Finance is Investing in AI. Why Aren’t CFOs Seeing Results?

Finance has an AI problem: investment is accelerating, but the payoff remains inconsistent. Recent Gartner research paints this picture clearly: AI initiatives are only succeeding about half of the time despite continued investment. With investment rising, and the use of AI infiltrating both our professional and personal lives, why is finance struggling to see the ROI?

The fundamental challenge is the disconnect between AI and finance. AI is probabilistic. Simply put, it uses prompts or data to anticipate what comes next. And, if you “ask” AI the same question three times, you’ll get three slightly different answers. It’s akin to a CEO asking the CFO, CRO and CMO why sales were down for the quarter—you’re going to hear three different explanations all based on the same information.

While some variation in response might be okay if you’re a university student using AI to help write a term paper, variability in finance is, to put it mildly, strongly discouraged. Finance operates in a strictly deterministic environment. Finance teams need the correct answer every time, and each balance, forecast, and close process must be traceable, explainable, and auditable.

Because of its probabilistic nature, many of today’s AI tools create reliability concerns. They often function outside existing controls and draw on information that spans across disconnected systems. This reality is incompatible with the standards that finance operates under. Whether the output is a forecast, board presentation, or financial close, the expectation is the same, and even a small discrepancy can cause issues that ripple across an enterprise. In finance, 99% accuracy is 0% trust.

The Difference Between Outputs and Execution

Organizations relying on generative AI are most likely to feel the friction of probabilistic vs. deterministic.

While generative AI can be useful for repetitive, well-defined tasks, increased efficiency in low-value routine processes rarely translates to substantial margin improvements.

Finance requires more than faster content generation – it requires governed systems that can move work forward. Today, finance teams are required to manually reconcile accounts, match transaction records to source documents, and complete consolidations. In short, the workflows that power the finance function cause operational challenges.

What finance teams need are trusted tools that help them address day-to-day realities and reduce operational burden without compromising control. This requires governed AI that can simultaneously understand the task and the financial context in which it occurs. That’s why industry conversations are increasingly shifting to agentic AI that reduces manual effort and executes work directly within the finance function.

Trusted AI that Completes the Work

While generative AI can speed up certain tasks, agentic AI can operate within core workflows and execute them. In a Deloitte poll, more than 2 out of 5 respondents (42.7%) reported increased efficiency and productivity as the greatest benefit of using AI agents to support finance and accounting processes. But this only works with full financial context.

In the same Deloitte poll, 1 out of 5 respondents (21.3%) cited trust as the leading barrier to the adoption of agentic AI. In other words, auditability cannot be an afterthought. Financial institutions need to equip AI with reliable information. AI should have access to governed data, understand every business rule, and work within existing reporting processes. It needs to track all work, creating a complete audit trail of how it got from point A to point B. This foundation should be co-managed by the finance and IT functions to determine whether AI models are deployable at scale.

Once organizations establish full auditability, agents can execute financial workflows reliably. They can create deterministic outputs that finance teams can easily trace back to the source, which will improve financial decision-making. Agents can make decisions within governed workflows, using the full extent of enterprise data to form auditable closing statements and accurate forecasts. Over time, governed agents learn from recurring processes and feedback, enabling broader use cases across financial institutions.

In short, intelligent agents can execute processes with greater accuracy and efficiency. While it doesn’t replace the need for human-in-the-loop practices, finance teams can oversee AI execution rather than manually push AI outputs from one step to the next. This result is fewer bottlenecks across operations – a direct contrast to the outcomes of generative AI in finance so far.

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Helping Finance Find AI ROI

AI that surfaces insights has value, but AI that optimizes processes delivers measurable business impact. That’s the difference between generative and agentic AI. For finance, the real value of this technology lies in its ability to integrate directly into the finance function and fundamentally change how work gets done. With the right governance in place, it allows teams to reduce work, optimize processes, and produce measurable value within a foundation that finance can trust.

When finance teams use trusted autonomous agents to address the workflows that hold back operations, they can realize greater returns on their AI investments and achieve meaningful results. This empowers finance teams to spend less time on administrative work and more time improving the enterprise through strategic planning, deeper analysis, and higher-value decision-making.

AI done right can be used to empower finance organizations to be dramatically more influential in ways that weren’t possible in the past.

About Prophix

Prophix helps finance teams lead from the center of the business. Prophix One by Prophix is a Financial Performance Platform, connects planning, budgeting, forecasting, reporting, and consolidation as one seamless experience.