How to Encourage AI Adoption at Your Financial Institution Through Governance, Trust, Culture, and Change Management

AI adoption in collections and recovery is often treated as a technology decision. I believe that is where many financial institutions begin to go wrong.

Technology matters, but it rarely determines whether an AI initiative succeeds. Adoption depends on whether leaders choose a meaningful problem, establish clear governance, prepare their institutional knowledge, and earn the confidence of the people expected to use the technology.

That makes AI adoption a business transformation initiative. Financial institutions that approach it as another software implementation risk deploying a capable tool that never becomes part of the way work gets done.

There is no value in adopting AI simply to keep pace with the market. A vague instruction to “use AI” gives teams little direction and creates understandable skepticism.

Start with a specific operational challenge. Your collections team may spend too much time researching accounts before a call. Documentation may be inconsistent. Collectors may struggle to locate the correct procedure for a complex borrower situation. Leaders may need better visibility into portfolio trends.

Define the problem, the desired result, and how you will measure progress before selecting the technology. Collections is a practical place to begin because the work is information-intensive, compliance-sensitive, and closely connected to the account-holder experience. Clear objectives keep the work grounded after the novelty of AI fades.

Governance should not be postponed until an AI tool is ready for broad deployment. It belongs at the beginning. Strong governance doesn’t have to slow adoption. In fact, clear guardrails can accelerate it. The institutions that move the fastest aren’t necessarily the ones with the fewest rules, but the ones with the clearest rules. Clear governance builds trust, and trust drives adoption.

Leaders need to decide what the AI is permitted to do, what information may inform its responses, how outputs will be reviewed, and where human judgment remains required. Those boundaries should reflect the institution’s risk appetite, policies, and service standards.

Collections leaders should also distinguish between the different roles that data, predictive models, and AI assistants play in collections operations. Data provides the underlying account, payment, engagement, and portfolio information. Predictive models identify patterns or risk signals in that data. AI assistants can help employees interpret information, locate guidance, or understand the next recommended step. Each capability requires appropriate controls, testing, and oversight.

My view is direct: if your financial institution cannot explain where an AI response came from, who is accountable for reviewing it, and how errors will be identified, it is not ready to scale that use case.

AI will not arrive with an understanding of your financial institution. Every bank and credit union has its own policies, procedures, terminology, approval paths, communication standards, and exceptions. Years of operational knowledge may be spread across desktop procedures, templates, training materials, and the experience of long-tenured employees.

Before expecting AI to provide useful guidance, examine that foundation. Are procedures current? Do departments describe the same process differently? Are important exceptions documented? Does written guidance reflect how collections work is performed today? Preparing that knowledge also means determining what the AI needs to know. Which procedures matter? Which exceptions need to be included? What terminology or acronyms are unique to the institution?

The quality and relevance of the information provided to an AI system directly affect its usefulness. One of the biggest misconceptions is expecting AI to be perfect on day one. Testing and employee feedback help refine how that information is applied. Leaders should plan for continuous improvement over time and create a disciplined process for reviewing results.

Adoption suffers when AI is introduced after the important decisions have already been made. Collectors understand which situations require extensive research, which procedures are difficult to interpret, and where borrower conversations demand flexibility. Their perspective should shape the use case, testing, and rollout.

I strongly recommend creating a group of frontline power users and involving them early in the process. Ask them to test realistic scenarios, identify unclear guidance, and challenge recommendations that do not reflect actual workflows. That’s where some of the most valuable learning happens. Their feedback helps refine the experience, while also giving them a role in helping their peers understand how AI can support their day-to-day work.

For complex organizations, consider implementing one department or specialty at a time. A focused rollout makes it easier to learn, adjust, and build credibility without overwhelming employees or implementation teams.

Organizations should be clear about where AI supports the work and where human judgment remains essential. Employees may hear “AI” and assume leadership is planning to eliminate positions. Avoiding that concern will deepen it. Explain which repetitive or administrative activities the technology may support, as well as the decisions and conversations that will continue to require people.

Collections and recovery depends on empathy, judgment, negotiation, and contextual understanding. Borrowers experiencing hardship, disputes, bankruptcy, or other sensitive circumstances need people who can listen and respond appropriately. AI can help prepare collectors with relevant information and guidance, while people remain responsible for applying judgment. The best AI doesn’t make decisions for people. It helps people make better decisions.

Leaders should also discuss how roles may evolve. Employees may spend less time searching for information and documenting routine activity, and more time resolving complex situations, counseling borrowers, improving processes, and developing specialized skills.

Trust is earned through consistent communication and visible evidence. Tell employees what is being tested, what has been learned, and what will change because of their feedback. Explain the roadmap without flooding them with technical detail. Measure employee and account-holder experience alongside productivity and efficiency.

Most importantly, set realistic expectations. AI requires testing, refinement, and oversight. The institutions that lead in AI will connect innovation with discipline, involve employees in meaningful ways, and maintain human accountability throughout the process.

I believe collections is one of the strongest use cases for AI across the financial institution. It brings together rich behavioral data, predictive models, complex workflows, direct account-holder engagement, and results that leaders can measure. AI can help employees turn that information into action while preserving the human judgment that sensitive borrower situations demand.

Financial institutions should approach adoption thoughtfully, but they should approach it with confidence. When leaders establish the right guardrails, prepare their institutional knowledge, and invite employees to shape the change, AI can become a trusted and valuable part of collections and recovery. I am optimistic about where this technology will take the industry, and I believe the institutions investing in responsible adoption today will define what better collections looks like tomorrow.