Agentic AI is poised to shake up banking, and it will be here soon

Not all new technologies live up to the buzz — think of robotic process automation or the metaverse. But generative artificial intelligence, combined with agentic AI, appears to be for real, with investors and financial institutions putting their money where the hype is: AI has received more than half of venture capital investment thus far in 2025.

Based on a forthcoming comprehensive review of global banking, including interviews with both boosters and skeptics, McKinsey’s conclusion is that gen AI, and agentic AI in particular, will change banking fundamentally, and quickly. The sky is the limit.

But while gen AI has been newsworthy in terms of its disruptive potential, there is a striking paradox: While nearly 80 percent of companies report using gen AI in at least one business function, a similar proportion report no significant impact on their bottom line.

Agentic AI is likely to change that. Agentic AI refers to autonomous AI agents powered by dedicated large language models. These agents can reason and act independently, with little or no human intervention, so that they can perform entire complex workflows. Early use cases have shown significant potential, reducing manual workloads by 30% to 50%. For example, a large bank has successfully upgraded its tech stack with a digital factory staffed by a mix of people and AI agents.

The good news for banks is that agentic AI could lower operational costs by 20% or more, equivalent to 9% to 15% of operating profits. The not-so-good news is that McKinsey expects most of those gains will eventually be competed away, as happened with digital banking and other similarly broad-based technologies. Moreover, implementing agentic AI is likely to be costly. Banks will need to upgrade their legacy technology systems, adopt a new operating model with effective risk guardrails and invest in fraud prevention.

If that’s not enough, agentic AI could also cut into revenues and profit margins. For example, AI agents could autonomously open new savings accounts and move money on consumers’ behalf to find them the best rates. Or agents could optimize credit card lending balances and take advantage of zero balance-transfer offers, threatening margins.

If banks fail to reinvent their business models, McKinsey estimates that global banking profit pools (about $1.2 trillion) could shrink by as much as 10% over the next five to 10 years. But outcomes for individual institutions will vary greatly. We estimate that AI pioneers could open a gap of 4 percentage points of return on tangible equity, or ROTE, relative to slow movers — capturing years of productivity benefits and giving them a meaningful cash flow edge before these advantages get normalized. Slow movers will be caught out with an uncompetitive cost base in a rapidly changing market.

There are four common pitfalls in AI adoption efforts: “copy-pasting” generic use cases instead of basing them on strategic priorities; deploying broad but shallow tools such as copilots that deliver widespread but diffuse productivity gains; failing to invest enough in redesign of work and practical training of employees; and allowing the proliferation of micro-initiatives.

For AI efforts that succeed, however, there are also common elements. These include redesigning the operating model for human-agent collaboration; developing a scalable technology platform with reusable capabilities; and deploying a comprehensive change management program.

In broad terms, what all this means is that banks should focus on transforming entire domains, rather on making incremental efficiency gains. And they should not delay. Early adopters will have the space to experiment, fail fast and iterate, capturing value while their competitors hesitate. Fast followers may be able to learn from leaders’ mistakes, but the risk is that they are caught short if the market scales quickly and they are still learning the ropes.

For a handful of institutions with limited scale and capabilities, or those with defensible business models, moving deliberately and buying vendor-led solutions may be reasonable. For most, however, the danger is that delay will make it impossible to catch up.

No matter the approach, to position their institutions for success, CEOs need to consider how the adoption of AI could change their economic assumptions regarding both revenue growth and efficiency. On that basis, they can address what they need to do to make gen AI and agentic AI bring results.

Think back to the advent of Internet-enabled banking. At first, it was a novelty. Before long, it was a necessity. Banks did not become more profitable overall due to online banking, but some managed to win big, while the rest fell behind. Most industry leaders we spoke to think agentic AI will be at least twice as revolutionary — and move even faster.

The use of agentic AI is going to shape the future of banking; it is already changing the present. Getting it right is not just one more operational decision, but a strategic necessity.