Implementing advanced AI technologies in finance

In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of the most tightly regulated functions in the enterprise is now among the most experimentally transformed.

What’s emerging is a layered shift in how work gets done. From variance commentary and fraud detection to contract review and close narrative drafting, AI is embedding itself across workflows, particularly where unstructured data once slowed down everything. Yet, as Glenn Hopper, head of AI and managing director at VAi Consulting, puts it, “the proliferation of AI happened kind of before governance and before a real plan came about.” That bottom-up adoption is forcing a recalibration at the top, where executives must now reconcile productivity gains with oversight, risk, and accountability.

Just as critical is reframing AI’s role. “AI as a means to an end, as opposed to AI being the end,” says Ranga Bodla, VP of industry and field marketing at Oracle NetSuite, underscores a growing consensus: the technology is most effective when it disappears into existing processes rather than outright replaces them. Embedded systems, seamless integrations, and tools like model context protocol (MCP) are accelerating this shift, making AI an ambient capability. Notably, ease of integration, not cost savings or new features, has become the strongest driver of adoption.

Still, the real constraint may be neither data nor technology, but people. “Talent is the actual root cause,” Hopper argues, pointing to a widening gap between domain expertise and AI fluency. Even as concerns about data security and model opacity persist, the more pressing risk may be misunderstanding the tools altogether or restricting them so tightly that employees look for workarounds beyond leadership control. “The auditability of it, I think, is critical,” Bodla notes. 

Looking ahead, the trajectory is clear but variable. AI agents capable of executing complex, multi-step tasks are beginning to materialize, while expanding context windows and interoperable systems promise deeper, more persistent intelligence. But the real transformation may be a gradual shift toward systems that bolster judgement, automate routines, and allow finance teams to spend less time reconciling the past and more time shaping what comes next. 

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