Alok Ajmera, CEO of Prophix
SaaS (Software-as-a-Service) didn’t eliminate the complexity of finance–it digitized it. Planning moved from spreadsheets to platforms, reporting became easier to distribute, and data was more accessible. But the underlying work – reconciling, validating, consolidating, and chasing inputs – remained manual. Finance teams got better tools for managing a burden they were never supposed to carry in the first place.
As organizations look for ways to alleviate that strain, AI investment continues to ramp up: 75% of finance department heads expect their AI budgets to increase over the next two years. But finance leaders aren’t looking for more tools to maintain; they need technology that reduces manual effort without weakening trust in the numbers.
The first wave of AI in finance made it easier to ask questions, summarize reports, and draft explanations. That has value, but it exposes a fundamental limit – insight without execution still leaves the work to finance. Copilots can produce more recommendations, but finance still has to validate the output and complete the next step. A faster answer doesn’t always change the workload.
The next phase of AI in finance isn’t generating more insight. The real goal is to reduce the effort required to get from question to action and fully trust the output. That’s why many organizations are moving away from traditional SaaS and toward agentic systems that can operate within the controls finance requires.
Agentic AI Changes the Equation
Traditional SaaS gave finance a place to manage work, but agentic AI changes how work gets done.
Instead of simply showing that expenses are above plan, an agentic AI-supported workflow can help identify the driver, prepare the supporting detail, and give the controller a stronger starting point for review. For finance professionals, this means less time spent managing routine work and more time applying judgment.
Agentic systems operate across multi-step workflows while the organization sets limits on what it can read, change, or approve. Gartner has noted that agentic AI in finance could perform increasingly complex activities across multiple applications and stakeholders, which is why CFOs need to prepare for the governance requirements that come with adoption.
Organizations need clear limits around what AI can access, which actions require approval, and how decisions are documented. As agents take on more responsibility, transparency and ownership become even more critical. Those controls become much easier to enforce when agentic AI operates within a governed system of record rather than outside of it.
Governed autonomy is where finance is heading, but CFOs have good reason to move carefully. The controls aren’t a constraint on what agentic AI can do, they’re what makes it deployable in the first place.
Trust Will Decide How Far AI Can Go
Finance leaders don’t operate with a sliding scale of accuracy. The standard is the same whether the output is a draft summary or an audit record, but the consequences of being wrong aren’t. A flawed forecast influences capital allocation, a misstatement in a close package triggers a restatement, and an unsupported number in a board deck erodes credibility in a room where it matters most. The question was never whether finance can tolerate imprecision—it won’t. The real question is whether AI can meet the standard that finance has always held itself to.
Agentic finance systems need to separate analysis from authority—not as a limitation, but as a foundational decision. AI can identify an anomaly, prepare the supporting detail, and surface a recommended action, but changes to the official record require the appropriate level of approval. That’s not a constraint on what agentic AI can do—that’s what makes it deployable in a function where every number needs an owner, and every change needs a trail. The organizations that scale agentic AI successfully will be the ones that build permissions, review points, and audit trails before the first workflow go live.
From Insight to Governed Action
The next phase of AI in finance won’t be judged by what AI can generate; it will be judged by the higher-value work it can complete within a foundation finance can trust. That requires more than better software. It requires governed systems that are designed to execute defined work, rely on human judgment when it matters, and produce outputs finance can rely on.
