Seventy-two percent of CFOs say FP&A applications play the most significant role in delivering value to their organizations. This clearly signals the importance of finance leadership in the strategic corporate hierarchy. FP&A is no longer viewed solely as a reporting and compliance function operating downstream of real decisions, but as a business partner who sits at the table when key decisions are made. FP&A professionals should be equipped with forward-looking analysis, scenario intelligence, and the kind of business partnership skills that shape strategy rather than simply record it.
Unfortunately, FP&A is coming to the table ill-prepared with legacy infrastructure and band-aided systems grown over time out of necessity. The 2025 FP&A Trends Survey, which synthesizes responses from thousands of finance practitioners across nine consecutive years of research, found that only 2% of organizations consider their FP&A teams fully optimized, with over 60% constrained by manual processes and inconsistent data. Nearly half of all FP&A working hours are spent on data collection and validation rather than on the strategic analysis that CFOs and board members explicitly demand.
The gap between what FP&A is expected to deliver and what its current tooling allows it to produce is not a skills problem. It is a structural one, and it is widening.
The Transformation Trap
Implementing a digital transformation has been the historical cure to bridge the structural gap between capabilities and expectations. This is presented through a multi-year, whole-stack modernization effort that aims to move FP&A from reactive reporting to a proactive strategic partnership. The theory is sound, but the execution record is considerably less so.
Most FP&A leaders who have lived through one of these initiatives can describe the process with uncomfortable precision.
- A platform is selected after months of vendor evaluation.
- A steering committee is formed.
A pilot launches with genuine organizational commitment.
Then things go off the rails:
- During implementation, data inconsistencies caused by accounting assumptions, reclassifications, and restructures, plus questionable operations data inconsistencies caused by manually entered data, or flawed capture methodology, force the need for workarounds, which will eventually lead to a “Phase 2” scope of work that, at best, is years away and, at worst, never materializes.
- Somewhere between the go-live date and the first quarterly close, the new system has to handle the process under real pressure, and the process fails, or a slew of manual intervention is required to make it work.
- The team reverts to the Excel models they know to validate the new system’s output, doubling the workload instead of reducing it.
- The budget still closes, the CFO still gets the scenarios, and maybe, over time, parallel processes will be eliminated to achieve the real results promised.
It’s not fun, it’s not efficient, but FP&A makes it work. The result: 29% of organizations take more than 10 days to finalize a single forecast cycle. Only 17% use fully driver-based models. Only 11% have fully aligned strategic, financial, and operational planning, and only 17% of organizations say that their data quality is good.
The future looks different. Gartner projects that by 2027, up to 90% of “what happened and why it happened” analysis in finance will be fully automated. The teams that are not building that capability incrementally today are not behind on a transformation initiative. They are falling behind on a competitive timeline.
The failure is not a lack of ambition; it is trying to bite off more than the organization can chew. FP&A teams see software as a silver bullet and attempt to modernize everything simultaneously. They then encounter the predictable weight of change management, governance decisions, and competing priorities, and make meaningful progress on none of it before momentum dissolves. The finance team’s aspiration remains intact, but the workload increases, expectations grow, and executive leadership’s understanding of the actual problem remains elusive.
Breaking Out of Transformation Purgatory
Start with a specific target in mind. The best FP&A teams demonstrating measurable progress in AI right now are not running transformation programs; they are identifying a single workflow that consumes the most time, causes the most friction, or produces the most errors, and fixing that one thing first using AI tools that are either already embedded in their existing stack or accessible without a six-month procurement process.
100% of FP&A professionals still use spreadsheets for planning and reporting at least quarterly. Stop trying to leave Excel behind; instead, identify which specific thing Excel is doing badly enough to justify replacing just that function, prove the improvement with data, and build the next move on that result.
The step-by-step framework that follows is built on exactly that principle. Each step names an FP&A pain point, identifies specific AI tools that may help address it, and has a measurable purpose that justifies the next investment of time and organizational energy. The goal is not a total digital transformation, but rather meaningful incremental changes that can be stacked. Stack enough of those together over twelve months, and the transformation narrative takes care of itself.
Excel's Role is Indispensable, But No Longer Sufficient
Excel does a lot of things right:
- It is flexible in ways that purpose-built software rarely matches.
- It is immediately accessible to every finance professional, with no training or onboarding required.
- It tolerates ambiguity in ways rigid systems do not, which makes it genuinely useful for ad hoc analysis, quick calculations, and exploratory modeling that lacks a defined process.
The AFP’s 2025 FP&A Benchmarking Survey found that 96% of respondents use spreadsheets for planning and 93% use them for daily or weekly reporting. These figures reflect genuine utility that no organization should ignore.
The problem is not Excel itself; it is what happens when FP&A teams use it as the operational backbone of workflows that have grown well beyond what a file-based system can reliably support. Five spreadsheet failures surface consistently across organizations of every size and sector:
- Absence of a real audit trail for assumption changes.
- Labor-intensive, manual consolidation delays that add days to every close cycle.
- Version-control breakdown that occurs when more than one analyst works on the same model.
- Fragile cross-file dependencies that collapse when a folder structure changes.
- Consistent scenario rebuild problems that force users to reconstruct models from scratch when an input, driver, or assumption shifts.
Each of these failures carries a measurable cost, with the two hitting FP&A hardest: version control and scenario rebuilding deserving closer examination.
The Version Control Problem
The version control failure in Excel is a hidden, creeping risk. It accumulates quietly, one saved copy at a time. A budget model gets distributed to eight business unit owners via email, then each returns their version with unreferenced edits. Someone on the FP&A team begins consolidating, discovers that two units used different headcount assumptions, and spends the better part of a day reconciling inputs that should never have diverged in the first place. By the time the consolidated model reaches the CFO, multiple versions of the file exist across users, and no one can state with certainty which numbers reflect the agreed assumptions and which reflect a late change that was never communicated upstream.
This is a very common scenario. More than 90% of spreadsheets in active organizational use contain errors. A single mislinked cell in a consolidation model can cascade into a materially incorrect number in an executive presentation, leading to unintended consequences. In addition, the absence of version control is an audit risk as much as an operational one.
The time spent finding, reconciling, and correcting errors directly feeds into the fact that nearly half of all FP&A working hours are spent on data collection and validation rather than analysis. Version control failures and the data preparation burden are not separate problems. They are the same problem in two different places.
The Scenario Rebuild Problem
When a CFO asks for three alternative scenarios before a board meeting, the request sounds reasonable and in a well-designed system built on driver-based logic, it is, but for most of FP&A professionals, the system is a manually maintained Excel model built around hard-coded relationships that requires a senior analyst to spend one to two days rebuilding the model, checking for broken links, validating that downstream calculations updated correctly, and producing output in a presentable format.
Only 9% of organizations have driver-based modeling fully automated, and twenty-one percent of FP&A teams cannot run scenario analyses at all. FP&A teams are generally highly sophisticated and technologically savvy, which means this analytical capability is not beyond them; rather, the tools they can leverage make it operationally prohibitive. This results in an FP&A function that is being asked to deliver real-time strategic insight while operating on a model architecture that requires days or weeks of manual labor to produce a single alternative view.
The Path Forward Is Not Wholesale Replacement
The challenge is identifying precisely which workflows have outgrown spreadsheets and replacing those specific functions with a tool designed to handle the load, while preserving the familiar interface that makes spreadsheet-based planning accessible in the first place. The most durable approach is surgical: replace the fragile infrastructure beneath while maintaining the spreadsheet experience above.
Farseer Sheets is built precisely on that principle, combining a familiar spreadsheet interface with an in-memory database that handles real-time collaboration, automated and manual end-user input, and on-the-fly assumption testing, without requiring analysts to change how they interact with data. Planning stays in one place, data fragmentation is eliminated at the structural level rather than managed manually, and permission settings ensure that business unit owners work from and return data to the same source. The version control problem is not managed; it is removed. Farseer’s client JGL reduced market template preparation and consolidation time by 50% using this approach, while CIOS eliminated spreadsheet chaos across 15 entities by centralizing planning within a single connected environment.
The following framework is built on the idea of precise, targeted replacement of workflows where Excel creates structural risk, rather than a wholesale migration that creates as much disruption as it resolves. The starting point, as with any credible process improvement effort, is knowing exactly where the time is going.
Five Stackable AI Moves That Create Quick Wins
Below is not a transformation roadmap; it is a sequenced set of decisions, each one designed to be executable within a single planning cycle, measurable against a clear before-and-after, and reversible if the results do not justify the next step. Reversibility matters. One reason large-scale FP&A modernization efforts fail is that they create irreversible organizational commitments before the value of the change has been demonstrated. The approach here is deliberately the opposite: prove it small, then scale what works.
Step 1: Map Where the Time Actually Goes
Before selecting any tool or targeting any workflow, FP&A teams need an honest accounting of where analyst hours are being spent. Most teams have a rough sense of this distribution, but very few have measured it precisely enough to make a defensible case for which workflow to target first.
I recommend doing a two-week time audit, tagging each task across the team as follows:
- Data wrangling – the process of cleaning, structuring, and enriching raw, complex data into a usable format for analysis.
- Model building – the process of creating a summary of a company’s financial position, typically in spreadsheet form, that can be used to calculate the impact of a future event or decision.
- Scenario analysis – a modeling technique used to predict potential business outcomes by evaluating how different sets of variables impact performance.
- Variance commentary – analyzes the difference between actual financial results and budgeted or forecasted figures to explain what happened, why it happened (root cause), how it impacts the full-year outlook, and recommendations for action.
- Stakeholder communication, or strategic work – sharing financial performance, risks, and strategies with key stakeholders.
- All else – Team building, collaboration sessions, administrative functions, etc…
Doing this will help to identify the largest time sink with enough specificity to act on it. Don’t overcomplicate this; make it simple. For example, create a shared time-tracking sheet that runs for 10 business days, and have everyone enter their time.
The output of this step is not a report. It is to target one workflow, with a measurable baseline that will allow the team to demonstrate improvement in concrete terms when a pilot concludes.
Purpose: A documented before-state that turns the subsequent AI pilot from a technology experiment into a business case.
Step 2: Exhaust What You Already Own Before Buying Anything
Most FP&A teams do not fully leverage the technology they have. Before looking to an outside vendor, a systematic audit of the existing technology stack almost always reveals usable tools.
Using the ubiquitous Microsoft 365, most office workers now have access to Copilot capabilities in Excel that can automate pattern detection, surface anomalies in datasets, and generate first-draft variance narratives from structured data. For more sophisticated users, Power BI with Copilot integration can automatically refresh dashboards and generate natural-language explanations of variance drivers without requiring a data science team to build the logic. Most modern ERP systems, including SAP, Oracle, and NetSuite, now embed AI-assisted variance flagging and consolidation features that finance teams routinely leave unused because no one configured them at implementation.
Purpose: At least one AI capability activated at zero additional cost, demonstrating to stakeholders that the team can generate value from technology investment already on the balance sheet.
Step 3: Replace the Infrastructure, Not the Interface
As previously stated, the solution is rarely a wholesale platform migration. It is targeted infrastructure replacement or enhanced utilization that preserves the spreadsheet experience while removing the structural fragility underneath.
Both the version control and scenario modelling problems can be mitigated by more effectively leveraging common ERP solutions that many organizations already have, such as Farseer, Vena Solutions, Workday Adaptive Planning, and Anaplan. Each allows assumption changes to propagate automatically through an entire model, converting a two-day rebuild into a minutes-long recalculation. Match the tool’s capabilities to the specific failure, not to the broadest possible feature set or the most prominent brand in the market.
Purpose: For the first forecast cycle or scenario request that does not require a manual consolidation run or a model rebuild from scratch, quantify the time recovered multiplied by an assumed hourly rate.
Step 4: Run a 30-Day Pilot With a Defined Finish Line
AI pilots fail in FP&A for a consistent reason: teams do not define success before they start. Without a specific, measurable target, the pilot drifts into a general assessment of whether the tool feels better than its predecessor.
Before activating any new tool, define three things.
- What is the baseline metric, for example, the number of hours spent on monthly consolidation in the previous three cycles?
- The target improvement, for example, is a 40% reduction in cycle time within 30 days.
- Define the decision rule, meaning the threshold at which the team will commit to expanding the tool to additional workflows or business units, or look to outside providers for larger scale.
Run the pilot on a single workflow with a single team and insulate it from competing priorities as much as possible. Assign an internal champion who is responsible for tracking results and surfacing friction points, because the friction points in the first 30 days are as valuable as the wins, as they reveal where the process needs to change, not just where a tool needs to be configured.
Purpose: A well-documented before-and-after on one workflow. This is the internal business case for Step 5, and it is more persuasive than any vendor ROI calculator.
Step 5: Measure the Return, Then Pick the Next Target
Standard ROI framing in technology adoption typically focuses on cost reduction or incremental revenue achieved. These measures underestimate the value generated by FP&A AI tools and make it harder to justify continued investment, so don’t use them. Instead, use a more complete measurement that includes four dimensions.
- Time recovered, expressed in analyst hours per cycle.
- Accuracy improvement, measured as forecast error reduction across comparable periods.
- Cycle time compression is the reduction in the number of days from data cutoff to executive-ready output.
- Strategic capacity created, meaning the hours formerly consumed by data preparation that are now available for business partnering, scenario analysis, and forward-looking work.
That last dimension is the biggest, the one senior leadership should care most about, but is also the hardest to value. For example, what is the value of one additional scenario or one more piece of information? The value can be both priceless and worthless at the same time, as it will depend on if the incremental analysis causes a decision to be made, risk avoided, or opportunity captured.
Purpose: A measurement framework that converts individual workflow improvements into an ongoing investment case, removing the need to relitigate the value of AI adoption with each new initiative.
From Quick Wins to Strategic Advantage
The FP&A function is at an inflection point that is more consequential than most transformation narratives acknowledge. 45% of FP&A teams are recognized by senior leadership as business partners, but only 9% believe they are acting as true strategic advisors.
By working through the five steps above, an FP&A team can redefine what the FP&A function is capable of and further its contribution to organizational decision-making. The gap between recognition and genuine strategic influence is not a talent gap. It is a capacity gap, and capacity is exactly what targeted AI adoption recovers.
Starting small does not mean an FP&A AI initiative should stay small. It builds evidence that a better result for the business is possible, and a more credible mandate for the next improvement. That compounding is the actual mechanism of financial digital transformation. It is the quiet accumulation of problems solved, time recovered, and trust built with the business units and executives who now receive better analysis faster than they did before.
The FP&A teams that lead AI implementations are not waiting for the next big digital transformation program. They identified the workflow that was costing them the most time, found the tool(s) that specifically address it, ran a short pilot with a defined finish line, measured the results honestly, and picked the next target. Racking up small, stackable AI wins over time is what will drive success.
FAQ
1. Why do most FP&A digital transformation initiatives fail to deliver lasting results?
Most large-scale FP&A transformation programs fail because they attempt to modernize everything at once. Multi-year implementations introduce change management complexity, governance conflicts, and infrastructure challenges that overwhelm teams before measurable value is delivered.
Instead of solving one structural problem at a time, organizations pursue wholesale system replacement. This often results in parallel processes, continued spreadsheet validation, and increased workload rather than reduced friction. The issue isn’t lack of ambition — it’s scope overload.
2. If Excel is still widely used in FP&A, what exactly is the problem?
Excel itself isn’t the problem — it remains flexible, accessible, and powerful. The issue arises when it becomes the operational backbone for complex, multi-entity planning processes it wasn’t designed to support.
The most common structural failures include:
- Version control breakdowns
- Manual consolidations that delay forecasting cycles
- Fragile cross-file dependencies
- Lack of audit trail for assumption changes
- Time-consuming scenario rebuilds
These failures create measurable costs in time, error risk, and lost strategic capacity.
3. What is the fastest way for an FP&A team to start using AI effectively?
Start small and target a single workflow.
Rather than launching a transformation program, identify one high-friction process — such as monthly consolidation, variance commentary, or scenario modeling — and improve that workflow within one planning cycle.
Begin with tools you already own (e.g., Microsoft Copilot, Power BI, AI features embedded in your ERP). Run a defined 30-day pilot with measurable success criteria. Prove value before expanding.
4. How should FP&A measure ROI from AI initiatives?
Traditional ROI models (cost reduction or revenue increase) underestimate the value of AI in FP&A. A better measurement framework includes four dimensions:
- Time recovered (analyst hours saved per cycle)
- Accuracy improvement (forecast error reduction)
- Cycle time compression (faster close/forecast delivery)
- Strategic capacity created (hours reallocated to analysis and business partnering)
The most important metric is often the last one — increased capacity for forward-looking, decision-driving work.
5. Do we need to replace our entire planning system to modernize FP&A?
No. Wholesale replacement is rarely necessary — or advisable.
The most durable approach is surgical: replace fragile infrastructure beneath the spreadsheet interface while preserving the familiar planning experience finance teams rely on. Modern planning platforms (such as Farseer, Vena, Adaptive, or Anaplan) allow real-time collaboration, centralized data control, and automated scenario recalculation without forcing analysts to abandon spreadsheet workflows.
Modernization works best when it is incremental, reversible, and stackable — not disruptive and all-at-once.
