A loan file can be ready for review and still go nowhere. The borrower submitted the documents. The relationship manager completed the initial review. The underwriter has room in the queue. Yet the file sits because a task was never assigned, an update remained in an inbox or nobody realized the missing document had arrived.
Financial institutions employ experienced people to make difficult decisions, but too often, they spend their days pushing work from one step to the next. And while artificial intelligence copilots have helped with parts of that burden, their work ends when the task is complete. They can summarize a document, draft an email and find information buried in a long file, but an employee still has to move the process forward.
Agentic AI can change that. AI agents can be configured to complete defined actions across a workflow, such as updating a task, routing a file or preparing a request for review, all with appropriate permissions, controls and human oversight. For financial institutions, the opportunity lies in reducing the manual coordination around important decisions.
Faster Tasks Can Still Produce a Slow Process
Consider a small business loan application. A borrower uploads financial statements and tax returns. An employee opens each file, checks whether the package is complete and finds that two pages are missing. The employee drafts an email, updates the loan record and sets a reminder. When the missing pages arrive, someone must notice, reopen the file and return it to the proper queue.
No single step takes very long, but multiply that sequence across a lending portfolio, and skilled employees spend a surprising amount of time sorting documents, monitoring inboxes and tracking unfinished work.
An AI agent could classify each upload, flag missing information based on predefined requirements, prepare the follow-up request and route the completed package to an employee. A person would review the communication, handle unusual circumstances and remain responsible for the decisions that follow. The process can keep moving with less reliance on someone to manually track every routine step.
Look at the Work Around the Judgment
Banks and credit unions often approach AI by asking whether it can make a decision. In lending, that quickly raises questions about credit authority, risk and accountability. The better starting point is the work surrounding the decision.
Underwriters need organized files before they can analyze credit. Relationship managers need current information before they can update borrowers. Closers need complete checklists before a loan can move forward.
Much of that preparation follows established rules. Agents can assemble documents, draft sections of a credit memorandum, create follow-up tasks and prepare status updates for employee review. Employees can then focus on exceptions, analysis and conversations that require experience.
This is already becoming a priority across financial services. The Capgemini Research Institute, an arm of global IT consulting firm Capgemini, released a survey in 2025 that found banks across three continents are targeting loan processing, customer onboarding, fraud detection and customer service for agentic AI adoption. Nearly half of the banks surveyed were also creating roles to supervise agents. That supervision matters. Someone must define what an agent can do, where it stops and who reviews its work.
Borrowers Notice When the Process Breaks
Customers may never know that an agent classified a document or created a task. They do know when the bank asks for the same information twice. They notice when days pass without an update. They notice when no one can explain where the application stands.
For a small business, those delays can affect a hiring decision, equipment purchase or expansion. A 2025 Goldman Sachs survey found that 81% of small business owners had difficulty accessing affordable capital. Nearly half said financing challenges caused them to halt expansion plans.
Banks cannot control the entire credit environment. They can control whether an application waits unnoticed in a queue.
Control Must Follow the Work
As agents take on more tasks, banks need a reliable record of their activity. Each agent should have defined permissions. Activity logs should show what information it accessed, what it produced and where a person reviewed the result. Exceptions need a named owner.
Governance cannot be separated from the workflow. A 2026 KPMG survey found that C-suite leaders across banking, technology, asset management and private equity firms identified data readiness, agentic-system complexity and human oversight skills as some of the biggest challenges they face in deploying AI agents.
The best place to begin may be easy to spot. It is the file that gets reviewed twice, the update trapped in an inbox or the process that works only because one employee knows whom to call. Those are the places where agentic AI can earn its role.
Previous