Survey Reveals Midsize Companies Now Use AI for FP&A

Survey reveals midsize companies now use AI for FP&A at a level that has moved beyond experimentation and into everyday finance operations. This reflects a broader shift toward AI-powered finance workflows. As adoption increases, finance teams are using AI not only to automate manual tasks but also to improve planning accuracy, accelerate analysis, and free up time for more strategic FP&A activities.

Midsize companies now use AI for FP&A at a scale that would have seemed unlikely just two years ago. According to a survey of 431 finance professionals conducted in partnership with research firm Benchmarkit, AI adoption in FP&A has jumped from 57% in early 2025 to 86% today. That is not a marginal increase, it is a near-total reversal of how finance teams view automation, forecasting, and reporting.

Why Midsize Companies Use AI for FP&A

The survey points to executive sponsorship as one of the clearest drivers behind this shift. 

• Seventy percent of respondents said their organization has a C-level mandate requiring the finance function to use AI; 

• And 34% reported they have already fully integrated AI agents across their FP&A workflows.

• Another 16% said that integration now extends beyond FP&A into other parts of the business.

This top-down push helps explain why midsize companies are adopting AI at rates comparable to (and in some cases faster than) much larger enterprises. Smaller finance teams often carry heavier workloads per person, so the appeal of automating repetitive forecasting, variance analysis and reporting tasks is especially strong. With leadership setting AI adoption as a corporate priority, finance teams have both the mandate and the incentive to move quickly.

How Widespread Is AI Adoption in FP&A Today?

Breaking down the numbers further, the report found that AI agents are now embedded in FP&A workflows to varying degrees:

• 14% of respondents say AI isn’t used at all in their FP&A workflows

• 15% are testing one or two use cases

• 22% have AI embedded in select workflows

• 34% have AI fully integrated across FP&A

• 16% have extended that integration to other parts of the business

Looking ahead, respondents expect this trend to accelerate. Thirty-seven percent believe at least half of their current FP&A workflows will be run by AI agents within the next two years, and 36% plan to invest specifically in AI tools for forecasting and analysis over the next 12 months. Whether that timeline holds up in practice remains an open question. Other industry research has suggested that companies sometimes overestimate how quickly agentic AI will deliver results. But the direction of travel is unmistakable.

How AI Is Transforming FP&A Teams’ Talent Strategy

As AI takes on more of the manual workload, FP&A teams are reshaping who they hire. The most common new roles being introduced are Financial Systems Manager or EPM Architect (cited by 40% of respondents), followed by FP&A Data Scientist and FP&A Analytics Manager (32% each). These hires reflect a deliberate move to build out the technical backbone needed to support AI-powered financial reporting and more sophisticated data management.

That said, many teams are still building this capability from scratch. Survey respondents described newly formed FP&A functions still defining roles and processes, staff juggling FP&A duties alongside other responsibilities, uneven skill distribution, and hiring freezes that limit how quickly teams can scale up their AI-readiness.

Closing the Strategic Perception Gap in AI for FP&A

One of the more telling findings has less to do with technology and more to do with perception. 

• Despite the surge in AI adoption in FP&A, only 31% of survey respondents said their executive leadership views the FP&A function as a “strategic business partner”.

• Sixty-one percent said leadership still sees FP&A as either a transactional reporting function or a reliable advisor on financials.

• Just 9% said leadership considers FP&A a “critical driver of growth”.

This gap matters because freeing up FP&A’s time is the whole point of adopting AI in the first place. If AI agents can absorb the manual work of data gathering, forecasting, and variance analysis, the logical next step is for FP&A professionals to spend more time on judgment-intensive, forward-looking analysis. But that shift in time allocation does not automatically translate into a shift in how the rest of the business perceives the function. Closing that perception gap will likely depend on how visibly FP&A teams put their newly freed-up time to strategic use.

How Finance Teams Automate Workflows With AI and What’s Still Missing

Adopting AI tools for FP&A teams is only part of the equation. The report’s authors caution that automation alone will not deliver real value unless it is paired with a solid data foundation. Despite high AI adoption numbers, more than half of respondents (51%) said they have only moderate or limited integration between their FP&A tools and core source systems such as ERP, CRM, HRIS, and BI platforms. Seven percent said they rely entirely on manual data uploads.

This disconnect is reflected in how finance teams rank their own challenges. Data quality and availability remain the single biggest reported bottleneck in FP&A, cited by 58% of respondents, virtually unchanged from the previous year’s report. Without governed, well-structured data feeding into AI workflows, even the most advanced automation can produce unreliable outputs.

The report puts it plainly: to get real value from AI, finance teams need more than automation. They need governed data, connected workflows and enough oversight to trust the decisions AI helps accelerate. That combination, more than the AI tools themselves, is what separates teams that are merely experimenting from those genuinely building next-generation FP&A capabilities.

What This Means for Modern FP&A Workflows Going Forward

The bigger story here extends beyond any one vendor’s survey. Midmarket AI adoption is accelerating across finance functions broadly, and FP&A has become one of the clearest proof points. As more midsize companies adopt AI for budgeting, forecasting and reporting, the competitive bar for what counts as a “modern” FP&A workflow keeps rising.

For finance leaders evaluating their own roadmap, the lesson from this survey on AI adoption in FP&A is twofold.

• First, AI adoption itself is no longer a differentiator. Most peer organizations are already there.

• Second, the real differentiator is execution: clean data, integrated systems and a finance team equipped to turn AI-generated insight into timely action.

Companies that get both halves right will be the ones that actually realize the productivity gains that so many respondents anticipate over the next two years.