How to measure ROI from finance AI before scaling it

Imagine you’ve invested in AI workflows and they’re doing exactly what you hoped they would. Your team now spends less time on manual tasks, processes run more smoothly and employees have more time for higher-value work.

But can you put a dollar value on those improvements? That’s where things get tricky.

It’s a common predicament for finance teams. According to PEX’s 2026 State of Finance report, only 5% of respondents say they can clearly measure ROI from AI, while another 13% believe they’re seeing a return and 38% say it’s too early to tell. 

That pressure grows as adoption matures. Among organizations with $50 million to $250 million in revenue, 11% name difficulty proving ROI as their biggest barrier to expanding AI, compared with 4% of all respondents. Once teams have deployed AI, the question shifts from whether it works to whether it pays. 

By tracking time savings, translating those gains into financial value and comparing them against investment costs, finance leaders can build a stronger business case for scaling AI.

The key to measuring ROI: establish a baseline

No one wants to spend more time tracking how much time they’re spending on manual work. But without a clear picture of where those hours go today, it’s difficult to prove how much time AI saves tomorrow. And those hours add up quickly: 31% of finance teams spend more than 20 hours a month on receipts, coding and reconciliation. 

Start by documenting the work involved in your existing processes. For each workflow, establish a baseline that includes:

• Time spent: How many hours do finance and non-finance employees spend completing the work each month?
• Labor costs: Which employees are involved, and what are their fully loaded hourly labor costs?
• Existing software costs: What are you currently paying for tools that AI could replace or consolidate?
• Manual intervention: What percentage of transactions require manual review, correction or follow-up?
• Transaction volume: How many transactions are processed monthly? This provides context for calculating the time and cost per transaction.

Together, these measures establish the current cost of the workflow and provide a starting point for evaluating the financial impact of AI.

Implement and track an AI workflow

With your baseline established, it’s time to put AI to work. Start with a defined workflow, such as transaction coding or receipt collection, where you can measure the impact against your existing process.

Focus on two steps:

• Define what AI will handle: Identify the specific tasks where you will insert AI and where human review or intervention will still be required. 
• Track the same metrics: Collect data over a representative period, accounting for changes in workload or staffing so you can make a meaningful comparison against your baseline.

Depending on the workflow, you might see improvements in areas such as:

• Transaction processing: Fewer hours spent collecting receipts, categorizing transactions and preparing expense records.
• Review and follow-up: A lower percentage of transactions requiring manual review, along with less time spent requesting documentation and resolving exceptions.
• Reconciliation and close: Fewer hours spent reconciling expenses, correcting errors and preparing financial reports.
• Employee administrative work: Less time spent by employees outside finance submitting receipts, organizing documentation and resolving expense issues.

PEX’s research reinforces the value of tracking these improvements. Among teams piloting or using AI, 69% report reduced manual review and 51% report shorter close cycles. Measuring similar changes in your own workflows provides the evidence you need to calculate the financial return on your AI investment.

Translate performance into financial value

Here’s how to turn the metrics you’ve been tracking into a business case for further investment.

1. Calculate hours saved by role. Determine how many hours AI saves each month for each type of employee involved in the workflow. For example, an accounts payable specialist might save 20 hours per month, while a controller saves eight hours. Multiply those figures by 12 to calculate annual savings.

2. Determine hourly labor costs. Calculate the fully loaded hourly cost for each role, including salary, benefits and employer-paid taxes. For example, the accounts payable specialist might cost $45 per hour and the controller $90 per hour.

3. Calculate the financial value of time saved. Multiply the annual hours saved for each role by its hourly labor cost, then add the results together. Using our example:

• Accounts payable specialist: 240 hours × $45 = $10,800
• Controller: 96 hours × $90 = $8,640
• Total annual productivity value: $19,440

4. Account for investment costs. Include software licenses and platform fees, implementation, training and ongoing maintenance. For example, an AI solution might cost $3,000 annually, with another $1,000 in one-time implementation costs. Subtract any existing software expenses that the new solution eliminates.

5. Consider avoided hiring costs. If AI allows your organization to handle growing transaction volumes without adding staff, you may be able to include avoided hiring as an additional financial benefit. Just be careful not to count the same labor savings twice.

With these figures, you can calculate both net financial benefit and ROI:

Net financial benefit = Total financial benefits − Total investment costs

In our example, the estimated annual productivity value is $19,440, with first-year investment costs of $4,000.

Net financial benefit = $19,440 − $4,000 = $15,440 

ROI = [Net financial benefit ÷ Total investment costs] × 100

Using the estimated productivity value, our illustrative ROI is:

($15,440 ÷ $4,000) × 100 = 386%

Keep in mind that this is a productivity-based ROI estimate, not necessarily a cash return. Time saved doesn’t automatically translate into reduced payroll expenses. It may instead free employees to focus on higher-value work. Distinguishing between productivity value and actual cost savings makes your calculations more credible.

Use ROI benchmarks to assess AI’s impact

You’ve calculated the potential value of your AI investment and estimated its ROI. Now what? How do you know whether it’s good enough to justify expanding AI into other workflows?

There’s no universal ROI percentage that signals it’s time to scale. Instead, evaluate your results against four benchmarks:

• Your organization’s ROI target: Does the return meet the minimum threshold your finance team uses to approve other technology investments? If it exceeds it, by how much?
• Payback period: How long will it take for the financial benefits to cover the initial and ongoing investment costs? A shorter payback period means you’ll recover your investment sooner.
• Consistency of results: Are the time savings sustained over several reporting periods, or did they reflect a temporary improvement?
• Capacity for growth: Can the workflow handle increasing transaction volumes without requiring proportional increases in staff time or costs?

PEX’s research found that the share of organizations reporting ROI, whether measured or perceived, rises from 3% at the crawl stage to 25% at walk and 59% at run. Organizations at the run stage also have an average of 3.7 live AI capabilities, compared with 1.6 at walk and 0.5 at crawl.

These findings show a connection between AI maturity and reported returns. For individual finance teams, however, the decision to expand should still depend on whether their results meet financial targets and hold up over time.

How PEX helps finance teams turn AI into measurable value

PEX combines AI with automation to help finance teams reduce manual work across the expense management process. Each capability also provides opportunities to track improvements:

• AI-powered GL code suggestions: Recommend transaction categories or GL codes for review, helping reduce manual coding work. Teams can measure time spent categorizing transactions, suggestion acceptance rates and corrections.
• AI-enabled receipt matching: Helps match submitted receipts to transactions, reducing the need for manual matching. Teams can track time spent matching receipts, match rates and exceptions requiring review.
• Auto Enforcer: Applies configurable rules that can temporarily block cardholders from making new purchases when they fail to submit required receipts within a specified timeframe. Teams can measure overdue receipts, documentation completion rates and time spent following up.
• 50+ pre-built accounting integrations: Transfer transaction data into connected accounting systems, reducing manual data entry and reconciliation work. Teams can measure reconciliation time and how long it takes for completed transactions to reach the ledger.

Customer proof: Cypressbrook

Cypressbrook Multifamily Management oversees 11 properties, each with its own account and reporting requirements. Before PEX, its accountant spent hours reviewing transaction notes, entering GL codes and chasing missing receipts. The resulting delays sometimes pushed month-end close back two to three months.

• Five hours saved every month on reconciliation. AI-powered GL code suggestions help property managers categorize transactions, while structured exports eliminate the need to manually re-enter codes
• Books closed within the accounting period. Auto Enforcer pauses card access when required receipts aren’t submitted on time, helping prevent documentation backlogs that previously delayed closing by two to three months
• New accounts onboarded in one to two days. Online account setup replaces a process that previously took weeks, helping Cypressbrook bring new properties online faster

For Cypressbrook, the five hours saved each month on reconciliation provide a starting point for calculating the value of time returned to its finance team.

Make your next AI investment count 

When you can put a number on the value AI delivers, you can make more confident decisions about where to invest next. That gives finance teams a path to expanding AI based on demonstrated results while maintaining control over spending and processes. 

For more insights into how finance teams are adopting AI and measuring its impact, download PEX’s 2026 State of Finance report.