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Finance automation stalls are often visible in spreadsheets, but a spreadsheet is not necessarily the root cause. The better question is whether the end-to-end workflow, its systems, data, controls, and ownership are ready to operate with less manual work.
In PwC’s 2025 Global Commodity Trading Survey, respondents cited too many manual processes (47%), time spent on month-end and year-end reporting (30%), and a fragmented IT landscape (23%) as obstacles to finance operational efficiency. Those figures describe commodity trading respondents, not finance teams generally; they do, however, show why blaming one tool can miss the wider workflow.
Why the spreadsheet is often a symptom, not the diagnosis
A spreadsheet can be where a finance team sees manual work most clearly: people copy figures between systems, reconcile mismatched records, or track approvals outside a core application. But that visibility does not establish that the spreadsheet caused the underlying problem. The work may be manual because systems do not connect, source data is unreliable, a process has too many handoffs, or nobody owns exceptions from beginning to end.
PwC’s commodity trading survey lists manual processes, close work, and fragmented IT as separate obstacles. APQC’s finance-automation material also identifies unreliable data, insufficient infrastructure, and employee skills gaps as barriers to scaling automation. Taken together, these findings support a broader diagnosis: a spreadsheet may expose a workflow gap without explaining it. The sources do not establish what share of finance work is spreadsheet-based across industries, or prove that spreadsheets are the principal cause of automation problems.
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Automation involves process change as well as technology
Technology matters, but it is only one kind of intervention. In the same PwC survey, respondents named technology implementation as a finance “game-changer” in the prior two years (21%); examples included AI, cloud computing, visualizations, and robotic process automation (RPA). They also cited end-to-end process streamlining (18%) and a change to a new ERP system (12%). These are reported views, not controlled comparisons showing that one approach caused better results.
The distinction matters because installing a tool does not automatically simplify the work around it. A new automation can inherit inconsistent inputs, preserve unnecessary approvals, or move the manual effort to exception handling. PwC’s finance transformation guidance says standardization and automation can support real-time transaction processing, reduce or eliminate manual reconciliations, and shorten close cycles. That is professional guidance, not a guaranteed or quantified outcome for every organization.
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What the wider automation benchmarks do—and do not—say
Several surveys indicate that organizations are pursuing automation, but they measure different populations and activities. They should not be combined into one estimate of finance automation maturity.
| Source and scope | Reported finding | What it measures |
|---|---|---|
| PwC, Global Commodity Trading Survey 2025 | 47% cited too many manual processes; 30% cited time spent on month-end/year-end reporting; 23% cited fragmented IT. | Obstacles to finance operational efficiency among commodity trading survey respondents. |
| PwC, Global Commodity Trading Survey 2025 | 21% named technology implementation, 18% end-to-end process streamlining, and 12% a new ERP system as finance “game-changers” in the prior two years. | Respondents’ reported initiatives, not comparative proof of impact. |
| Gartner, September 11, 2024 | 58% of finance functions were using AI in 2024. | AI use in finance functions. Gartner cited intelligent process automation, anomaly and error detection, and analytics among use cases; AI use is not the same as end-to-end process automation. |
| Federal Reserve Bank of Richmond, June 20, 2024 | Nearly two-thirds of CFOs said their company had a strategic priority to automate tasks typically performed by employees; nearly 88% of firms named improving business processes as their main automation motivation. | Company-wide automation priorities and motivations, not finance-team-only adoption. |
| Zuora, commissioned survey conducted in May 2025 | 79% said manual work remained a top challenge. | Survey of 991 finance and accounting leaders in North America, the UK, and France. |
The table describes stated usage, obstacles, priorities, and challenges—not the proportion of finance work automated, spreadsheet use, or realized return on investment. APQC’s public material discusses automation maturity and scaling barriers, but detailed report data may be member-only; no additional percentage can be inferred from that material.
How to assess an automation option
Compare projects by the work they change and the conditions needed to make that change stick, rather than ranking technologies in the abstract. Before choosing a tool, map a repeatable workflow from input to completion, including handoffs, decisions, exceptions, and controls. This is a practical recommendation, not a sequence experimentally validated by the cited surveys.
1. Define the workflow boundary
Name the process and its start and end points: for example, record-to-report, procure-to-pay, order-to-cash, close, reconciliation, or planning and forecasting. A narrow, shared definition makes it possible to see where information is entered, transferred, approved, or corrected—and to avoid automating only one isolated step while leaving the rest of the work untouched.
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2. Establish the maturity stage
Distinguish a pilot from an automation in regular use, and both from an optimized process operating at scale. APQC’s maturity framing is useful here: getting automation into use is not the same as scaling and optimizing it. Treat an implemented bot or workflow as a milestone, not evidence that the entire process is automated.
3. Check data, exceptions, and controls
Confirm that input data is dependable and that the design makes exceptions visible. Identify where an audit trail is required and ensure approval and control rules remain intact. If people must still investigate mismatches, decide who handles them and how the work returns to the process; otherwise, automation can obscure rather than resolve the source of manual effort.
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4. Map system dependencies and ownership
List the ERP and other systems involved, integration limits, handoffs, and duplicate entry. Then name the process owner, the people responsible for maintaining the automation, and the staff who will resolve exceptions. Infrastructure and employee skills are among the barriers APQC identifies in scaling finance automation, so both belong in the assessment rather than being deferred until after implementation.
5. Set a business measure before choosing a tool
Define what improvement would count for the specific workflow. Possible measures include cycle time, exception rate, visibility, or close duration; these are evaluation recommendations, not results reported by the cited sources. Record a baseline and decide how controls will be checked alongside the operating measure. That makes it easier to tell whether a project changed the work, rather than merely added a tool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to take to the next automation review
Bring one owned, clearly bounded workflow—not just a request to eliminate spreadsheets. Document its handoffs, systems, data dependencies, exceptions, and controls; identify the maturity stage; and agree on a baseline and a target measure. Then compare interventions against those conditions. The evidence supports examining process streamlining, systems, data, infrastructure, and skills alongside technology, but it does not promise savings or prove that a particular intervention will deliver them.
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