Key takeaways:
Picture the scene. It's the week before the forecast is due and half the team is still reconciling data across five different data silos. Someone is cross-referencing last month's model against a manually updated actuals file. Another analyst is on their third round of chasing down numbers from a business unit that uses a completely different template.
Sound familiar?
The people doing this work are talented, analytically astute, and capable of far more than the tasks they're currently drowning in. But the problem with the manual effort above is structural — one built into how your FP&A processes were designed, and one that compounds quietly over time.
The cost of manual FP&A workflows often shows up in multiple places, whether in decisions that take too long, in teams that are stretched too thin, or in strategic opportunities that get missed because the analysis came too late.
Putting a number on the problem
Research by the University of Baltimore and DataRails puts a striking dollar figure on what inefficient FP&A actually costs: $7.8 billion a year across U.S. companies. That breaks down into $6.1 billion in lost productivity — high-value analysts burning hours on low-value data management work — and $1.7 billion in innovation that simply never happens, the kind of forward-looking analysis that leads to breakthroughs like Amazon Prime. The number makes the abstract concrete: this isn't a workflow inconvenience, it's a measurable drag on business value.
Consider what that means in practice. A team of five analysts, each working a 40-hour week, might collectively generate fewer than 50 hours of genuine analytical output — with the remaining 150-plus hours absorbed by process overhead that, in many cases, could be automated.
The concept of FP&A productivity reframes how we think about this. It's not about headcount efficiency — hiring fewer people to do the same amount of work. It's about strategic yield and value creation per analyst hour. How much strategic value is your team generating relative to the time they have available? When that ratio is low, the issue isn't the size of the team, it's how the team's time is being consumed.
And every hour an analyst spends reconciling data is an hour not spent on scenario modeling, building relationships with business unit partners, and providing the kind of forward-looking guidance that gets finance a seat at the strategic table.
Understanding this cost — and what to do about it — is increasingly central to any serious conversation about FP&A transformation.
Why the manual work persists and why it compounds
If the cost of manual FP&A is this visible, why haven't more teams solved it? The answer has nothing to do with the team and more to do with a combination of structural barriers, including:
- Legacy systems that weren't designed to talk to each other
- Data that lives in siloed platforms across finance, HR, sales, and operations — each with its own format, cadence, and owner
- Spreadsheet dependencies built over years that have become load-bearing infrastructure nobody wants to touch
- AI features bolted on to existing tools as an afterthought, rather than being architected into the foundation of the planning system itself
What compounds the problem is scale. A manual process that absorbs 60% of a five-person team's time becomes even more burdensome as the business grows and the volume of data, entities, and planning scenarios multiplies. Headcount rarely scales in proportion to this complexity — which means the burden per analyst actually increases over time, not decreases. Financial planning automation isn't just about productivity; it's also about scalability.
What AI-driven workflows change
So now we've established the challenge, but what does an AI-driven FP&A workflow look like in practice? Here are some of the key capabilities:
Data aggregation: Rather than analysts manually pulling, cleaning, and consolidating data from multiple sources, AI-driven systems handle integration automatically, surfacing a clean, unified data set that's ready for analysis, not for prep work.
Anomaly detection: Instead of analysts manually reviewing variances and trying to spot the signal in the noise, AI flags the numbers that warrant attention, accelerating the review process, and reducing the risk of costly blind spots and forecast misses.
Dynamic scenario generation: Rather than building each scenario by hand — a time-intensive process that limits how many options finance can realistically model — AI-enabled planning platforms generate multiple scenario outputs in a fraction of the time, allowing analysts to stress-test assumptions and present leadership with a richer, more responsive set of options.
Natural language querying of financial data: Business partners can get quick answers without requiring analyst time for every ad hoc request, freeing the FP&A team for higher-complexity analytical work.
All these capabilities require a shift in the analyst's role, from data wrangler to strategic business interpreter. AI isn't replacing finance judgment to operate. Human oversight remains central to how AI-driven FP&A works in practice.
The downstream effect on strategic finance
When manual work is automated, finance teams don't just get time back — they get influence back.
Forecast cycles become faster, not because analysts are working harder, but because the data infrastructure supports a shorter loop between inputs and insights. Scenario modeling becomes more responsive because the heavy lifting of building models isn't the rate-limiting step. Business unit partnerships deepen because finance has the capacity to engage proactively rather than reactively.
And at the CFO level, all of this translates into something that matters enormously: predictable performance and defensibility in the boardroom. A finance function that consistently arrives with fresh data, multiple scenarios, and a clearly defended recommendation carries far more weight than one that arrives with last month's report and a promise to model the alternatives in two weeks.
The teams that make this shift aren't just more productive. They're more valuable to the business and more difficult to sideline when strategic decisions are being made.
"With Anaplan we got the best of both worlds: automation with flexibility. That's huge for a large, complex organization like ours."
— Tim Kay, Director of Financial Planning and Analysis, Lippert
Deploying finance talent where it creates the most value
The analytical skills, business judgment, and stakeholder relationships that talented FP&A teams bring to an organization are irreplaceable. The question is where that talent is deployed. Right now, too much of it is being consumed by work that technology can handle.
The teams that address this structural problem now are building something beyond short-term productivity gains. They're building a structural advantage in how fast and confidently they can guide the business — and in how much influence finance has at the table when it matters most. That advantage compounds over time, just as the manual burden compounds when it's left unaddressed.
Anaplan changes that with a next-generation planning platform built with AI at the core, giving finance teams the intelligence, flexibility, and automation needed to move faster. With out-of-the-box applications designed to accelerate time-to-value, we enable your finance team to spend less time managing the mechanics of planning and more time shaping decisions that protect margins and drive profitable growth. And that impact is measurable: A commissioned Forrester Consulting Total Economic Impact™ study found that a composite organization using Anaplan achieved a 152% return on investment (ROI), $11.5 million in net present value, and payback in less than six months.
