Artificial intelligence is emerging in the financial services space as both banking products and workplace tools.
Recent moves by banks and providers suggest that the most direct benefits of AI will come from internal operations. Banks are using this to prepare advisors for customer meetings, accelerate technology development, review digital product designs, and compress administrative work between employees and completed tasks. A common theme across the board is that banks want AI to reduce the time of expensive processes.
Banks are targeting employee workloads
Merrill Wealth Management and Bank of America Private Bank have introduced Meeting Journey, an AI-powered workflow tool for financial advisors. The system prepares advisors before client meetings, supports note-taking during meetings, and creates follow-up documents afterwards. The bank says the tool saves advisors up to four hours per millions of customer meetings per year.
This tool compiles customer relationship data, recent account activity, and pre-meeting briefing materials. With client consent, record and summarize online discussions.
JPMorgan’s recent focus on AI adoption points in the same direction. The bank suggests that AI talent is becoming central to how large financial institutions plan staffing, technology and productivity priorities. The strategic view is not just that banks need more technology. That means AI capabilities are becoming part of operational capabilities.
TD Bank recently announced that AI has reduced the pre-approval process for a mortgage loan from about 15 hours to about 3 minutes.
Advertisement: SCROLL TO CONTINUE
Technology delivery is becoming a key target for AI
The second point is that banks and their technology providers are using AI to attack the development backlog.
The partnership between Fiserv and Cognition is a useful example. The company announced that it will leverage Devin, Cognition’s AI software engineering platform, to modernize its core banking technology and accelerate work across its complex codebase. This effort is aimed at engineering productivity, testing, quality checks, and faster delivery of functionality to financial institution customers.
This is important because bank modernization has often been slowed not by strategic misalignment, but by the mechanics of changing legacy systems. Code reviews, testing, documentation, integration efforts, and release cycles take a huge amount of time. Artificial intelligence is being introduced into these processes because they are measurable, iterative, and directly tied to delivery speed.
US Bank’s Design Assistant reflects the same operational logic early in the product cycle. In-house tools review designers’ work, flag potential issues, and suggest improvements across digital products. This stemmed from 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 some cases, that means assisting advisors with preparation and follow-up. In other words, it means that product issues are discovered before they have to be reworked. In other words, it means easing the burden of modernizing banking technology.
Operational infrastructure is becoming a strategy
The third point is that AI is becoming part of the machinery that supports banks’ growth.
In a recent PYMNTS conversation, FIS Banking Solutions Co-President Jim Johnson told PYMNTS CEO Karen Webster that issuer processing is moving from a back-office function to a strategic asset as real-time rails, digital wallets and new payment credentials reshape the way banks compete. His point was that banks that treat processing as a cost center risk losing relevance as payment decisions move closer to customer activity.
That insight also applies to AI. As banks begin to measure the time, cost, and execution impact of artificial intelligence, the technology becomes part of infrastructure planning rather than product marketing.
Bank of America’s advisor platform, US Bank’s design tools, Fiserv’s engineering initiatives, and JPMorgan’s hiring priorities all point to the same management question. It’s about where the work slows down and what parts of the work can be shortened.
The answer may vary depending on your institution. For some banks, the next use case could be supporting commercial lending. For others, it may be financial services, fraud, digital product release cycles, or technology modernization. This pattern is consistent. Use AI to remove repeatable work from expensive teams, freeing them to focus on higher-value decisions, client engagement, or execution.
