Three Practical Pillars of AI Readiness

‍Banks and credit unions are entering a new phase of AI adoption. According to recent analysis from McKinsey, the industry is moving beyond isolated pilots toward AI systems capable of executing real institutional work—modernizing legacy processes, reducing rework, and lowering operating costs at scale.‍

For institutions still early in their journey, this moment brings both urgency and opportunity. The question is no longer “Should we use AI?” It’s “How do we apply AI in a way that’s structured, governed, and aligned to how our institution actually operates?”

Why AI Readiness Matters

McKinsey estimates that banks delaying modernization risk leaving significant value on the table by the end of the decade. At the same time, competitive pressure is increasing from consumer-facing AI tools that can instantly compare rates, move deposits, and surface better offers—eroding traditional loyalty advantages.

Institutions that are truly AI-ready will be able to:

• Execute manual work more consistently
• Improve compliance accuracy and defensibility
• Respond faster to customer and member needs
• Reduce operational friction and rework
• Retain deposits through more timely, informed action

Importantly, readiness doesn’t require massive budgets or large data science teams. The institutions making the most progress are focused on foundational moves that turn AI into a governed, repeatable capability rather than a collection of disconnected tools.

Pillar 1: Structure Institutional Knowledge

Before AI can execute work, it must operate within the same rules the institution is accountable to. That starts with structured access to policies, procedures, and operational documentation.

Focus first on:

• Centralizing key policies and procedures
• Eliminating outdated or conflicting documents
• Reducing file-share and folder sprawl
• Making institutional rules easy to find, interpret, and apply

Unstructured AI struggles when information is fragmented or unclear. AI cannot consistently follow rules it cannot see—or interpret differently each time.

Pillar 2: Apply AI to Real, Repeatable Work

AI readiness is not about brainstorming abstract use cases. It’s about identifying institutional work that benefits from consistency and structure.

Look for:

• Repeatable, rules-driven processes
• High-volume operational tasks
• Areas with regulatory or policy risk
• Functions with long cycle times or heavy rework

Early impact often comes from frontline workflows such as account onboarding, lending documentation, compliance reviews, reporting, or policy interpretation—areas where consistent execution matters as much as speed.

Pillar 3: Build Governance Into Execution

AI should strengthen human judgment, not bypass it.

True readiness means embedding oversight directly into how work is executed, including:

• Clear guardrails for sensitive activities
• Human review and approval where required
• Traceable outputs and auditability
• Alignment with internal policy and external regulation

The goal isn't full autonomy—it’s controlled execution. AI should produce work that is structured, reviewable, and prepared for human judgment, not opaque answers that are difficult to defend.

The Bottom Line

AI capable of executing institutional work is already reshaping operating models, cost structures, and customer expectations.

But readiness is the deciding factor.

Banks and credit unions that invest now in:

• Structured, accessible institutional knowledge
• High-value, repeatable workflows
• Governance and oversight built into execution

Will be positioned to scale AI safely, operate with greater confidence, and compete without increasing operational fragility.

You don’t have to be first.

But you can’t afford to be unprepared.