Most banks and insurers are deep in the transformation trenches right now. Trading teams have copilots. HR launched an internal chatbot. Legal is piloting document review. Finance rolled out expense assistance. Each department is building its own AI future, one use case at a time.
But here's what we're seeing across Global 2000 financial firms: the hardest part isn't deploying GenAI. It's knowing if it's actually working.
The real challenge: adoption across departments
Financial institutions are tackling GenAI transformation with the same playbook they used for digital initiatives. Department-by-department rollouts. Change management frameworks. Executive sponsorship. Training programs. All essential.
But unlike past transformations, GenAI creates a unique blind spot: you can't see how people are actually using it.
When HR launches that internal chatbot, they know it's running. They see login counts. But are employees getting useful answers? Are they coming back? Are they asking about sensitive topics that should be flagged? Traditional IT dashboards won't tell you.
When trading deploys copilots, leadership wants to know: "Is this making our analysts more productive?" Token counts and API calls don't answer that question. You need to understand what people are asking, where they get stuck, and whether the AI is actually helping them make better decisions faster.
Why measurement becomes mission-critical
Every successful digital transformation in banking has required feedback loops. You launched online banking, then optimized based on user behavior. You rolled out mobile apps, then refined based on customer journeys.
GenAI transformations are no different, except the feedback is hidden in conversations instead of clicks.
Here's what we're seeing at firms that get this right:
Department-level insights drive better rollouts. One bank discovered their compliance team had much higher AI usage than expected, while treasury barely touched their assistant. Instead of guessing why, they dug into usage patterns and found compliance was getting exactly what they needed, while treasury was getting generic answers to specialized questions. The fix: better training data for treasury, more investment in compliance use cases.
Risk management becomes proactive, not reactive. A global insurer was rolling out GenAI across regions. Usage analytics revealed that European staff were including more PII in prompts than US teams. Instead of learning this during an audit, they could address training and controls immediately.
Executives get real transformation data. Instead of "the AI had 99.9% uptime last quarter," leadership hears "trading analysts are asking 40% more complex research questions and completing tasks 25% faster." That's transformation language, not IT language.
The transformation-to-measurement bridge
The pattern we see in successful financial services AI programs:
- Start with business-led pilots (not metrics) — let departments define success in their terms
- Instrument behavior tracking from day one — understand what people actually do with the tools
- Connect usage to business outcomes — link conversation patterns to productivity, compliance, and satisfaction
- Scale based on proven value — expand what works, fix what doesn't
The measurement layer doesn't replace transformation strategy. It makes transformation strategy work. When HR sees that employees are asking about benefits more than policy, they know where to focus training. When trading sees analysts dropping off after getting long-winded responses, they know to tune for conciseness.
Beyond launch: making transformation stick
Most banks nail the launch. They get buy-in, deploy tools, and run training. Where many struggle is month three and beyond. Usage quietly drops off. People revert to old workflows. Early enthusiasm fades.
The difference between stalled pilots and company-wide transformation? Continuous feedback loops that show you what's really happening.
You can't manage what you can't see. And in GenAI, what matters most — how people actually interact with AI, what they need, where they get frustrated — happens in conversations, not dashboards.
The successful financial services AI programs we work with treat user behavior as transformation data. They measure adoption, satisfaction, and business impact from day one, then use those insights to guide everything from training to executive reporting.
Want to see how leading banks are turning GenAI conversations into transformation strategy? Book a demo to explore what real usage analytics reveals about your AI investments.
