide examples of other financial institutions’ experience using AI to make the opportunity more concrete.
Frontline employees: Will this replace my job?
Be clear that AI is meant to reduce repetitive work, not replace the judgment, relationships, and accountability employees bring to their roles. Provide examples of how this might work in practice.
Emphasize human oversight
Barretto
AI is a support tool, not a substitute for institutional judgment. Strong internal advocacy should make clear where people remain in the process, whether it’s reviewing outputs, making decisions, or owning outcomes.
“AI is not about replacing human judgment; it’s augmenting it,” said Bailey Barretto, Abrigo’s Director of Advisory Services. The goal is to give decision makers intelligent tools that will give them better information and visibility, and more time for their judgment-based work.
An example of how that works in real-life financial services is Old National Bank’s use of Abrigo’s AI-powered Loan Review Assistant to scale its commercial loan review capacity. Read the Loan Review Assistant case study.
The assistant analyzes credit memoranda, annual reviews, financial statements, collateral documentation, guarantor information, and supporting credit files and drafts review narratives and workpapers. But reviewers validate the AI-generated content, and analysts remain responsible for conclusions and documentation. Even with the time required for a staff member to stay in the evaluation loop, reviewers report average efficiency improvements of 20% to 40%. The increased level of document analysis possible translates into nearly two full work weeks of analyst capacity each month, leaving reviewers more time to evaluate risk, support conclusions, and apply professional judgment.
Defensibility is the bridge from AI interest to action
Stakeholders who see AI’s value will next ask whether the institution can implement it responsibly. Build trust and secure support from various people and teams by addressing AI explainability, control, documentation, and governance.
Leaders need to help stakeholders trust the guardrails around AI so they feel confident using it. Internal buy-in grows when those involved or affected by the technology understand both what AI can do and how the institution will oversee and defend its use.
Corporate governance should align AI use with the institution’s strategic goals and regulatory expectations. Support AI efforts with documentation, board-level oversight, and training. Learn key questions for AI governance at credit unions.
A quick checklist before proposing an AI use case
To build internal buy-in, be ready to answer:- What specific problem are we solving?
- Who benefits from the improvement and how will it be measured?
- What data is involved?
- Where and how will human review occur?
- How will outputs be explained and documented?
- Who owns ongoing oversight and monitoring?
- How will success be measured and who reports on it?
Buy-in requires clarity
Successful AI adoption takes more than one person championing the technology. It gains traction when stakeholders understand what the bank or credit union is trying to accomplish, how they will control AI, and why the effort is worth supporting. Start with one workflow. Document governance and controls, and report results against agreed success metrics. For practical guidance and templates, see Abrigo’s additional resources on AI.This blog was written with the assistance of ChatGPT, a large language model. It was reviewed by Abrigo subject matter experts.
Download this brief guide to learn more: "10 Moves that strengthen AI oversight & compliance"
Download nowFAQs
How do banks and credit unions build buy-in for AI?
Banks and credit unions build buy-in for AI by connecting each initiative to a clearly defined business problem, such as reducing manual work, improving service, or strengthening risk monitoring. Early involvement from leadership, compliance, IT, operations, and frontline employees helps create shared ownership and address concerns before implementation.
Which stakeholders should be involved to build support for AI?
AI initiatives should involve executive leadership, IT, operations, compliance, risk, legal, data teams, and employees who will use or be affected by the technology. Early cross-functional participation helps identify concerns, clarify responsibilities, and prevent AI from being treated as a technology-only initiative.
How should banks and credit unions address employee questions about AI?
Banks and credit unions should explain how AI will change specific tasks, where human judgment remains necessary, and what training employees will receive. Involving employees in testing and workflow design can reduce uncertainty, surface operational risks, and create greater trust in the adoption process.
The information, content and materials provided through this website are for informational purposes only and are not intended to constitute legal advice. Customers should consult with their legal counsel regarding the application of laws and regulations to their specific circumstances.
About the Author
Mary Ellen Biery
Senior Strategist & Content Manager
Mary Ellen Biery is Senior Strategist & Content Manager at Abrigo, where she works with advisors and other experts to develop whitepapers, original research, and other resources that help financial institutions drive growth and manage risk. A former equities reporter for Dow Jones Newswires whose work has been published in
