The focus was sharp and the takeaways diverse at LatentView’s exclusive event — On the Dot in New York. Centered on AI’s impact in finance and how enterprises are embedding decision intelligence into day-to-day workflows, the event featured leaders from the BFSI sector and drew an audience spanning healthcare, tech, and beyond.
CEO, LatentView Analytics
Rajan Sethuraman set the stage for the evening by diving into the accelerating pace of AI adoption. While most companies are still early in the journey, he noted, GenAI and Agentic AI are already reshaping how businesses compete.
Despite rapid innovation, many organizations are still working through foundational barriers, including disconnected data, unclear governance, and ongoing debate around where humans fit in. Many financial services leaders feel unprepared to scale AI across their organization.
Three themes cut across the sessions: Laying the groundwork for AI-powered decision-making, building trust in intelligent systems, and using Agentic AI to move finance from reactive operations to strategic foresight. The underlying message was that real autonomy doesn’t start with tools, it starts with a larger strategic mindset.
Business Head – Financial Services
LatentView Analytics
Automation to Autonomy
Getting the AI Advantage in Finance
Parijat opened with a look back to 1967, when the first ATM gave banks the ability to operate even while branches were closed. That moment was a major milestone for automation in financial services. Since then, the industry has moved from pneumatic tubes to mainframes to workflow platforms like Pega and UiPath. However, transformation isnʼt just about speed or scale today.
Things have advanced to intelligent decision making. This shift is where automation evolves from just completing tasks to actively shaping decisions in real time. Organizations now want systems that can guide choices, make recommendations, and adapt to new inputs. One executive he spoke with put it simply: “Today, I have 300 people running trade operations. What I really want is 50 people and 250 agents.ˮ
These emerging AI agents behave less like static tools and more like digital colleagues. They are onboarded, trained, and eventually retired. Managing them across workflows, systems, and regulatory constraints is becoming the next frontier in workforce design.
Yet, the leap from ambition to execution is daunting. The reason? Data. No matter what the technology, poor data quality will block adoption. To help overcome that, we at LatentView have developed RAISE, a practical framework to support AI implementation:
Targeted AI applications like PlanScanAI, LatentView’s AI-powered solution to streamline insurance plan design, are already accelerating decision-making for organizations. When a large enterprise wanted to assess multiple health plan options for its workforce, the manual process of comparing providers took weeks. PlanScanAI cut the review time from 14 days to under two. “AI will not replace insurers,” Parijat said. “But insurers using AI will replace those that don’t.”
However, the future of financial services doesn’t look like a room full of bots. It looks like teams of humans orchestrating hundreds of agents, with a decision architecture built around speed, reliability, and accountability.
Enterprise Analytics Leader,
Stellarus
Associate Director, Financial Services,
LatentView Analytics
AI and the Trust Equation:
Ensuring Transparency, Fairness, and Accountability
What does it mean to trust AI, not just as a technology, but as a decision-making agent? That question anchored the first fireside chat of the evening, moderated by Jyothi Prakash Gandhamaneni (JP) of LatentView Analytics, in conversation with Carol Rosman Neiditch of Stellarus.
JP set the context for how trust is established between humans, highlighting three important aspects: credibility, reliability, and intimacy, and asked about how trust will be established with AI. He mentioned, “AI can often meet the first two, but it falls short on intimacy, empathy, and human connection.” In response, Carol shared that organizations first need to educate customers and patients, letting them know when bots or AI agents are being used. They also need to take steps to improve the personalization of AI agents. They need to be trained to be conversational and show empathy, especially in healthcare, and be increasingly personalized. People need to feel that there’s a benefit in speaking or interacting with an AI agent.
At Stellarus, Carol has seen how both healthcare and financial services wrestle with fragmented governance and ambitious growth demands. She has also led a transformation to consolidate years of unstructured clinical data (e.g., doctors’ notes, claims records, patient history, etc.) into a unified platform using GenAI. That effort yielded a significant reduction in ingestion time. Yet, she emphasized that speed is just one part of the equation. “Everyone wants faster access to data,” she said, “but without proper context, speed may lead to harm.”
This insight reflects broader industry risk. A recent Trustmarque report found that while 93% of organizations use AI in some form, only 7% have embedded governance frameworks, and just 8% integrate governance into the software development lifecycle. Most businesses lack oversight tools such as audit trails, accountability mechanisms, and ongoing bias testing.
JP steered the discussion toward explainability. Regression models offer transparency by default, but modern GenAI is opaque. “Trust isn’t about making promises,” Carol said. “It’s whether your model delivers on what it promised.” This includes performance validation, transparent outcome tracking, and interpretability.
JP also pushed the conversation into user experience, where fairness isn’t just an algorithmic outcome, but a design choice. In healthcare, conversational bots must interpret legal and clinical language while signaling limits and decision boundaries. “A bot doesn’t need all the answers,” Carol said. “But it should guide you to the right questions.”
Asked about where GenAI can make the biggest near-term impact, Carol mapped out three practical areas where intelligence meets scale:
Responding to JP’s final question on risk boundaries, Carol said, “There’s a difference between using AI to tell you if a procedure is covered by your insurance plan and using it to actually recommend for or against the surgery.” That line, she stressed, must remain sharply drawn. AI can support health decisions, but it should not replace human judgment.
Chief Financial Officer
Bill360
Growth Leader, Financial Services
LatentView Analytics
Agentic AI in Finance:
Shifting from Response to Foresight
While the first fireside chat explored trust, this session turned to timing and touched on how Agentic AI is enabling finance teams to shift from lagging indicators to scenario-driven insight. As Glenn Schulhafer put it, “Agentic AI doesn’t wait for instructions. It anticipates and acts.”
Glenn Fodor emphasized that this is no longer speculative. “We’ve grown up with process automation,” he said, “but GenAI is pushing us further into strategic foresight.”
Fodor then mapped out five high-value domains where Agentic AI is no longer a lab experiment but is providing tangible value:
Schulhafer noted that most SMBs (accounting for nearly half of the U.S. GDP) are now demanding enterprise-grade AI tools. A recent Salesforce study found that 91% of SMBs using AI report revenue growth, and many cite autonomous agents and predictive tools as critical levers.
However, both speakers stressed that increased speed cannot sideline accountability. “You can’t automate accountability,” Schulhafer said. That’s why human oversight remains critical for high-stakes decisions, where trust must never be delegated entirely to machines.
When asked how teams can innovate responsibly within financial regulation, Fodor underscored the importance of sandboxes as controlled environments for safe experimentation. Governments in other countries are already using such frameworks to pilot AI use cases while maintaining compliance.
The panel also explored generational shifts: Younger users may trust bots instinctively, but organizations still need to earn that trust the hard way through transparency and results. “Trust isn’t just a signup form,” Fodor said. “It’s about how you deploy, measure, and iterate the model.”
The discussion then turned to talent. Fodor shared that professionals entering the finance-tech space don’t need to know how to code. Curiosity and the ability to identify “whitespace” applications are more valuable. “Ask questions no one else is asking,” he advised.
The conversation closed on a balanced note of optimism tempered by real-world caution. Fodor said: “The opportunities are boundless, but we must be mindful of privacy, job shifts, and ensuring AI doesn’t make decisions humans aren’t prepared to stand behind.”
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Location: New York
Centered on AI’s impact in finance and how enterprises are embedding decision intelligence into day-to-day workflows, the event featured leaders from the BFSI sector and drew an audience spanning healthcare, tech, and beyond.
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