What BofA’s AI Academy Means for Community FI Loan Growth Teams

Devon Kinkead June 19, 2026 0 Comments

Bank of America recently made headlines by training 95% of its employees—roughly 200,000 people—on artificial intelligence fundamentals through an internal academy program.^[1] The initiative wasn’t designed to turn every teller into a data scientist. Instead, it aimed to help employees identify automation opportunities and work more effectively alongside AI tools.^[1]

For community bank and credit union executives watching from the sidelines, the reaction might be a familiar mix of admiration and anxiety. You don’t have the budget to build an AI academy. You likely don’t have a dedicated data science team. And you’re competing for the same borrowers that BofA is now targeting with AI-optimized precision.

But here’s the strategic insight that matters: community FIs don’t need to build AI capabilities. They need to buy them intelligently. And that requires a different kind of investment—one focused on AI fluency rather than AI construction.

The Build vs. Buy Decision Is Already Made

For institutions with assets under $10 billion, building proprietary AI systems for prescreen marketing or credit decisioning isn’t just impractical—it’s economically irrational. The median community bank operates with roughly 50-100 employees. The cost of hiring even one machine learning engineer—with median salaries exceeding $136,000 annually according to the Bureau of Labor Statistics^[2]—would consume a significant portion of most community FI marketing budgets.

The megabanks understood this calculus years ago, which is why they invested in internal infrastructure. But the same technology arbitrage that once favored scale now favors specialization. Third-party platforms can deliver AI-powered prescreen marketing, credit analytics, and campaign optimization at a fraction of what it would cost to build internally.

The question isn’t whether your team can build sophisticated prescreen models. It’s whether they can evaluate, deploy, and optimize the AI-powered platforms that surface the right credit offers to the right members at the right time.

What AI Fluency Actually Looks Like

BofA’s training program emphasized practical application over theoretical knowledge—teaching employees to spot processes that could benefit from automation and to collaborate effectively with AI systems.^[1] Community FIs need the same orientation, but applied to vendor evaluation and campaign management rather than internal development.

For lending and marketing leaders, AI fluency means understanding:

Model transparency: Can the vendor explain how their prescreen targeting works? Black-box algorithms that can’t be audited create compliance risk and limit your ability to refine campaigns.
Data integration: How does the platform incorporate bureau data, your core banking data, and behavioral signals? The quality of inputs determines the quality of outputs (garbage in, garbage out).
Performance metrics that matter: Response rates tell part of the story. Funded loan rates, risk-adjusted yield, market share and cost per acquired account tell the rest.
FCRA compliance architecture: Firm offers of credit carry specific regulatory requirements. Your team should understand how any AI-powered prescreen solution maintains compliance at scale.

This isn’t about becoming technical experts. It’s about asking the right questions and recognizing incomplete answers.

The Informed Buyer Advantage

Community FIs that develop AI fluency gain negotiating leverage and implementation success. They can distinguish between vendors offering genuine machine learning capabilities and those simply repackaging rule-based systems with AI marketing language. They can set realistic performance expectations and hold partners accountable to meaningful benchmarks.

Consider the prescreen marketing workflow. Traditional approaches rely on static criteria—credit score bands, geographic filters, basic demographic targeting. AI/automation-powered platforms can analyze thousands of variables to identify prospects with high response probability, strong credit migration potential, and alignment with your institution’s risk appetite.

But realizing that potential requires someone on your team who understands what “good” looks like. What response rate and market share should you expect from an optimized prescreen campaign versus a traditional batch-and-blast approach? How should performance vary across product types? What’s a reasonable timeline for model optimization?

An AI-fluent team knows that initial campaign performance is just the starting point. They understand that optimization models improve with feedback—and they structure their vendor relationships to capture that value over time.

Practical Steps for Building Institutional Fluency

You don’t need a six-figure training budget to develop AI fluency. Start with these concrete actions:

Designate an AI point person: Assign someone on your marketing or lending team to develop deeper expertise. This doesn’t require technical background—curiosity and critical thinking matter more.
Require vendor education: Make AI explainability a selection criterion. Partners who can’t teach your team how their technology works probably don’t understand it themselves.
Benchmark rigorously: Track campaign performance against traditional approaches using consistent metrics. Build institutional knowledge about what works for your customer, member, or target prospect base.
Join peer conversations: Industry associations and peer groups increasingly discuss AI adoption. Learn from institutions at similar scale who’ve already navigated the vendor landscape.

The Community FI Differentiator

Here’s the counterintuitive truth: community banks and credit unions may actually be better positioned than megabanks to extract value from AI-powered prescreen marketing. Large institutions are often constrained by legacy infrastructure, internal politics, and the sheer complexity of coordinating AI initiatives across business lines.

Community FIs can move faster. A marketing leader who understands AI capabilities can evaluate a new prescreen platform, pilot it within weeks, and scale successful campaigns without navigating corporate bureaucracy. The same relationship-driven culture that defines community banking becomes an advantage when members receive precisely targeted offers that reflect their actual financial needs.

BofA trained 200,000 employees to work alongside AI. Your institution needs to train a handful of key decision-makers to buy AI wisely. That’s not a resource disadvantage—it’s a strategic focus that megabanks can’t replicate.