CFOs aren’t short of AI pitches. They’re short of evidence that pays back fast. Adoption has surged — more than three-quarters of companies now use AI in at least one function, and 71% say they regularly use generative AI — yet most firms still do not see enterprise-level EBIT impact. The message from recent surveys is consistent: value appears when you rewire workflows, track KPIs, and scale what works — not when you run endless demos.

Finance leaders also face a credibility gap: boards hear big promises, while pilots stall. Only about one in five organisations moves beyond proof-of-concept to real value, and only a small minority creates substantial impact. Meanwhile, finance processes like AP remain ripe for quick wins, with measurable cost-per-invoice and cycle-time benchmarks.

This article shows EU/MENA executives how to pick AI use cases that “clear the CFO” — i.e., demonstrate payback and control within 90 days — and how to run them with an execution rhythm that scales. We’ll keep it practical: specific use cases, measurable metrics, and a delivery model you can trust.

A use case clears the CFO when it: (1) targets a costed, benchmarked process; (2) lands working software with users in production; and (3) produces auditable deltas on KPIs (cost, time, leakage, risk). In today’s market, CFOs will fund quick wins that are controllable, governed, and scalable.

Reality check. Despite soaring adoption, more than 80% of companies report no tangible enterprise-level EBIT impact from genAI yet; the differentiator is workflow redesign plus KPI tracking. In short: don’t buy models — fix processes.

These target measurable baselines and rely on data you already own.

1. AP invoice automation (capture → code → match → approve).
   Average processing time is ~10 days and cost per invoice often ~$10; best-in-class run near $2.78 per invoice. Even modest touchless rates (30–50%) reduce rework, exceptions, and late-payment fees within a quarter. Start with high-volume suppliers, enforce three-way match, and measure cost/time per invoice, exception rate, and DPO impact.

2. Cash-application & collections prioritisation (AR).
   Machine-learning scorecards rank accounts by collectability and next-best action; LLMs draft dunning emails with human-in-the-loop controls. KPIs: DSO, % current, write-offs, and agent productivity. (Benchmarks from APQC/industry dashboards help set targets and show gains early.)

3. Spend analytics & policy audit.
   GenAI assists with vendor deduplication, category classification, and anomaly detection on P-cards/expenses. The first 90 days typically surface leakage (duplicate payments, maverick spend) worth immediate remediation. KPIs: % addressable spend categorised, detected duplicates, recovered value.

4. IT ticket deflection & close-time reduction.
   Not a finance process, but it cuts internal service cost allocations. AI copilots and retrieval-augmented bots reduce time-to-resolve and deflect L1 tickets. Independent studies report up to 55% task completion speed-ups for developers; similar patterns translate to service tasks with proper guardrails. KPIs: deflection rate, MTTR, cost per ticket.

5. Forecasting assist (cash, demand, or AP accruals).
   Use classical ML plus lightweight genAI narration. A 90-day sprint can productionise a narrow forecast (e.g., weekly cash) and attach confidence bands to decisions. Pair with a KPI tree (MAPE/bias → cash buffer → working capital). Leading surveys show that organisations that set and track clear KPIs for genAI are more likely to report bottom-line impact.

Score candidate use cases 1–5 on: (a) Data readiness (accessible, labelled, permissions clear), (b) Process measurability (baseline known, KPI is money/time), (c) Stakeholder controllability (one function can own change), and (d) Reusability (components re-use across teams). Prioritise scores ≥16.

External evidence supports this bias: companies that redesign workflows and track defined KPIs for genAI show stronger EBIT impact; meanwhile, only a small minority achieves significant value because they fail to scale beyond pilots.

Week 0–2 (Design): Lock KPIs and baselines (e.g., cost per invoice, cycle time). Create a data access plan and red-team risks.
Week 3–6 (Run): Ship a thin slice to production (e.g., top three suppliers in AP). Demo every two weeks with real metrics.
Week 7–10 (Improve): Remove blockers: exception causes, OCR confidence, routing rules, user prompts.
Week 11–12 (Validate/Expand): Publish the KPI delta, adoption evidence, and a scale-out plan; request funding for the next tranche.

This cadence aligns with what industry research shows: adoption succeeds when teams embed solutions into processes, communicate value, and follow a KPI-driven roadmap.

Examples

• EU manufacturer (AP automation). Starting with 20% of supplier volume, the team launched capture-to-approve in eight weeks, cutting manual touches and late fees. The CFO approved expansion after a live 30-day run with auditable deltas on cost and cycle time (benchmarked against Ardent metrics).

• MENA distributor (collections). A prioritisation model plus templated outreach improved right-first-time contact and reduced ageing buckets. The finance lead green-lit scale after DSO and agent productivity moved in the first quarter (targets aligned to APQC measures).

At Averroa, we avoid “pilot theatre.” We run a repeatable loop — DRIVE™ (Design → Run → Improve → Validate → Expand) — and organise talent with ORBIT™ so the right expertise engages at the right time.

• Design: Confirm the business case, KPIs, and governance.
• Run: Deliver increments in production, not slides.
• Improve: Fix data quality and flow; tune prompts and rules.
• Validate: Prove the KPI delta with finance sign-off.
• Expand: Scale to more volume, regions, or functions.

Engagement tracks:

• Research & Innovation: 2–6 weeks to baseline KPIs, test options, and produce a costed roadmap.
• Execution & Delivery: Sprint-based build to production with adoption and MLOps.
• Rescue & Support: Turnaround and steady-state value reviews with SLAs.

• Adoption is real but payback needs process change. 78% use AI in at least one function; KPI tracking and workflow redesign correlate with EBIT impact; <1/3 follow best practices; >80% see no enterprise EBIT impact yet. McKinsey & Company
• Scaling remains the chokepoint. Only ~22% move beyond PoC; ~4% create substantial value. BCG
• Finance has measurable quick wins. Average cost per invoice around ~$9–10 and ~10 days cycle time; best-in-class near $2.78. TradeshiftDatoCMS Assets
• Productivity uplifts are plausible with controls. Developers complete tasks up to 55% faster with AI assistants, a pattern that translates to operations when tethered to KPIs and human-in-the-loop. The GitHub Blog
• CFO sentiment is improving, but scrutiny is high. Risk appetite is returning in 2025; CFOs want provable value.

• Pick one finance use case with clear benchmarks (AP, AR, spend).
• Lock three KPIs and baselines; publish them in Week 2.
• Ship to production by Week 6 for a small but real slice.
• Hold bi-weekly demos with KPI deltas and user feedback.
• Track adoption and exceptions alongside accuracy and cost.
• Build a scale plan (volumes, regions) only after Validate.

Ready to clear your CFO in 90 days? Start with a 2-week diagnostic on AI & Analytics — then ship a use case that proves itself.

• McKinsey (2025). The state of AI: How organizations are rewiring to capture value. McKinsey & Company
• McKinsey (2024). The state of AI 2024. McKinsey & Company
• BCG (2024). Where’s the Value in AI? BCG
• Ardent Partners (2024). AP Metrics that Matter. Tradeshift
• Ardent Partners (2025). AP Metrics that Matter 2025 (Best-in-Class). DatoCMS Assets
• APQC (collection & measures). AP/AR benchmarking. APQC+1
• GitHub (2024). Copilot impact in the enterprise (with Accenture). The GitHub Blog
• Deloitte (2025). CFO Signals 4Q24 press note. Deloitte United Kingdom