Sandeep Shivam is Associate Director at Tavant, building software products that streamlines lending and enhances customer experience.
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Not long ago, I was speaking with a senior leader in financial services who made a point that stayed with me. He said that while institutions have invested heavily in digital systems over the years, many core processes still depend on manual review, fragmented handoffs and too much human effort. The channels may be digital now, but the operating model often still feels old.
I think that observation captures the moment we are in.
Across financial institutions, including banks, stock brokerages, insurance companies, lending institutions and other regulated firms, technology has improved access, connectivity and customer interaction. But it has not always reduced the underlying work required to move decisions, cases and transactions forward. That is why agentic AI is getting so much attention. It offers the possibility of not just responding to requests, but helping work progress with more context, continuity and intelligence.
That said, agentic AI should not be treated as a plug-in upgrade. In regulated industries, there needs to be a practical path.
The best AI programs do not begin with the technology. They begin with the work.
Financial institutions should first identify where processes slow down, where decisions wait too long, where employees spend too much time gathering information and where customers experience unnecessary friction. These are usually the points where manual effort adds the most cost and where intelligent automation can create the most value.
This matters because not every workflow needs an agent, and not every process is ready for one. Institutions that start with real operational friction are far more likely to find use cases that produce measurable results.
One of the most practical early uses of agentic AI is improving how information enters the workflow.
Agents can review incoming documents, extract relevant data, map it to the right fields, identify missing information and guide users on what comes next. In customer-facing journeys, they can answer common questions, explain requirements and help people move forward outside business hours. Internally, they can support teams by organizing inputs before human review even begins.
This is more important than it may sound. Many downstream delays are created upstream. When intake improves, files become cleaner, decisions become faster and avoidable back-and-forth begins to decline.
In regulated industries, the strongest near-term role for agentic AI is not replacing professionals. It is reducing the low-value effort around them.
An agent can help relationship managers, underwriters, operations analysts, claims specialists, servicing teams or compliance reviewers by surfacing relevant information, flagging inconsistencies, identifying likely next actions and preparing work for review. That allows professionals to spend less time chasing information and more time applying judgment where it matters most.
This is also the stage where institutions need to build trust through actual workflow evidence. Agents should work alongside real processes so teams can compare outputs, study performance and understand where the agent adds value and where it still needs tighter boundaries. A polished demo can create enthusiasm, but real operating confidence comes from how the agent behaves in live conditions.
Once institutions begin seeing value, the next step is to use agentic AI to improve decision preparation.
Financial institutions run on decisions, but those decisions are often delayed because information is incomplete, scattered or difficult to interpret. Agentic systems can help by reviewing documents, identifying exceptions, comparing inputs across systems, flagging anomalies and surfacing issues before they turn into escalations.
In lending, that may mean preparing a file for review. In insurance, it may mean organizing claims information before adjudication. In brokerages, it may mean helping operations teams reconcile exceptions or prepare account reviews. In banking, it may mean improving onboarding, servicing or compliance readiness.
But this is also where discipline becomes critical. Before autonomy expands, institutions need clear guardrails. What is the agent allowed to do? What remains advisory only? What requires human approval? What should trigger escalation? What happens if the agent behaves unexpectedly?
Without those controls, autonomy creates risk. With them, autonomy becomes usable.
The long-term value of agentic AI will not come from isolated tasks. It will come from connected journeys.
Customers do not experience financial institutions in silos. They experience one relationship. The same should be true of intelligent systems. Agents should be able to support continuity across onboarding, servicing, support, compliance and future product engagement, while still respecting role boundaries and governance requirements.
At scale, success will not be defined by how many agents an institution deploys. It will be defined by how responsibly those agents are operationalized. That means building trust through evidence, oversight, explainability and measured expansion. In financial services, speed without control is not transformation. It is exposure.
Agentic AI has the potential to reshape how financial institutions operate. It can reduce manual effort, improve responsiveness, strengthen decision preparation and help institutions scale more intelligently. But in a regulated environment, the winners will not be the ones that move fastest or speak most loudly about AI. They will be the ones that introduce it with purpose, build trust through real performance and expand its role only when the controls are ready.
That, in my view, is the real opportunity: not just to adopt agentic AI, but to make it operationally credible.
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