Banks and financial institutions are rushing to roll out AI for everything from customer service to compliance and risk. AI’s efficiency is undeniable since it flags anomalies in seconds and handles high volumes of inquiries effortlessly. But when people aren’t involved in strategic decisions, trust starts to slip. Same goes for accountability. In places where the stakes are high — think cross-border payments, catching fraud, or figuring out regulations — AI just can’t do it all. Nowadays, everyone wants to scale up with AI. Are you doing it responsibly, though? That’s where leadership shows.
The Compass Isn’t Enough: Scaling AI Without a Map
Honestly, it reminds me of when Columbus set sail for America. The possibilities seem endless, but you can’t see past the edge of what you know. These days, AI is the compass everyone follows. Developers, project managers, business owners — they’re all using it to find their way. ChatGPT lets regular people launch businesses or smooth out their daily grind. CoPilot can snap together code and dashboards almost instantly. It’s thrilling, no doubt. But if you look a little closer, there’s a reality check: most companies are still steering without a map.
Numbers back this up. Around 60% of businesses don’t have the right people in-house to really scale up AI. So most teams are figuring it out on the fly, sailing through waters nobody’s mapped yet. As a result, systems become even more fragile, lacking clear goals, as projects often collapse under their own weight. In fact, I would argue that half of all AI pilots fail simply because success metrics aren’t defined at the start.
And yes, FOMO plays a role. When leaders feel pressured to adopt AI just to stay competitive, they often prioritize speed over substance. In high-stakes industries like finance, this shortcut can be costly. Automation without oversight doesn’t just risk inefficiency, as it can easily erode trust.
Why AI Alone Isn’t Enough
Financial services are built on relationships, regulation, and risk management, representing three areas where AI actually struggles. While algorithms can process data at scale, they don’t understand urgency, emotion, or concrete circumstances. A client facing financial stress doesn’t want a scripted reassurance. They want to hear, “I understand what this means for your family. Let me help.”
AI also falters in edge cases. Finance is full of exceptions: cross-border transactions, regulatory quirks, and unusual client behavior. When data is incomplete or contradictory, AI often fails to respond appropriately. Worse, it can misinterpret compliance signals — putting institutions at legal risk.
The Human Factor: Why Oversight Matters
Let’s be critical: there’s a solid reason why humans are still a vital part of any contemporary client service. Skills like emotional intelligence, intuition, and the knack for reading between the lines are things that no bot, no matter how sophisticated, can replicate. A skilled advisor can sense when a client’s getting frustrated just from their voice or a few words, knows when a situation needs escalation, and sometimes, yes, will bend a policy if it means solving a genuine problem. AI sticks to the script. People know when the script needs to change.
Take a straightforward scenario: a client’s card gets stolen while he is traveling overseas. AI can freeze the card in seconds for sure — and that is what would most likely happen in reality. But if the client’s stranded with no cash or backup, that’s not a problem an algorithm can fix. That’s when you need a human who can actually step in, pull some strings, and get emergency funds sorted. That’s not just ticking boxes — that’s real, responsive service.
So, when financial institutions rely too heavily on AI, the cracks often show where it matters most. Clients facing unresolved issues may quietly disengage, especially when automated support falls short in moments that require nuance or urgency. Regulatory pressure can build just as quickly if compliance or KYC/AML processes lack human oversight. And for high-value clients, personal attention isn’t a luxury — it’s an expectation. When that expectation fades, so does loyalty.
And this isn’t just a theory. In 2023, a major European neobank found out the hard way when its high-net-worth clients jumped ship after the AI-only support failed to resolve a cross-border payment issue. Those clients didn’t leave because the tech wasn’t good enough — they left because nobody offered them a hand of support and comfort.
Designing Hybrid Models That Work
The best platforms don’t make you pick between AI and real people. They mix both, and that’s where the magic happens. Here’s what actually goes on: AI covers the basics — answering routine questions, handling transactions, collecting client info, and staying available 24/7. But when things get tricky, like a big client issue or something sensitive, that’s when the team steps in. We shouldn’t leave those moments to automation.
At the same time, predictive AI keeps an eye out for signs of trouble — like a customer about to leave or, say, various odd activities — and flags it fast. When something’s off track, people jump on it before it turns into a bigger problem. This promising partnership between technology and human expertise keeps us responsive, efficient, and trusted.
This is real. We just launched an AI chatbot to back up our teams, not push anyone out. The goal was to make our operations smoother and give clients better service. Our bot takes care of the usual questions and onboarding, so our advisors can dive into the big-picture stuff. It’s not flawless, but honestly, it gets the job done. And it’s built with compliance and empathy in mind.
In this respect, an interesting example comes from JPMorgan Chase, which in 2024 integrated predictive AI into its private banking division. The system flags declining engagement or unusual portfolio behaviour, prompting human advisors to take action. The expected result is a measurable decrease in churn and an increase in client satisfaction scores.
Even smaller market participants are finding a leverage for efficiency by using these tools. Payoneer, a global payments platform, uses AI to triage support tickets, but ensures that any flagged for urgency or regulatory complexity are routed to trained specialists. This model has helped them maintain high CSAT scores while scaling globally.
Lessons from the Field
Drawing on my 14+ years in commercial strategy and digital transformation — from Salesforce and LinkedIn to Oracle, Zoom, and now our similar products — I’ve seen firsthand how AI can transform operations. But I’ve also seen how easily it can fail when deployed mindlessly — simply driven by fashion and curiosity.
At LinkedIn, we experimented with AI-driven lead scoring. The model was accurate — but it lacked depth. Sales reps quickly learned that the “low score” leads were often the most promising, simply because they didn’t fit the model’s assumptions. Unique human style and judgment turned out to be the irreplaceable ingredients.
At Zoom, AI helped us scale onboarding and support during the pandemic. But when enterprise clients faced urgent compliance questions, only human escalation could resolve them. The lesson was clear: AI is a multiplier and performance enhancer, not a replacement.
Final Thoughts: Augment, Don’t Replace
All in all, although AI is a powerful tool, it’s not a substitute for human judgment. In finance, where trust and substance remarkably define success, augmentation beats automation every time. Institutions managing to scale AI responsibly by keeping their goals intact while preserving original layered models and human oversight will succeed in this transformation. Not only are they to avoid the pitfalls of blind deployment, but also build systems proving resilience, quality responsiveness, and readiness for the future.
The horizon may be unclear, but the direction is obvious: AI should empower people, not replace them.
