Artificial intelligence is emerging as both banking product and workplace tool in the financial services realm.
The latest bank and provider moves suggest that AI’s most immediate payoff is coming from internal operations. Banks are using it to prepare advisers for client meetings, speed technology development, review digital product designs and compress the administrative work that sits between an employee and a completed task. Overall, the common theme is that banks want AI to cut time out of expensive processes.
Banks Are Targeting Employee Workload
Merrill Wealth Management and Bank of America Private Bank introduced AI-Powered Meeting Journey, a workflow tool for financial advisers. The system prepares advisers before client meetings, supports note-taking during meetings and creates follow-up documentation afterward. The bank said the tool can save advisers up to four hours per client meeting across millions of meetings each year.
The tool pulls together client relationship data, recent account activity and briefing materials before a meeting. With client consent, it records and summarizes online discussions.
JPMorgan’s recent emphasis on AI hiring points in the same direction. The bank is signaling that AI talent is becoming central to how large financial institutions plan staffing, technology and productivity priorities. The strategic read is not simply that banks need more technologists. It is that AI capability is becoming part of operating capacity.
TD Bank said recently that that AI cut mortgage pre-adjudication work from about 15 hours to roughly three minutes.
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Technology Delivery Is Becoming a Prime AI Target
The second takeaway is that banks and their technology providers are using AI to attack development backlogs.
Fiserv’s partnership with Cognition is a useful example. The company said it would use Devin, Cognition’s AI software engineering platform, to modernize core banking technology and accelerate work across complex codebases. The effort is aimed at engineering productivity, testing, quality checks and faster delivery of capabilities to financial institution clients.
That matters because bank modernization has often been slowed less by strategic disagreement than by the mechanics of changing legacy systems. Code review, testing, documentation, integration work and release cycles consume enormous amounts of time. Artificial intelligence is being inserted into those processes because they are measurable, repetitive and tied directly to delivery speed.
U.S. Bank’s Design Assistant reflects the same operating logic at an earlier stage of the product cycle. The in-house tool reviews designers’ work, flags likely issues and suggests improvements across digital products. It grew out of an internal review of design workflows that identified common delays between concept, engineering handoff and launch.
The common thread is that banks are applying AI where internal delays accumulate. In one case, that means helping advisers prepare and follow up. In another, it means catching product issues before they become rework. In another, it means reducing the burden of modernizing bank technology.
Operational Infrastructure Is Becoming the Strategy
The third takeaway is that AI is being pulled into the machinery that supports bank growth.
In a recent PYMNTS conversation, FIS Co-President of Banking Solutions Jim Johnson told PYMNTS CEO Karen Webster that issuer processing is moving from a back-office function into a strategic asset as real-time rails, digital wallets and new payment credentials reshape how banks compete. His point was that banks that treat processing as a cost center risk losing relevance as payment decisions move closer to the point of customer activity.
That insight applies to AI as well. Once banks begin measuring artificial intelligence by its effect on time, cost and execution, the technology becomes part of infrastructure planning rather than product marketing.
Bank of America’s adviser platform, U.S. Bank’s design tool, Fiserv’s engineering initiative and JPMorgan’s hiring priorities all point to the same management question: where does work slow down, and which parts of that work can be shortened?
The answer is likely to vary by institution. For some banks, the next use case may be commercial lending support. For others, it may be treasury servicing, fraud operations, digital product release cycles or technology modernization. The pattern is consistent: use AI to remove repeatable work from expensive teams so those teams can concentrate on higher-value judgment, client engagement or execution.
