Finance leaders face a critical AI opportunity as over half lag in process automation
Finance teams see the upside of AI, but the execution gap is still wide. According to new research from IDP provider Rossum, 54.2% of finance leaders have yet to fully automate their processes, and 1 in 10 still rely on manual data entry and spreadsheets. That is lost time, higher error rates, and more exposure to fraud.
The tools exist to automate end-to-end workflows. Most organizations aren't ready to deploy them at scale or capture the savings. Closing that gap is a priority that pays back quickly.
The upside: efficiency now, fraud prevention always on
Finance leaders are optimistic about AI's potential. 43.8% say automation and AI bring more security opportunities than risks, given their ability to spot patterns humans miss.
Duplicate invoices, concealed delivery shortages, and spoofed supplier requests slip through traditional methods until it's too late. AI can flag anomalies across invoice volumes, payment cycles, and vendor behavior in near real time, tightening controls without slowing the team.
What teams are prioritizing
Nearly 31.3% are using AI to improve the accuracy of financial reporting and analysis. That aligns with the top priority: reliable data.
Leaders are also measuring success at the operational level: 34.2% track cost per invoice, processing time, and error rates. The focus is shifting from hype to measurable outcomes.
As Rossum's CTO and co-founder Petr Baudiลก puts it: "In finance, the measure of AI's value is whether it can deliver accuracy, foresight, and protect against fraud - not just speedโฆ The next step is achieving verifiable accuracy and predictive power."
What's blocking adoption
- Data quality and availability (52%) - scattered sources, inconsistent formats, missing fields
- Lack of internal expertise (49%) - not enough AI, data, or automation skills in-house
- Regulatory or legal concerns (31%) - model risk, privacy, record retention
- Resistance to change (30%) - process owners protect current workflows
- High implementation cost (27%) - unclear scope and vendor sprawl
- Lack of clear ROI (23%) - benefits not tied to P&L or cash conversion
- Ethical concerns (15%) - transparency, bias, and accountability
A practical 90-day plan
- Pick one high-volume process (AP invoice capture, 3-way match exceptions, vendor onboarding). Define success in three KPIs: cost per invoice, cycle time, and accuracy.
- Stand up IDP with human-in-the-loop for edge cases and fast feedback. Target 70-80% straight-through processing in phase one.
- Fix the data: standardize vendor master fields, enforce required metadata, remove duplicates, and agree on one source of truth.
- Add fraud signals: duplicate detection, supplier banking changes, unusual invoice timing/amounts, and PO/delivery mismatches.
- Integrate lightly: API into ERP for posting and status sync; keep the pilot sandboxed with audit trails.
- Review weekly: tune extraction models, retrain on exceptions, and publish KPI deltas to the CFO and controller.
Build with control by design
Automation without governance creates audit headaches. Put policy and controls up front: data lineage, approval thresholds, exception routing, and human oversight for high-risk cases.
Use established frameworks to structure risk management and accountability. The NIST AI Risk Management Framework is a solid starting point for model risk, transparency, and monitoring.
Core stack for finance automation
- IDP for unstructured docs (invoices, POs, receipts, vendor forms)
- Workflow/orchestration for approvals, escalations, and SLAs
- ERP integration for posting, 3-way match, and payment status
- Data quality and MDM to stabilize vendor, GL, and cost center data
- Monitoring for model performance, drift, and control exceptions
Fraud: move from reactive to proactive
Fraud patterns change, but the signals are consistent: duplicates, timing outliers, supplier detail changes, and small-amount splitting. Bake these checks into your workflow, not just quarterly reviews.
For benchmarks and playbooks, see the Association of Certified Fraud Examiners' resources on controls and detection here.
Metrics that matter
- Cost per invoice and first-pass yield (straight-through processing rate)
- Cycle time from receipt to approval to posting
- Exception rate and rework minutes per document
- Accuracy by field (vendor, amount, PO, line items)
- Fraud and error catch rate pre- vs. post-payment
- Working capital impact: early-pay discounts captured, late fees avoided
Make the business case
Quantify savings with a simple model: current cost per invoice x monthly volume vs. target cost with automation, plus improved discount capture and reduced write-offs. Add a sensitivity range to address risk and adoption speed.
Fund the pilot from quick wins, then expand to adjacent processes (expense receipts, order-to-cash disputes, vendor master changes). Keep scope tight and results visible.
Next steps
- Start small: one process, one set of KPIs, one quarter
- Design for auditability: logs, approvals, overrides
- Close the data gap before scaling
- Publish results and reinvest in the next use case
If you want a curated view of AI tools built for finance teams, this list is a useful shortcut: AI tools for finance.
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