The Next Wave of AI in Banking Will Have Nothing to Do with Technology
By Steve Cocheo, Senior Executive Editor at The Financial Brand
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It’s over: AI skepticism is passé.
“We’re shifting from experimentation. Most people are believers in the capabilities” says Keri Smith, banking and capital markets AI and data lead at Accenture. “Now, the discussion is about how to get to P&L impact, and how to mobilize around scaled bets where AI can make a difference.”
Banking organizations are also learning how to tie AI tools and approaches into their strategic priorities, according to Smith.
“People are leveraging AI a lot in their personal lives as well as at work,” says Smith. “There are many more educated opinions regarding what banks need to do, how they need to manage it, and more.”
Even long-time holdouts among Accenture’s own clientele are on-board. “They’re coming to AI later than their peers, but they want to do more than catch up. Now they want to leapfrog the leaders,” says Smith.
Reality check: Can late adopters really catch up? Smith believes it is possible.
“With any other emerging technology, that would be difficult to do,” says Smith. “But there’s been a lot of democratization of AI capabilities and tools, and exposure of blueprints for success and a lot of lessons learned. So, I say it’s actually feasible to be able to come in late and yet be able to succeed in bypassing your competition.”
Need to Know:
- Who should own AI in a bank? Among the earliest adopters, implementation began centrally, in part to ensure application of guardrails and security. But Smith says that as efforts mature, more decision-making is being pushed to the business unit, with centralization still playing a role.
- Cross-bank communication is essential, especially as agentic AI implementation accelerates. “You cannot be doing agentic protocols and related moves in silos,” Smith warns.
- Human resources issues are real but go beyond the doomsday headlines. “How are banks going to be managing some of the intellectual capacity that’s being freed up, that will be redeployed? And what are banks thinking about regarding the workforce of the future?” says Smith.
- Regulation and supervisory oversight turned out to be an advantage, not a burden. In the first round of AI, requirements around model risk evaluation and more gave the industry muscles nonfinancial industries lacked.
According to Smith, the next stage of AI will focus on getting past innovation drama and “me-tooism” and pinpointing where adoption, especially of GenAI and agentic AI, will support institutions’ strategic business goals.
“Banks have to marshal limited resources to be able to deliver results,” says Smith.
1. How AI Will Change the Banking Workforce
AI implementation in the workforce often focuses on the perceived fear and resistance of employees, who may believe that cooperation will mean unemployment.
Smith says that, in her experience, however, lack of communication and transparency from management creates the greatest friction points.
Too often, she says, there is little discussion of the overall vision for AI at an organization.
Reality check: Ironically, there’s often grassroots frustration among groups that have not yet received the bank’s AI tools of choice. “There’s a lot of pent-up demand,” she says. “Employees don’t want to be left out.”
What should you do: Smith says now is the time for banks to confront the need for “upskilling” in the wake of increased adoption of AI.
In this context, she frequently uses the word “evergreen” — in other words, upskilling will be a continual process, part of everyone’s employment, as the role of the human in banking institutions changes.
Now is the time to begin talking to the talented about what leadership has in mind, advises Smith.
Beyond adoption of an “always-on” training mindset, banks should also become micro-focused on what upskilling will look like. Smith sees the need arriving for learning and retraining tailored down to individual employees.
Read more: Banking on Intelligence: How to Successfully Structure the Human + AI Workforce
2. How AI Will Change the Role of Banking Leaders and Boards
Adoption of AI — and competitive urgency — has also accelerated the role of boards in AI evaluation, strategizing and decision making. Shifting an industry as intricate as banking takes budgets and allocation of other resources that are ultimately the remit of directors.
In the case of AI, senior leadership must have a deeper understanding of the issues than tech issues of the past demanded. In part, this is driven by the need to apply AI tools as broadly as necessary to achieve the bank’s goals.
“Where an organization’s efforts are siloed or ad hoc, they are just not able to get to the speed and scale needed to succeed,” says Smith. Aligning efforts across a whole institution requires strong senior level sponsorship with evidence that there will be resources to get it done.
Key takeaway: Alignment is also important because proofs of concept and pilot applications will sit in no-man’s land if early effort isn’t made to understand how a specific project fits into the bank function, and, ultimately, into the bank as a whole.
Read more: Digital Bank Employees Used to be the Stuff of Science Fiction. Not Anymore
3. How AI Will Change Organization Charts
Smith says banks will face a dual challenge — applicable to both human talent and to AI efforts — as implementation accelerates.
“There’s going to be more collapsing of silos, and banks will need to move quickly to respond as that occurs,” says Smith. “We’re going to have to harness the power of the more-connected firm.”
Functions that used to be on a different floor or a different state will be key ingredients as AI takes over more areas. Imagine putting players from risk management, compliance and multiple lines of business in one room, and then consider that happening more often as AI mashes more things together.
“It takes a particular type of leadership skillset to be able to speak in the language of these different personas and to be able to mobilize them across a new vision,” says Smith.
She calls this skill “empathy,” not a term often coming up in AI contexts to date, but likely one that will be heard more often.
Key takeaway: Today’s leaders must reconcile themselves to a key difference between past technologies and new forms such as agentic AI, says Smith.
She says that past tectonic changes like cloud computing adoption didn’t require that much depth from leaders, so long as they got the gist.
AI is increasingly different. Leaders must get ready to get more into the weeds.
“In order to guide teams and to make the right leadership decisions,” says Smith, “you need more technical competency than was previously required.”
Read next: Retail Bankers Are Adopting AI for All the Wrong Reasons
