For bankers, AI planning is a sprint, not a marathon

The rapid rise of artificial intelligence in banking has created both incredible opportunity and intense pressure.

Can a community bank realistically shut itself off from AI? The short answer: no. Banks already rely on core providers, cloud partners, and fintech vendors that are embedding AI into everything from fraud monitoring to customer onboarding. Even if a bank doesn’t explicitly seek to adopt AI, it will most likely still encounter it through upgrades, features, and compliance tools increasingly baked into existing systems.

Customers now expect modern experiences like instant fraud alerts and faster decisions, and regulators aren’t warning banks away from AI — they’re urging it to be used responsibly, with governance, transparency, and explainability.

This is why treating AI as a slow, five-year roadmap is risky. By the time such a plan is executed, the competitive landscape will have shifted. Technology is evolving too quickly for slow play. The banks that “win” will treat AI as a sprint: moving quickly, implementing incrementally, and proving value use case by use case.

Banking’s planning mindset vs. AI’s pace

Banking executives are world-class strategic planners. Core conversions, mergers, branch expansions — those take careful planning and multi-year roadmaps. But that same approach doesn’t work in the era of AI.

AI doesn’t move in years; it moves in quarters. Every few months, new capabilities emerge that can reshape customer engagement, compliance, and operations. Yet too many banks are still applying aged vendor selection and diligence processes to AI, slowing their momentum. The result? Wasted time evaluating vendors against yesterday’s checklists while competitors are already in market, learning and iterating.

If you’re waiting on a three-year AI roadmap — or trapped in 18 months of vendor due diligence — you’re likely already behind. By the time you execute, others will have iterated multiple times and built a competitive moat.

Of course, no bank should be reckless. Responsible AI governance, regulator readiness, and explainability must guide every step. But in the AI era, the winners won’t be those with the longest plan drawn out. They’ll be the ones who start now, move fast, and adapt often. AI isn’t a someday project. It’s a right-now imperative.

AI Work Intelligence: A practical starting point

AI is not a panacea, nor a single tool that fixes everything. The smarter approach is to start with targeted problems where AI-powered tools deliver measurable benefits, like in Work Intelligence. Practitioners often call this a human-in-the-loop ethos — automation that supercharges people instead of replacing them.

Community and regional banks can start by tackling high-friction, document-heavy, or compliance-heavy workflows:

• Loan origination: AI-enabled document processing can instantly scan, classify, and validate pay stubs, statements, and forms, reducing delays and freeing lenders to focus on advising customers.
• Compliance reporting: Automated testing and reporting can reduce the burden of audit prep and regulatory reviews, providing risk teams with confidence while saving staff hours.
• Workflow orchestration: Intelligent agents can coordinate tasks across systems, guiding employees through steps in real time while ensuring process consistency and reducing error risk.

Each of these use cases represents a sprint-sized project: measurable, manageable, and immediately impactful.

The human-in-the-loop difference

One of the misconceptions about AI is that it replaces people. The best applications keep humans firmly in the loop, allowing employees to intervene in automated processes when necessary while saving valuable time. For instance, intelligent agents (digital workers) don’t replace staff, they eliminate repetitive “BS work” so employees can focus on higher-value, cognitive activities that build customer trust and drive growth.

This human-in-the-loop approach is especially relevant for community banks, where customer relationships are the cornerstone of value. AI should amplify, not diminish, that human touch.

Why sprinting matters

The misconception that AI can be “put off” until the timing feels perfect is risky. Avoiding it would mean higher costs, slower operations, and greater vulnerability compared to peers already adopting these tools. Banks don’t need to leap into AI blindly, they need to curate, control, and govern it responsibly. That means working with regulator-friendly providers, demanding explainability, and setting clear policies for how AI is applied. It also means prioritizing employee involvement in the sprint to AI, ensuring that human governance and decision-making remains central to business operations and AI oversight.

Waiting for a multi-year plan is no longer safe. A five-year roadmap is outdated the moment it’s written. By contrast, each sprint-sized initiative — onboarding automation, compliance reporting, and lending workflows — has the potential to deliver immediate impact while building the institutional muscle memory needed for bigger transformation.

A path forward

The winners won’t be the banks that wait for a perfect five-year plan. They’ll be the ones that start now: proving value in a single workflow, learning from that experience, and then expanding AI-based Work Intelligence step by step. Each sprint builds momentum, teaches lessons, and creates the foundation for broader adoption.

Community banks owe it to themselves — and to their customers and employees — to run the AI sprint, not the marathon.

Todd P. Michaud is CEO of HuLoop Automation.