Some recent reports checking in on the progress of generative artificial intelligence (Gen AI) found that success is less about what an organization adopts than it is about how the organization adopts it.
A July report from the Massachusetts Institute of Technology (MIT) found that financial institutions are not making much progress in transforming their business processes through adoption of Gen AI.
MIT researchers reviewed more than 300 AI initiatives and interviewed 153 senior leaders at 52 organizations.
Despite massive investments, “this report uncovers a surprising result in that 95% of organizations are getting zero return,” the researchers wrote. “Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact.”
The researchers found tools like ChatGPT and Copilot were widely used, but custom-built or vendor-supplied enterprise-grade systems “are being quietly rejected.” They said the custom solutions “stall due to integration complexity and lack of fit with existing workflows.”
Media and telecom, and professional services were the only two of eight major industries where adoption led to “meaningful structural change.” Financial services along with three other industries were identified as having relatively weak gains.
“The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time,” they wrote.
The report, “The GenAI Divide: State of AI in Business 2025,” was published by MIT’s NANDA initiative, which it describes as “an open-source, decentralized framework enabling trillions of AI agents to discover each other, communicate, and transact securely across organizational boundaries.”
A McKinsey & Co. report released July 9 looked closer at bank implementation of Gen AI, which also found to be widespead, but shallow.
McKinsey surveyed and interviewd executives at 44 banks with assets or $100 billion or more in the second half of 2024. Half of the executives said Gen AI was a priority, and saw particular promise in early-warning systems, credit memo drafting, and customer engagement activities.
“That said, sentiment is not universally positive,” McKinsey said. “Many banks are cautious about scaling amid continuing skepticism over the technology’s financial benefits. As a result, only a few, mainly larger institutions are ahead of the curve, while most say progress has been slower than expected.”
McKinsey identified two structural constraints:
“First, decision-makers are focused too narrowly on simple use cases rather than seeking to transform more complex workflows and end-to-end journeys.
“Second, we find that most banks have only recently started to deploy agentic AI, a version of the technology that uses decisioning algorithms to create cross-cutting impacts, for example, in the middle and front offices across lines of business.”
The study found very few uses of Gen AI have been fully deployed. For example, 27% of the banks had launched pilots for synthesizing information for credit decisioning, but none had reached full deployment. Many were trying to first mitigate risk before scaling up.
“At many institutions, there is considerable skepticism over the technology’s potential to boost productivity, often reflecting previous experiences where tech rollouts did not achieve the expected gains,” McKinsey said.
Download the McKinsey report: “Banking on gen AI in the credit business: The route to value creation”.
The Filene Research Institute is also interested in persuading credit unions to consider the how over the what of AI adoption.
Lamont Black, associate professor of finance at DePaul University and a Filene fellow, emphasized during an Aug. 6 Filene webinar that AI adoption needs to be strategic, and strategy requires involvement by the chief executive and his leadership team.
The alternative is often a multitude of Gen AI projects scattered around the organization, often attracting a patchwork of vendors.
“Tactics are not the same as strategy,” Black said. “Strategy should be driving tactics. It’s the executives at the top of the house thinking about what is the direction and how do we move in that direction together.”
“This is not just a technology change, this is really organizational change,” he said. “Every executive has a role to play here to take ownership of AI within their part of the organization. that ownership ... gives them skin in the game.”
Strategy includes setting priorities, and deciding how to measure success. And this is where the board needs become involved.
Black said boards can be brought into the process through education.
“You want to get your board to the place where they can actually participate in thinking about how does this relate to the strategic plan,” he said. “I often run into board members who at the beginning of the session are kind of scared by it and very much like arms crossed, but by the end of the session they’re like, ‘OK, I can see this and that would probably be helpful.”
The webinar,“AI and Executive Leadership: How CU Execs Should Approach AI Strategy,” can be found here.
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