Balancing Your Bank’s People and AI Processes with Intention
Last week’s Massachusetts Bankers Association panel on The AI Imperative captured this reckoning for Massachusetts bankers: How do you adopt something that feels inherently transactional yet keeps your institution feeling fundamentally human? The conversation reinforced that bankers see the value of AI but are equally determined not to let it push humans out of the equation. What follows is my overview of the strategies these bank leaders are implementing to build a sustainable “partnership” between their people and AI, along with some of my own observations from the conversation.
What Community Bank Leaders Are Doing to Build a Human-AI “Partnership”
1. Align AI to Your Mission and Core Values
Like any successful institution, the strategic decisions you make are anchored in your core values and mission. Your approach to AI should be no different. Let your mission do two things here: protect the human element at your bank and define what AI should be solving for.
For example, if your mission is something like growing mutually beneficial relationships with customers, community, and team members, back-office automation is a potentially strong fit. Automated collections outreach that puts pressure on a struggling customer, perhaps not.
2. Clean Your Data As Soon As Possible
Garbage in, garbage out.
The same discipline you’d apply to a core conversion, a new system onboarding, or a loan file audit applies here. Treat this as your AI readiness window. The quality of your AI outputs will only be as good as what you put in, and structured, accurate, consistently formatted data is the foundation for an AI deployment whose outputs are worth acting on.
3. Invest Time in Actually Understanding AI
One of the clearest points of consensus from both panelists and the audience members was that leaders need to invest time in better understanding AI themselves. One panelist schedules an hour every Friday to test their own tasks in. They elaborated on this weekly practice, sharing that their increased understanding of AI’s value and limitations has added significant merit to their perspective in decisions about AI deployment.
4. Proactively Build Your AI Policies
Compliance and legal leaders on the panel were thinking hard about AI policy. Each of them has already added AI addendums to vendor contracts. They’ve also embedded AI-specific language into existing policies. The panelists further suggested adding AI guardrails before deploying any new solutions, as well as revisiting the vendors you already have relationships with. Ask them, What data can AI access? Who reviews AI-generated outputs before they go out? How are you documenting AI use for examiners? AI policies require ongoing iteration, and in a nod to the moment, several panelists shared that they used AI to draft the actual AI policies.
5. Ask Your Vendors What They're Doing with AI
You may be using more AI right now than you realize, one panelist cautioned.
Most core systems, document platforms, and loan origination software are already piloting and embedding AI. Ask every vendor in your stack to tell you exactly where AI is being used, how outputs are validated, and what data of yours is being processed. If they can’t answer clearly, that’s a signal for mistrust. The same due diligence you’d apply to any material third-party relationship applies to their AI usage.
6. Address Employee AI Concerns Early and Often
Fear of being replaced by AI came up consistently from the audience during the session, and it’s anxiety worth addressing directly and early. The leaders in that room were clear that their intention with AI isn’t to eliminate roles; it’s to make those roles more meaningful. If AI automates financial spreading and document closing workflows, employees gain time for skill growth and strategic thinking.
How those conversations happen will look different at every institution; a town hall, a department meeting, a memo from leadership, or a one-on-one with a nervous employee, but your team likely needs to hear it more than once and from more than one direction.
To empower employees’ AI adoption in their day-to-day work, several banks have had success identifying what they called super users, employees who are already comfortable with AI tools, and tapping them to run informal sessions for their peers. Buy-in often occurs faster in peer-to-peer conversations, they’ve observed.
7. Don't Let Teams Work in Silos
One of the more practical observations from a banker in the audience: deploying AI and actually getting value from it are two different things. They shared that their bank had rolled out an enterprise instance of ChatGPT but beyond writing better emails, they weren’t sure what to do with it. If your operations team has figured something out and your credit team is still struggling, those groups should be talking. Cross-functional working groups, even informal ones, create routine feedback loops so that learnings from one team compound across the institution.
No Two Banks Will Approach AI the Same Way, Just Don't Be the One That Sits It Out.
Naturally, every institution has a different appetite for AI. That’s exactly how it should be, because your bank’s unique values should be guiding that decision. What matters next is that you approach AI deliberately. Pretending AI doesn’t exist is an approach, and not one those bank leaders would advise. You can’t control how AI evolves, but you can control how your institution responds to it.
At GoDocs, our stance is human-led, AI-assisted, and we’re always open to talking through what that balance looks like in practice. If you’re looking for a thought partner on your AI strategy, or want to understand which of our automation and integration options align with your institution’s comfort level, we’d welcome that conversation.
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