Finance automation stalls are often diagnosed too narrowly when spreadsheets are treated as the cause rather than as one visible part of a larger workflow. In PwC’s 2025 Global Commodity Trading Survey, respondents cited manual processes, close work and fragmented IT as obstacles to finance efficiency. Those findings are specific to commodity trading, but they point to a useful diagnostic: look across the process, systems, data and ownership before choosing a tool.
What the reported obstacles actually show
PwC’s 2025 Global Commodity Trading Survey asked commodity trading respondents about finance operational efficiency. In that population, 47% identified too many manual processes as an obstacle, 30% cited time spent on month-end or year-end reporting, and 23% cited a fragmented IT landscape. These are survey responses from one industry, not a representative measure of every finance team.
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The three obstacles describe different parts of a workflow. Manual work may occur at handoffs or during exception handling; close effort may reflect reconciliation and review; fragmented systems may require duplicate entry or make reliable inputs harder to assemble. A spreadsheet can surface these problems, but the survey does not establish that spreadsheets caused them.
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A spreadsheet can be a practical local workaround when systems do not exchange information cleanly, process ownership is unclear, or inputs need review before they can be used. In that situation, replacing the spreadsheet without fixing the upstream handoff may simply move manual work elsewhere. This is an interpretation of the reported combination of manual-process and fragmented-IT obstacles, not a causal finding measured by PwC.
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Nor do the available benchmarks establish how much finance work remains in spreadsheets across industries. They do not support a universal spreadsheet-error rate or a claim that spreadsheets are the principal cause of finance automation gaps. Diagnose the task the spreadsheet is performing: capturing data, transforming it, reconciling accounts, routing approvals, or tracking exceptions. Each points to a different process or control issue.
Automation is a process change as well as a technology choice
In the same PwC survey, respondents named several finance “game-changers” from the prior two years: technology implementation (21%), end-to-end process streamlining (18%), and a change to a new ERP system (12%). Technology examples included AI, cloud computing, visualizations and robotic process automation (RPA). These are reported choices, not controlled evidence that one approach caused better results.
The figures matter because they put technology alongside process redesign and system change. A tool can automate a stable, well-defined task; it cannot by itself decide which version of a process is authoritative, make unreliable inputs dependable, or assign responsibility for exceptions. 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 quantified guarantee for a particular project.
How to tell whether an automation effort is ready to scale
Putting automation into use is different from operating an optimized process at scale. APQC’s finance-automation material identifies unreliable data, insufficient infrastructure and employee skills gaps among reported scaling barriers. Its public material does not establish a universal rate of automation across finance, so use the barriers as diagnostic prompts rather than as a scorecard.
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- Workflow and process: Name the end-to-end flow in scope—such as record-to-report, procure-to-pay, order-to-cash, close, reconciliation, or planning and forecasting. Map where information enters, changes hands and requires human judgment.
- Maturity: State whether the work is a pilot, an implemented automation, or an optimized process operating at scale. A live tool does not mean the whole process is automated.
- Data and controls: Check whether inputs are dependable, exceptions are visible, audit trails are retained, and approval rules remain intact. Automation should not obscure who authorized a transaction or how an exception was resolved.
- Systems and integration: Identify ERP and surrounding-system constraints, handoffs and duplicate entry. If the workflow crosses applications, determine where information must be reconciled or re-entered.
- People and ownership: Assign a process owner and decide who will monitor the automation, resolve exceptions and maintain the needed skills.
- Business outcome: Choose a baseline and a target before selecting technology. Cycle time, exception rate, visibility and close duration are useful candidate measures; they are evaluation recommendations, not outcomes established by the cited surveys.
How to choose a first workflow
Begin with a repeatable workflow that has a clear boundary and owner, rather than with the most conspicuous spreadsheet. Map its inputs, handoffs, approvals, controls and exceptions; then establish the baseline measure the business wants to improve. This sequence is an operating recommendation, not a tested prescription from the surveys.
- Define the workflow: Specify the start and end points, participating systems, accountable owner, and what counts as a completed transaction.
- Map normal work and exceptions: Separate routine steps from judgment, rework and control checks. Record where people rekey information or reconcile conflicting records.
- Check readiness: Verify data quality, integration constraints, auditability, infrastructure and the capacity to handle exceptions. Address a blocking weakness before automating around it.
- Set a measurable objective: Record the current cycle time, exception rate, close duration or another relevant operating measure, then define how and when it will be assessed.
- Compare interventions: Consider process standardization, system integration or ERP change, RPA, and other technology only against the workflow’s actual constraint. Preserve approvals and audit trails as part of the design.
- Review after implementation: Check the chosen measure and control outcomes, and determine whether the process is still a pilot, merely implemented, or operating reliably at scale.
What broader automation surveys can—and cannot—tell finance leaders
Other surveys show that automation and AI are prominent business priorities, but they measure different things and should not be combined into a single estimate of finance automation.
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- Gartner reported that 58% of finance functions were using AI in 2024. Reported use cases included intelligent process automation, anomaly and error detection, and analytics. AI use is not the same measure as end-to-end workflow automation or spreadsheet reduction.
- The Federal Reserve Bank of Richmond reported in 2024 that 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. These findings concern firms’ automation priorities and motivations, not finance-only adoption.
- Zuora’s commissioned survey, conducted in May 2025 among 991 finance and accounting leaders in North America, the UK and France, found that 79% said manual work remained a top challenge. That result reflects this survey’s specified population and scope; it is not a cross-industry measure of work done in spreadsheets.
Taken together, the surveys describe interest in automation and persistent process friction, but they do not prove that a particular technology will deliver a particular return. A finance leader should use benchmarks to frame questions, not to substitute for a local workflow baseline.
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