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Finance transformation often stalls before analytics or automation can deliver anything. The delay happens earlier, when people have to gather, reconcile, and trust the data those later steps depend on. In the Association for Financial Professionals’ 2025 FP&A Benchmarking Survey: Technology & Data, data reliability and data accessibility were among the most prominent obstacles respondents described. Those findings describe practitioner experience. They do not prove that data assembly is the only reason transformation projects stall.
What the 2025 AFP survey measured
The figures below come from AFP’s 2025 FP&A Benchmarking Survey: Technology & Data, as summarized in AFP’s January 14, 2025 press release. The survey was fielded in fall 2024 and drew 362 FP&A and finance practitioners from organizations of varying sizes around the world. AFP does not state a response rate or describe the sample as a representative probability sample, so every figure below describes survey respondents, not finance teams in general.
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| Measure | Result | How to read it |
|---|---|---|
| Respondents who said lack of data reliability posed a challenge | 61% | Share of survey respondents; not a population-wide prevalence estimate |
| Respondents who said lack of data accessibility held them back | 60% | Share of survey respondents; same limits as above |
| Respondents who used spreadsheets for planning daily or weekly | 96% | Reported use in the 2025 survey |
| Respondents who used spreadsheets for reporting daily or weekly | 93% | Reported use in the 2025 survey |
| Respondents who used EPM tools for planning at least quarterly | 71% | AFP notes that spreadsheet use stayed high alongside EPM use |
| Respondents who used AI in FP&A daily, weekly, or monthly | 23% | Survey-reported adoption as of fall 2024 fieldwork; not a measure of 2026 adoption |
| Respondents testing AI who planned to implement it within the next year | 40% | Stated plans from fall 2024 fieldwork; not confirmed implementation |
The survey also reports that more than half of respondents used at least eight categories of planning tools and ten types of reporting tools each quarter. AFP connects that tool sprawl to data challenges, including difficulty merging data. Because the fieldwork is from fall 2024, treat these numbers as a snapshot of that period rather than a current benchmark.
Why data assembly becomes the bottleneck
Data assembly is the unglamorous work between a business question and a usable answer. It includes pulling figures from the general ledger, subledgers, regional entities, planning files, and operational systems, then mapping their definitions, reconciling differences, and checking that the result is current enough to use. When any of those steps is slow or manual, every downstream tool inherits the delay.
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AFP’s release lists the leading reasons respondents gave for juggling multiple planning and reporting tools. Each one points back to assembly work.
Merging data across sources, systems, and geographies
The most direct friction is the inability to merge and analyze data from multiple sources, systems, and geographies. A regional entity may define a cost center differently, refresh its data on a different schedule, or keep it in a local system. Someone then has to align those differences by hand before a consolidated view can be trusted. This is our reading of how the survey’s findings play out in practice, not a figure AFP reports.
Legacy systems that have not been upgraded
AFP’s respondents also cited failure to upgrade legacy systems. Older ledgers and planning tools often lack the connectors, APIs, or data structures that newer platforms expect, so finance staff export files, reformat them, and upload them again. The work continues even when a newer tool sits alongside the old system.
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Lack of system integration is the third reason AFP lists. Where systems do not share master data or pass transactions automatically, the reconciliation burden stays with people. Integration gaps are often a larger obstacle than any single tool’s feature set, which is why a new platform rarely removes assembly work on its own.
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Too few decision-makers willing to use the tools
The fourth reason is less technical: too few decision-makers are willing to use the tools. If leaders still request figures by email or ask for a rebuilt spreadsheet, teams keep maintaining parallel versions of the truth. Adoption is therefore part of the assembly problem, not a separate concern.
Why spreadsheets and EPM coexist without solving the problem
The survey shows that spreadsheets and EPM are not alternatives in most finance teams. Nearly all respondents relied on spreadsheets for planning and reporting at least weekly, while most also used EPM tools for planning at least quarterly. AFP’s data supports that coexistence. It does not establish that EPM or spreadsheets cause the reliability and access problems respondents describe.
The pattern we see most clearly in the findings is additive. A team buys or adds a planning tool, but the inputs still arrive through unresolved connections, inconsistent definitions, and manual handoffs. The spreadsheet stays because it fills the gap between the new tool and the data it cannot reliably pull. Adding tools without addressing connectivity, definitions, ownership, and trust leaves the assembly work in place.
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These steps are practical recommendations drawn from the barriers above. They are not a tested methodology, and they do not promise a specific productivity or forecasting gain.
- Start from the decisions finance must support. List the decisions, such as monthly margin review or regional capacity planning, then identify the metrics and data each one requires. AFP describes actionable intelligence and fast decision-making as the goals of FP&A technology, so the decision is the reference point.
- Map sources, geographies, owners, definitions, and refresh timing. For each metric, record the source system, the entity or region it comes from, the person accountable for it, how it is defined, and how often it refreshes. Make lineage visible before choosing another platform.
- Set minimum validation and reconciliation rules. Define the checks each dataset must pass before it feeds a report, such as tying subledger totals to the ledger, and make exceptions visible rather than silently correcting them in a spreadsheet.
- Test whether the current environment can integrate and upgrade. Separate missing capabilities, such as no connector to a regional system, from process or ownership problems, such as two teams defining revenue differently. The fix for each is different.
- Evaluate tools against your actual constraints. Use the criteria in the next section before committing to a product category.
- Track whether assembly work actually shrinks. Measure manual reconciliation hours, data timeliness, and the share of decisions made from governed sources. If those measures do not change, the bottleneck has moved rather than disappeared.
Evaluating tools against assembly needs
AFP’s CEO, Jim Kaitz, framed the issue this way in the January 14, 2025 press release: “Technology, when implemented and upgraded properly and paired with skilled FP&A professionals, can have a significant impact on the success of an organization.” The phrase “implemented and upgraded properly” is the point. Tool selection matters less than whether the tool fits the systems and people already in place.
When comparing FP&A planning software, EPM platforms, finance data integration tools, or data governance tools, compare them on the same six axes:
| Criterion | Question to ask | What to check |
|---|---|---|
| Source connectivity | Can it connect to our actual systems and geographies? | Named connectors for each source, plus a test pull from the least standard regional system |
| Definitions, validation, and lineage | Does it show where each number came from and how it was checked? | Shared metric definitions, configurable validation rules, and visible lineage |
| Fit with existing workflows | Does it work with the EPM tools and spreadsheets we already use? | Import and export paths, and whether spreadsheet models can be retired or must keep running in parallel |
| Security, audit, and governance | Does it meet our access and control requirements? | Role-based access, audit trails, and change logs reviewed by finance control owners |
| Implementation and ownership | Who builds and maintains it after go-live? | Internal capacity, named data owners, and a realistic implementation plan |
| User adoption | Will business users actually work in it? | Pilot results with decision-makers, not just power users |
Gartner’s public abstract for its 2024 Hype Cycle for finance data and analytics governance says effective governance improves data quality, decision-making, and AI adoption. It also notes that finance leaders are investing in data cataloging, validation, and integration to improve quality and accessibility. That supports the governance and validation criteria above, although the abstract is a summary, not the full report.
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What the evidence does and does not establish
- The figures describe survey respondents from fall 2024, published in 2025. They are not a current benchmark for 2026.
- The survey is correlational. It shows that data concerns, spreadsheet use, and EPM use coexist, but it does not measure cause and effect.
- No independently sourced return-on-investment figure, universal transformation failure rate, or time-savings estimate from these sources is cited here, so none should be inferred.
- Gartner’s governance view is available only through its public abstract, so the detail behind its claims cannot be assessed.
The practical takeaway is narrower than a promise of transformation. If reliable, accessible data is not in place, new analytics and automation will inherit the same gaps. Start with the decisions, the data behind them, and the people who own that data.
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