In the CIO role within financial services, you quickly learn that responsibility, trust, and scale shape every technology decision.
That reality is exactly why AI conversations feel different from past waves of innovation. Lately, every conversation I have as a CIO, with the board, my peers, or my team seems to come back to the same question: What are we actually doing with AI?
Not whether we believe in it. That debate is over.
The real question is why, despite all the pilots and proofs of concept, so few institutions feel like they’ve truly moved the needle.
From where I sit, AI itself isn’t the hard part. The friction comes from everything that surrounds it.
We operate in an environment where stability and trust matter as much as innovation. Every system we touch carries regulatory implications. Every data set has ownership questions. Every new capability introduces scrutiny from risk, compliance, and security teams, often for good reason.
At the same time, we’re competing with fintech’s that don’t carry decades of technical and organizational baggage. They move fast. We must move right.
That tension defines the role.
If I’m being honest, we’re not short on ideas. We’re overwhelmed by them.
Every business leader has a use case. Customer on-boarding. Claims processing. Fraud detection. Developer productivity. We have more ideas than we can realistically stand behind. The real question is which of these efforts actually scale, which create durable value, and which look promising in a demo but don’t hold up once they’re in production.
Pilots are easy to justify. Production is where accountability shows up.
Once something moves beyond experimentation, we have to answer uncomfortable questions: Who owns it? How is it governed? How do we measure its impact beyond anecdotal success? How do we ensure it doesn’t introduce new risk or bias?
Without clear answers, pilots stay pilots. And over time, that erodes confidence, not just in AI, but in our ability to operationalize it.
There’s a perception that financial services firms are “data rich.” In theory, that’s true. In practice, the data is fragmented, inconsistently defined, and tied to systems that weren’t built for real-time intelligence.
As CIOs, we’re expected to stand behind AI-driven outcomes, yet we often don’t fully control the quality, lineage, or integration of the data feeding those models. That creates hesitation. And hesitation slows momentum.
Modernizing legacy platforms isn’t just expensive; it’s risky. But not modernizing them is a risk too. That’s the trade-off we navigate every day.
In financial services, moving fast without governance isn’t bold, it’s irresponsible.
What’s changed is the pace. GenAI introduces new questions around transparency, explainability, and data usage that traditional controls weren’t designed to handle. Boards want confidence. Regulators want clarity. And rightly so.
The mistake is treating governance as something that happens after innovation. When that happens, everything grinds to a halt. Governance has to be built in, not bolted on.
From my perspective, the goal isn’t to slow teams down, it’s to give them clear guardrails so they can move faster with confidence.
GenAI isn’t just changing systems. It’s changing how people work.
Developers write code differently. Analysts interact with data differently. Business users suddenly have access to capabilities that used to require specialized teams. That’s exciting, and unsettling.
Part of my role now is making sure AI is positioned as an enabler, not a threat. That means investing in education, redefining roles, and showing early wins that augment human capability rather than replace it.
The organizations that get this wrong will struggle with adoption no matter how good the technology is.
Over time, a few things have become hard to ignore.
The organizations making real progress aren’t chasing the loudest use cases or the newest tools. They’re putting discipline around how intelligence enters the enterprise.
That usually starts with clarity on outcomes, what actually needs to change in the business, rather than technology experiments looking for a home.
It’s followed by decisions around how AI is governed, funded, and scaled. Not as an afterthought, but as part of the delivery model itself. When those expectations are clear, teams move faster, not slower.
There’s also a growing recognition that value doesn’t come from one-off deployments. It comes from building repeatable patterns, common platforms, shared standards, and a consistent way to move from idea to production without starting from scratch every time.
And finally, there’s an understanding that automation only works when it reduces friction in real workflows. Intelligence has to show up where work actually happens, not as another system people have to learn around.
When those pieces come together, AI stops feeling experimental and starts behaving like a core enterprise capability.
Very few organizations can navigate all of this alone. Not because they lack talent, but because the challenge spans strategy, architecture, governance, operations, and culture all at once.
Progress accelerates when there’s support that understands how to align ambition with execution, how to move from roadmap to reality without creating risk or disruption.
That kind of help doesn’t show up as a tool. It shows up as clarity.
AI will undoubtedly reshape financial services. But success won’t come from who experiments the most, it will come from who scales with intent.
From the CIO’s chair, the real work is asking the hard questions early, building trust into the system, and ensuring intelligence delivers outcomes the business can stand behind.
That’s what turns potential into progress.
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