Key Takeaways
- Only 34% of organizations have achieved advanced AI-enabled spend management automation across their full procurement and finance stack, while the majority still rely on partially manual processes (Ardent Partners, Procurement Innovation Study 2025)
- Best-in-class organizations process expense reports for $6.85 each on average, versus $58.00 for fully manual departments - an 88% cost advantage driven by AI extraction, policy enforcement, and touchless routing (GBTA Business Travel Benchmarking 2025)
- AI-powered spend management platforms reduce expense report cycle times from an industry average of 8.3 days to under 24 hours in mature deployments, with leading platforms reporting same-day reimbursement for clean, compliant submissions (PYMNTS Expense Management Tracker 2025)
- Policy compliance rates rise from a manual average of 78% to 94% or higher when AI spend controls and real-time guardrails are in place, according to Ramp's 2025 State of Business Spend report covering 25,000+ companies
- Forrester's Total Economic Impact study on leading spend management platforms found a 287% three-year ROI, with the median organization recovering its full implementation cost within 8 months (Forrester TEI 2025)
AI spend management automation statistics 2026: what the data shows
Corporate spend management spans every dollar a company spends: employee expense reports, accounts payable, purchase orders, vendor contracts, and procurement sourcing. Managed well, these processes protect margins and working capital. Managed poorly, they generate errors, fraud, policy leakage, and FTE overhead that scales directly with company size.
The 2026 AI spend management automation statistics show an industry mid-transition. Automation tools have moved well past basic digitization - modern platforms enforce policy at the moment of transaction, flag anomalies before payment clears, and process most clean transactions without a human touch. Deployment depth still varies sharply, though, and the performance gap between organizations that have gone deep versus those that have addressed only one process is measurable in dollars per transaction, days per cycle, and percentage points of savings captured.
The data here draws on Ardent Partners, APQC, the Global Business Travel Association (GBTA), PYMNTS, Ramp, Brex, McKinsey, Deloitte, Gartner, and Forrester. For the broader accounting automation context, the AI in accounting and finance statistics 2026 covers CFO-level adoption and full finance function metrics. For payroll automation specifically, see AI payroll processing statistics 2026. For staffing cost benchmarks across the finance function, see financial services staffing costs 2026.
1. Adoption of AI automation in spend management (2026)
AI spend management automation encompasses a wide range of applications: expense report processing, real-time policy enforcement, purchase order matching, vendor payment automation, anomaly detection, and procurement analytics. Survey methodologies capture these differently, which means headline adoption numbers require context about what is actually being automated.
Ardent Partners' Procurement Innovation Study 2025, drawing on 312 procurement and finance leaders, found that 67% of organizations use some form of automation in at least one spend management function, up from 48% in 2022. However, Ardent distinguishes between tactical automation (one or two processes automated in isolation) and strategic automation (AI embedded across the full spend lifecycle). Only 34% qualify as advanced adopters operating with AI-native workflows across expense, AP, purchasing, and vendor management simultaneously.
Gartner's 2025 Finance Technology Adoption Survey found spend management automation ranked as the third most common AI use case in production across finance teams, cited by 41% of respondents - behind knowledge management (49%) and accounts payable automation (37%). The distinction matters: many organizations have addressed AP in isolation without connecting it to upstream purchase controls or downstream spend analytics.
Deloitte's 2025 Global CPO Survey, covering 450 chief procurement officers, found that 58% of organizations have deployed AI tools specifically in procurement sourcing or supplier management, but only 29% describe those deployments as fully integrated with their ERP and expense platforms. Integration gaps are the primary reason automation savings are not fully realized.
AI spend management automation adoption by function (2025)
| Function | Adoption rate (any automation) | Advanced/integrated adoption | Source |
|---|---|---|---|
| Expense report processing | 71% | 38% | PYMNTS Expense Management Tracker 2025 |
| Accounts payable / invoice processing | 73% | 34% | Ardent Partners State of ePayables 2025 |
| Purchase order matching and approval | 61% | 31% | Ardent Partners Procurement Innovation 2025 |
| Vendor/supplier management | 52% | 27% | Deloitte Global CPO Survey 2025 |
| Procurement sourcing and analytics | 58% | 29% | Deloitte Global CPO Survey 2025 |
| Full-stack spend automation (all of above) | 34% | - | Ardent Partners Procurement Innovation 2025 |
2. Expense report processing: cost and cycle time benchmarks
Employee expense management tends to be where spend management automation pays back fastest. The process is high-volume, largely rule-driven, and historically error-prone - a combination that AI handles well.
The Global Business Travel Association's 2025 Expense Management Benchmark Report, covering data from 3,400 organizations, provides the most widely cited cost-per-report benchmarks:
- Fully manual processing: $58.00 per expense report
- Partially automated (digital submission, manual review): $26.50 per expense report
- AI-automated with touchless routing for clean reports: $6.85 per expense report
The $58.00 fully loaded figure includes FTE time for employee submission, manager review, finance audit, reimbursement, and error correction. At organizations processing 10,000 expense reports per year, moving from fully manual to AI-automated processing saves approximately $512,000 annually on processing costs alone - before accounting for fraud prevention and compliance improvements.
PYMNTS' Expense Management Tracker 2025 found that expense report cycle time - from submission to reimbursement - averages 8.3 business days across the industry. At organizations using AI-powered platforms with real-time policy checking and automated routing, median cycle time drops to 1.1 business days. Leading platforms report same-day reimbursement for clean, policy-compliant submissions processed before a daily cutoff.
Ramp's 2025 State of Business Spend report, drawing on data from 25,000+ companies using its platform, found that AI-generated receipt matching and auto-categorization reduces time employees spend on expense reporting by 62% per submission. For a 500-person company, that translates to approximately 3,100 hours of employee time recovered annually - time previously spent on administrative work with no revenue-generating value.
Expense report processing benchmarks (2025)
| Metric | Manual | Partially automated | AI-automated | Source |
|---|---|---|---|---|
| Cost per expense report | $58.00 | $26.50 | $6.85 | GBTA 2025 |
| Cycle time (submission to reimbursement) | 8.3 days | 3.7 days | 1.1 days | PYMNTS 2025 |
| Error rate (coding/categorization) | 19% | 8% | 1.2% | Brex 2025 Spend Insights |
| Employee time per submission | 24 minutes | 14 minutes | 9 minutes | Ramp State of Business Spend 2025 |
3. Cost-per-transaction savings across procurement and AP
Beyond expense management, AI automation reduces per-transaction costs across the broader procurement and accounts payable stack. These savings compound at scale: an organization processing 500,000 invoices and purchase orders annually at an average cost reduction of $4 per transaction saves $2 million annually from transaction-level efficiency alone.
APQC's 2025 Open Standards Research provides the most rigorous purchase order processing benchmarks:
- Top performers (25th percentile): $3.74 per purchase order
- Median: $8.12 per purchase order
- Bottom performers (75th percentile): $14.91 per purchase order
The performance gap between top and bottom quartile - $11.17 per PO - has widened over the past three years. APQC attributes the divergence primarily to automation depth: top performers use AI-assisted requisition processing, automated three-way matching, exception prediction, and touchless PO routing. Bottom performers have typically digitized the submission step but retained manual approval and reconciliation.
McKinsey's 2025 State of Procurement report, based on interviews with 185 CPOs and finance executives, found that organizations with advanced procurement automation achieve a 40-60% reduction in total cost to process across the full purchase-to-pay cycle compared with organizations using legacy or manual workflows. The report identifies three primary drivers: elimination of manual data entry (15-20% of total processing cost), reduction in exception handling (10-15%), and faster vendor payment cycles that unlock early payment discounts (5-15%).
Brex's 2025 Spend Insights report, drawing on aggregated data from its customer base, found that automated spend controls - real-time card limits, merchant category restrictions, and receipt requirements - reduce out-of-budget purchases by 34% compared with traditional reimbursement models where employees submit expenses after the fact.
Cost-per-transaction benchmarks by process (2025)
| Process | Manual cost | AI-automated cost | Reduction | Source |
|---|---|---|---|---|
| Expense report processing | $58.00 | $6.85 | 88% | GBTA 2025 |
| Invoice processing (AP) | $10.89 | $2.36 | 78% | APQC 2025 |
| Purchase order processing | $14.91 | $3.74 | 75% | APQC 2025 |
| Vendor payment processing | $6.80 | $1.90 | 72% | Ardent Partners 2025 |
| Contract review and routing | $185 | $42 | 77% | Deloitte CPO Survey 2025 |
4. Policy compliance and out-of-policy spend reduction
Policy leakage - employees and teams spending outside approved categories, vendors, or amounts - is one of the highest-cost problems in corporate spend management. Traditional audit-based compliance catches violations after the fact; AI-powered controls prevent them at the point of transaction.
Ramp's 2025 State of Business Spend report, drawing on data from 25,000+ companies, found that organizations using real-time AI spend controls achieve a policy compliance rate of 94.2%, compared with 78.1% for organizations relying on post-hoc receipt review and monthly expense audits. The 16-percentage-point gap represents substantial leakage: for a $50 million annual T&E budget, moving from 78% to 94% compliance recovers approximately $8 million in previously uncontrolled spend.
Forrester's 2025 report on AI-enabled expense management found that real-time policy enforcement - where card transactions are blocked or flagged before they clear - reduces out-of-policy spend by 38% in the first year of implementation, with incremental improvements as the system learns category and vendor patterns for specific organizations.
The Global Business Travel Association's 2025 data found that the average organization loses 3.8% of its T&E budget to out-of-policy purchases that are submitted, reviewed, and approved without triggering a violation flag. AI anomaly detection - which flags contextually suspicious expenses (a hotel rate that is 60% above the median for that city and date range, for instance) rather than just rule violations - reduces this figure to 1.2%.
Gartner's 2025 Finance Technology Survey asked CFOs to identify the highest-ROI applications of AI in their finance functions. Policy compliance automation ranked first, cited by 44% of respondents as their highest-ROI AI application - ahead of forecasting (31%), invoice processing (28%), and cash flow optimization (24%).
Policy compliance benchmarks: manual vs. AI-controlled spend (2025)
| Metric | Manual / post-hoc review | AI real-time controls | Source |
|---|---|---|---|
| Policy compliance rate | 78.1% | 94.2% | Ramp 2025 |
| Out-of-policy spend (% of T&E budget) | 3.8% | 1.2% | GBTA 2025 |
| Out-of-policy spend reduction (year 1) | - | 38% | Forrester 2025 |
| Duplicate expense submissions detected | 61% | 97% | Brex Spend Insights 2025 |
| Violations flagged before approval | 23% | 91% | PYMNTS 2025 |
5. Fraud and anomaly detection accuracy
Expense fraud and procurement fraud represent a meaningful share of corporate losses. The Association of Certified Fraud Examiners' 2024 Report to the Nations found that organizations lose a median of 5% of annual revenue to fraud, with billing and expense reimbursement schemes accounting for 17% of all cases. AI anomaly detection addresses this at scale - reviewing every transaction against behavioral baselines, peer groups, and vendor risk profiles rather than sampling.
Deloitte's 2025 Finance Operations Survey found that AI-powered anomaly detection in spend management identifies 85% of fraudulent or out-of-policy transactions before payment, compared with 41% for manual audit sampling. The gap is particularly pronounced for low-value, high-frequency fraud schemes (repeated small expenses that individually fall under review thresholds) that manual review structurally cannot detect at volume.
PYMNTS' 2025 B2B Payments Fraud Tracker found that organizations using AI-enabled spend controls report a 48% reduction in fraud losses compared with the prior year, while organizations without AI controls saw fraud losses increase by 14% over the same period, driven by more sophisticated social engineering and vendor impersonation attacks.
McKinsey's 2025 analysis of procurement fraud found that AI-enabled supplier fraud detection - which cross-references vendor registration data, payment routing, and invoice patterns against known fraud signatures - reduces duplicate payment losses by 67% and vendor impersonation losses by 74%. At an organization processing $500 million in annual AP spend, this translates to $2-4 million in recovered losses.
Ramp's 2025 data found that AI receipt matching - which validates receipt images against transaction metadata in real time - catches 96% of fabricated or altered receipts, compared with 34% for manual expense audits. The gap reflects volume constraints: a finance team that manually audits 10% of expense reports simply cannot review the 96% of reports where receipt manipulation occurs.
Fraud and anomaly detection accuracy benchmarks (2025)
| Detection type | Manual / sampling | AI-automated | Source |
|---|---|---|---|
| Fraudulent transactions caught pre-payment | 41% | 85% | Deloitte Finance Operations Survey 2025 |
| Duplicate payment detection | 63% | 97% | McKinsey Procurement Analytics 2025 |
| Fabricated/altered receipts caught | 34% | 96% | Ramp 2025 |
| Vendor impersonation fraud losses | Baseline | -74% | McKinsey 2025 |
| Overall fraud loss reduction | - | -48% | PYMNTS B2B Payments Fraud Tracker 2025 |
6. Finance and procurement FTE impact
AI spend management automation affects staffing requirements in two ways: direct reduction of FTE hours devoted to manual processing, and redeployment of remaining staff toward higher-value analysis, supplier relationships, and strategic procurement activities. Both effects are documented across multiple sources.
APQC's 2025 benchmarking data found that best-in-class organizations process the same spend volume with 2.1 procurement and finance FTEs per $1 billion in managed spend, compared with 5.8 FTEs per $1 billion at median organizations. The 2.7x FTE efficiency gap is primarily explained by automation depth: best-in-class organizations have automated transaction processing, exception routing, and reconciliation, freeing staff for supplier negotiation and category management.
McKinsey's 2025 State of Procurement report estimates that AI automation is capable of automating 40-60% of the tasks currently performed by procurement and accounts payable staff - data entry, invoice coding, PO matching, expense auditing, and standard vendor communications. This does not imply an equivalent headcount reduction; most organizations are redeploying staff to activities that directly improve savings rates, supplier quality, and working capital management.
Deloitte's 2025 Global CPO Survey found that organizations with advanced procurement automation report 30% higher procurement FTE productivity (measured as managed spend per FTE) compared with organizations without AI-enabled workflows. Importantly, they also report higher employee satisfaction scores: procurement staff at advanced automation adopters rated their work as more strategic and less administrative at a 22-percentage-point higher rate than peers at manual-process organizations.
Forrester's 2025 Total Economic Impact study on spend management automation platforms found that the average organization eliminated 2.4 FTE-equivalent hours of manual work per week per 100 employees - translating to significant annual labor savings that compound as the organization grows.
Procurement and finance FTE efficiency benchmarks (2025)
| Metric | Median organization | Best-in-class | Source |
|---|---|---|---|
| Procurement FTEs per $1B managed spend | 5.8 | 2.1 | APQC 2025 |
| AP staff hours per 1,000 invoices | 46 hours | 11 hours | IOFM 2025 |
| Finance staff time on manual data entry | 38% of week | 9% of week | Deloitte 2025 |
| Procurement FTE productivity improvement | - | +30% | Deloitte CPO Survey 2025 |
| Manual work eliminated per 100 employees per week | - | 2.4 FTE-hours | Forrester TEI 2025 |
7. Working capital improvement and savings rate
Spend management automation creates two distinct working capital levers: payment timing optimization (capturing early payment discounts and avoiding late fees) and managed spend savings (using analytics to identify better pricing, consolidate vendors, and improve contract compliance).
Ardent Partners' 2025 Procurement Innovation Study found that organizations with AI-enabled payment optimization capture early payment discounts on 43% of eligible invoices, compared with 12% at organizations without automation. Given that typical early payment discount terms (2/10 net 30, for example) represent a 36.7% annualized return, discount capture is among the highest-ROI use cases in spend management.
McKinsey's 2025 State of Procurement analysis found that best-in-class procurement organizations - those with AI-enabled sourcing analytics, contract management, and spend visibility - achieve managed spend savings rates of 8-12%, compared with a 4-6% savings rate at organizations without these capabilities. For a company with $500 million in managed spend, the gap between 5% and 10% savings is $25 million annually.
Deloitte's 2025 analysis of working capital benchmarks found that AI-enabled dynamic payment scheduling - which optimizes payment timing against cash flow forecasts and early payment discount availability - improves Days Payable Outstanding (DPO) by an average of 8.3 days. At the same time, organizations capture an additional 1.2% of managed spend in early payment discounts that previously went uncaptured because manual processes could not identify and act on discount windows in time.
PYMNTS' 2025 data on B2B payment optimization found that organizations using AI-driven payment rails and spend scheduling reduce late payment penalties by 71% and free up an average of 12 days of additional working capital compared with organizations processing payments manually or through standard ACH schedules.
Working capital and savings rate benchmarks (2025)
| Metric | Without AI automation | With AI automation | Source |
|---|---|---|---|
| Early payment discount capture rate | 12% of eligible | 43% of eligible | Ardent Partners 2025 |
| Managed spend savings rate | 4-6% | 8-12% | McKinsey 2025 |
| DPO improvement from payment optimization | - | +8.3 days | Deloitte 2025 |
| Late payment penalty reduction | - | -71% | PYMNTS 2025 |
| Cash flow forecast accuracy | 61% | 84% | Gartner CFO Survey 2025 |
8. ROI and payback period benchmarks
ROI data on spend management automation is available from multiple methodologies: vendor-sponsored Total Economic Impact studies, independent survey-based benchmarking, and CFO self-reported results. Each has limitations, but the directional consistency across sources is notable.
Forrester's 2025 Total Economic Impact study on leading spend management platforms - conducted independently across eight enterprise customers with median annual spend of $380 million - found:
- Three-year ROI: 287%
- Payback period: 8 months
- Net present value (three-year): $4.2 million (median customer)
- Primary value drivers: labor savings (39% of total benefits), fraud prevention (28%), compliance improvement (19%), and supplier savings (14%)
Gartner's 2025 Finance Technology Survey asked finance leaders to report their actual measured ROI on AI spend management implementations. Median self-reported ROI after 18 months was 214%, with the top quartile reporting ROI above 380%. Organizations that integrated spend management automation with their ERP and procurement systems reported 2.3x higher ROI than those running standalone tools.
McKinsey's 2025 analysis found that procurement automation ROI is highly sensitive to implementation scope. Organizations that automate the full purchase-to-pay cycle (requisition through payment and reconciliation) achieve 3-4x higher ROI than organizations that automate only one segment - typically AP - in isolation. The compounding effect of connected automation is the primary argument for platform consolidation over point solutions.
Ardent Partners' 2025 survey found that 71% of organizations with advanced spend management automation report full cost recovery within 12 months, while only 22% of organizations with partial or siloed automation achieve payback within the same window.
ROI and payback benchmarks (2025)
| Metric | Partial/siloed automation | Full-stack AI automation | Source |
|---|---|---|---|
| Three-year ROI | 95-140% | 214-287% | Forrester TEI 2025, Gartner 2025 |
| Median payback period | 18-24 months | 8-12 months | Forrester TEI 2025 |
| Organizations with 12-month payback | 22% | 71% | Ardent Partners 2025 |
| Top-quartile three-year ROI | - | 380%+ | Gartner CFO Survey 2025 |
9. Vendor management and procurement analytics
Vendor management - the process of onboarding, evaluating, paying, and renewing supplier relationships - is among the least automated areas of spend management despite being one of the highest-value opportunities for AI application.
Ardent Partners' 2025 Procurement Innovation Study found that only 31% of organizations have AI-enabled supplier risk monitoring, which continuously evaluates supplier financial health, compliance status, ESG performance, and delivery reliability against predefined risk thresholds. The 69% without AI monitoring rely on periodic manual reviews that typically occur annually - a cadence that is inadequate for identifying fast-moving supplier risks.
Deloitte's 2025 Global CPO Survey found that organizations using AI-powered vendor analytics identify 3.7x more cost reduction opportunities in their supply base than organizations using manual category management. The difference is visibility: AI can analyze spend patterns, benchmark against market pricing, and identify consolidation opportunities across thousands of vendor relationships simultaneously.
McKinsey's 2025 State of Procurement analysis found that AI-enabled contract compliance monitoring - which automatically checks invoices against contract pricing terms and flags deviations - identifies pricing discrepancies on an average of 4.1% of invoices at organizations that implement it. At organizations without this capability, those same discrepancies are typically undetected and paid. For a $100 million AP volume, 4.1% pricing discrepancy recovery represents $4.1 million annually.
Gartner's 2025 Procurement Technology Market Guide projects that AI-driven supplier intelligence platforms will be adopted by 65% of large enterprises by 2027, up from 31% in 2025. The growth driver is increasing supply chain disruption risk: organizations that experienced supplier failures in 2023-2024 are investing in continuous monitoring rather than periodic audits.
Vendor management and procurement analytics benchmarks (2025)
| Metric | Without AI | With AI | Source |
|---|---|---|---|
| Supplier risk monitoring coverage | Periodic annual review | Continuous real-time | Ardent Partners 2025 |
| Cost reduction opportunities identified | Baseline | 3.7x more | Deloitte CPO Survey 2025 |
| Invoice pricing discrepancies caught | ~1% | 4.1% | McKinsey 2025 |
| Supplier onboarding cycle time | 18 days | 5 days | Deloitte CPO Survey 2025 |
| Contract compliance rate | 71% | 93% | Ardent Partners 2025 |
10. Market size and growth projections
The AI spend management automation market has grown quickly as enterprise procurement and finance teams prioritize cost control and automation investment.
MarketsandMarkets projects the global procurement software market - which includes spend analytics, procure-to-pay platforms, and supplier management - to grow from $7.6 billion in 2024 to $15.3 billion by 2029, at a CAGR of 15.0%. AI-native spend management capabilities are the primary driver of new platform investment.
Gartner's 2025 Procurement Technology Hype Cycle places AI-powered spend analytics near the Plateau of Productivity, meaning organizations can now expect production-grade deployments rather than pilots. Two years ago, most implementations were still in proof-of-concept mode.
Ardent Partners' 2025 CPO Rising report found that spend management and analytics is the top technology investment priority for procurement organizations in 2025-2026, cited by 54% of CPOs - ahead of supplier risk management (47%), contract lifecycle management (43%), and source-to-contract platforms (38%).
Forrester's 2025 Procurement Technology Forecast projects that the enterprise spend management market will consolidate around a smaller number of AI-native platforms by 2027, with organizations migrating from point solutions to integrated suites that connect expense, AP, procurement, and analytics in a single data model.
AI spend management market size and growth (2024-2029)
| Segment | 2024 market size | 2029 projected | CAGR | Source |
|---|---|---|---|---|
| Procurement software (total) | $7.6B | $15.3B | 15.0% | MarketsandMarkets 2025 |
| Expense management software | $3.2B | $6.8B | 16.3% | Grand View Research 2025 |
| AP automation software | $3.0B | $7.5B | 20.2% | MarketsandMarkets 2025 |
| Supplier intelligence platforms | $1.1B | $2.9B | 21.4% | Gartner 2025 |
Key takeaways for finance and procurement leaders
The 2026 data points to a consistent gap between organizations that have automated their full spend lifecycle and those that have addressed only one or two processes in isolation.
Automation adoption is widespread, but most organizations have only gone partway. Two-thirds have automated at least one spend management function, but only a third have reached the integrated, full-stack state that drives the highest returns. Getting there requires connecting expense, AP, purchasing, and analytics into a single workflow rather than running separate point solutions.
The transaction economics are not subtle. Processing expense reports for $6.85 versus $58.00, invoices for $2.36 versus $10.89, and purchase orders for $3.74 versus $14.91 - these are not incremental improvements. They represent a structural shift in what it costs to manage corporate spend, and the gap between top and bottom performers has widened over the past three years, not narrowed.
Compliance and fraud prevention tend to pay back faster than process efficiency. Policy compliance rising from 78% to 94%, fraud detection improving from 41% to 85% pre-payment, and catching pricing discrepancies on 4.1% of invoices all show up as cash recovered, not just headcount saved.
Working capital improvements scale with spend volume. Capturing early payment discounts on 43% of eligible invoices instead of 12%, and picking up 8.3 days of DPO improvement through payment optimization, are benefits that grow proportionally as the organization's spend base grows.
ROI comes in front-loaded. The median organization in Forrester's TEI study recovered its implementation cost within eight months, with most of that value coming from labor savings and compliance gains in year one. Delaying implementation pushes that payback out while the platform's anomaly detection has less time to learn company-specific patterns.
If there is a single prerequisite, it is spend visibility. Before any workflow can be automated or any anomaly flagged, the organization needs a complete, categorized view of its spend in one place.
For broader context on finance function automation, see the AI in accounting and finance statistics 2026. For staffing cost benchmarks relevant to evaluating the FTE impact of automation, see financial services staffing costs 2026. For AI applications specifically in payroll automation, see AI payroll processing statistics 2026.
Sources: Ardent Partners Procurement Innovation Study 2025; Ardent Partners State of ePayables 2025; Ardent Partners CPO Rising 2025; APQC Open Standards Research 2025; Global Business Travel Association (GBTA) Expense Management Benchmark Report 2025; PYMNTS Expense Management Tracker 2025; PYMNTS B2B Payments Fraud Tracker 2025; Ramp State of Business Spend 2025; Brex 2025 Spend Insights; McKinsey State of Procurement 2025; Deloitte Global CPO Survey 2025; Deloitte Finance Operations Survey 2025; Gartner Finance Technology Adoption Survey 2025; Gartner CFO Survey 2025; Gartner Procurement Technology Market Guide 2025; Gartner Procurement Technology Hype Cycle 2025; Forrester Total Economic Impact of Spend Management Platforms 2025; Forrester Procurement Technology Forecast 2025; IOFM AP Department Benchmarking Survey 2025; Institute for Finance and Management (IOFM) 2025; MarketsandMarkets Procurement Software Market Report 2025; Grand View Research Expense Management Software Market Report 2025; Association of Certified Fraud Examiners Report to the Nations 2024.