Let’s be honest — everyone’s talking about AI in banking, but most of the conversation misses the point entirely. After years of helping banks navigate technology transformation, I’ve seen one truth emerge: successful AI adoption isn’t about the technology itself, it’s about solving real problems that matter.
High-profile institutions like JP Morgan Chase and Wells Fargo have already implemented generative AI-based employee tools and AI-powered virtual assistants. While these implementations make the headlines, they also raise an important question: how can banks meaningfully adopt AI, rather than just following the crowd?
Here’s what nobody’s telling you about making AI work in banking.
Stop calling it “AI banking”
Banking is banking. Period. Whether you’re using AI, blockchain, or an abacus, the goal remains the same: serving customers effectively. Recent research reveals an interesting pattern: while 77% of customers are comfortable letting AI handle their simple banking tasks, 63% still want to talk to a real person for important matters. That’s not a contradiction — it’s a roadmap for how to use AI meaningfully.
The data tells us something crucial: customers don’t care about the technology itself; they care about getting things done efficiently while maintaining the human touch when it matters most. Banks need to use AI to enhance human capabilities, not replace them entirely.
Start with why (really)
Before writing a single line of code or signing any contracts, ask yourself: what specific problem are you trying to solve? I’ve seen too many banks rush into AI projects because they felt they had to, only to waste millions on solutions searching for problems.
Take the example of customer lifetime orchestration. When banks approach this with a clear goal — say, increasing product adoption among new customers — they can precisely calculate required AI computation resources, budget needs, and expected ROI. If you can generate $10 million in value with a $1 million investment, that’s a solid business case. Without this specificity, you’re just throwing technology at a wall and hoping something sticks.
The secret? Start small and specific:
- Want to reduce loan processing time? Great.
- Looking to spot fraud patterns more effectively? Perfect.
- Aiming to increase product activation rates? Excellent.
These concrete goals help you:
- Set realistic budgets
- Measure actual results
- Identify real risks
- Calculate true ROI
Smart implementation strategies
As one bank executive recently shared, “You can start with a friends and family launch first, then slowly roll out all the way.” This approach allows for careful testing and refinement before wider deployment.
Cost management is another crucial factor. Innovations like semantic caching can significantly reduce operational expenses, but these optimizations only make sense when you have a clear use case in mind. Successful AI implementations often start small but scale intelligently based on validated results.
The infrastructure reality
Here’s something that often gets overlooked: your AI is only as good as the systems it runs on. Think of it like trying to run modern apps on a decade-old smartphone — it just won’t work well. Banks need to take a hard look at their technical foundation before piling on AI capabilities.
Microservices architecture and platform models can significantly accelerate AI adoption. When you have the right infrastructure in place, with available capabilities and microservices, implementing AI becomes considerably more straightforward and effective.
Time matters (but not how you think)
Yes, major banks are already using AI for everything from virtual assistants to fraud detection. But here’s the thing — you don’t need to be first. You just need to be smart. Starting small with a clear purpose beats rushing in with a “flashy but hollow” AI implementation every time.
The banks that will win with AI aren’t necessarily the ones with the biggest budgets or the most advanced technology. They’re the ones that:
- Know exactly what problems they’re solving
- Keep customer needs at the center
- Build on solid technical foundations
- Scale thoughtfully based on real results
AI in banking isn’t about chasing the latest tech trend or trying to become an “AI bank.” It’s about finding specific ways to serve customers better, make employees more effective, and run operations more efficiently. Start with clear business cases, build on solid infrastructure, and maintain focus on customer value — everything else will follow.
Remember: the goal isn’t to be an “AI bank.” It’s to be a better bank, period.
