05.10.2026
The conversation around AI in banking has focused on what machines can do. The more interesting question is what people should do next. As AI reshapes the industry, human expertise is becoming more valuable, not less, changing the skills banks need to succeed.
AI changes the work. People change the outcome.
As HSBC noticed, it is true that 73% use AI for finance and investment, but it’s the people that make decisions.
I spend my life talking to people at the sharp end of financial services, and lately it feels like a sprint to see who can bolt the most AI and analytics onto their systems first. Everyone is racing to automate, and in the rush to digitise we keep forgetting the person behind the screen.
After dozens of conversations on my Women Who Make It Happen podcast, I have landed on something. The real winners here are not the ones with the deepest pockets for new software. They are the ones doubling down on their people.
Tech is the plumbing. People are the architects. Our industry carries a serious social purpose, from protecting pensions to helping families buy homes, and if we let that turn into a cold automated front, we lose the heart of the thing. The future is not just smarter machines. It is a more human way of leading.
Four ways to keep financial services human in the age of AI
AI is changing how banks analyse data, manage risk and serve customers. But the biggest opportunity lies in how people use those insights. These four principles show how banks can combine technology with the judgement, curiosity and leadership that AI can’t replicate.
1. Automate the grunt work to unlock the golden nuggets
Automation is not about binning jobs. It is about clearing the tedious bits off people’s desks. Nidhi Agarwal, Chief Model Risk Officer at Virgin Money, is a strong advocate for letting the machine take the mechanical, repetitive side of modelling so people do not have to.
In her interview with Sue Saunders, she said:
“If you don’t automate it, you still have the team then having to do a lot of tedious task of repeating the process which is not that much a value ad.”
The point is not the technology. It is what people do once the grunt work is gone. Strip away the repetition of cleaning data and running basic code, and your team gets the room to find what Alexandra Winward, a Finance Data Executive, calls the golden nuggets: the moments of real insight. That is a leadership win. People finally have the space to be brilliant.
2. From IQ to EQ: the new leadership currency
Banking leadership used to run on technical IQ. The currency now is EQ, which is emotional intelligence. Code can be efficient, but it cannot intuitively read what a customer needs, or bring empathy when someone is in a mess.
Ellen Watson Hicks, a Chief Risk and Compliance Officer, champions compassionate leadership, not as a soft option but as a strategic one: a culture where people feel safe enough to perform. It connects to what Vicky Stubbs calls strength-based leadership. Use AI to manage the mechanical weak points in a process, and double down on the natural strengths of your people.
Three things a machine will not replicate.
- Empathy – understanding the real-world impact of financial decisions on families and businesses.
- Psychological safety – an environment where teams can fail fast, learn and improve without fear.
- Reading the room – sensing the stakeholder concerns that never show up in a spreadsheet.
Human oversight matters more now than it did twenty years ago, not less.
3. Data is useless without the story
Raw data is noise until someone says what it means for the business. That is where data storytelling earns its keep. As Alison Tattersall and Alexandra Winward both note, data alone does not change minds. The story you tell with it does.
Alison Tattersall, a banker turned marketeer, says the best leaders are:
“Really clear around setting a direction and painting a picture and telling the story.”
You are the bridge. The machine gives the output, you give the board the narrative. You take the figures and turn them into a strategy people can believe in: the “what” from the AI, the “so what” from you.
4. Curiosity is the ultimate risk mitigator
Curiosity is one of the most powerful risk tools we have. Clare Pearson, a self-described “nosy” auditor, believes that while AI spots patterns, only a human scratches the surface to find the why.
That curiosity pays. AI is good at spotting the pattern, but it takes a human to ask why the pattern is there. Clare has described walking into a firm facing a major regulator’s fine and finding the mess underneath: cash all over the place, client money mixed into office accounts, the books held together by spreadsheets. That kind of nosiness, by her account, helped turn a near fatal penalty into a survivable one. A machine flags the anomaly. A person works out what it means.
Machines follow rules. Humans ask the cheeky questions. In a boardroom where everyone assumes they already know, the person willing to ask “does this actually make sense?” is your best protection against failure.
The competitive edge is still human
AI is transforming banking, but technology alone won’t determine who succeeds. As more organisations adopt similar tools, the real difference will come from the people using them. Technology is the engine, human EQ is the driver. You can run the most advanced AI super model on the market, but without the right people guiding it you are heading for a crash.
That has been the thread running through every conversation in Women Who Make It Happen. AI can process information at extraordinary speed, but it can’t replace judgement, curiosity or empathy. Those qualities remain firmly human, and they’re becoming more valuable as technology evolves.
At Morson Edge, we specialise in finding exactly that kind of talent. Whether it is permanent, interim, contract or executive search, we help banks and fintechs turn rigour into a competitive advantage by putting people first. You can learn more on our Morson Edge Financial Services page.
One question to leave you with. As your organisation invests in AI, is it investing just as much in the people who will make it work?
