AI for CFOs in Mid-Market Companies
How to get real ROI from AI in Finance without blowing up your controls
A pragmatic playbook for the next 24 months
Executive Overview – Why AI Matters Now for Mid-Market CFOs
Finance has quietly become one of the most AI-intensive functions in the business.
- A Gartner survey found 58% of finance functions are already using AI in 2024, up 21 points from 2023, and half of the remaining teams plan to implement it.
- A Richmond Fed analysis shows about 20% of firms adopted labor-saving AI tools in just the last 12 months, with another 30% planning to add more.
- Yet Deloitte's CFO Signals research shows most CFOs say GenAI has had "minimal or no impact" on their finance talent model so far, even though 60%+ say GenAI skills are important.
Translation for a mid-market, Midwest-leaning CFO:
Your peers are experimenting aggressively, but very few have translated "cool demos" into controlled, measurable P&L impact.
This page is about that gap: How a cautious, numbers-driven CFO can use AI to reduce manual work, accelerate close, sharpen forecasts, and protect controls—starting with a 90-day, tightly-scoped pilot, not a moonshot.
Primary CTA woven through this page:
Book a short working session to scope a 90-Day AI Finance Pilot
Focus: 1–2 high-ROI use cases in your finance org, with clear KPIs and guardrails.
Why the Office of the CFO Is Uniquely Suited to AI
1. Finance is where the data lives
The office of the CFO sits on ERP/GL transactions, AP/AR data, forecasts, budgets, and board decks. That's exactly the "data exhaust" AI needs: large volumes, repeatable patterns, and clear labels.
2. Processes are repeatable and rule-heavy
AI thrives where workflows are structured (close, reconciliations, invoice approvals), decision logic is documented, and volume is high. AP, AR, close, and FP&A all fit this pattern.
3. Finance decisions are measurable
The CFO function is uniquely positioned to show hard ROI: hours removed, days shaved off close, forecast error reduced, working capital improvements. That makes AI investments easier to justify—and easier to kill if they don't perform.
Risk-Averse CFOs Aren't Behind—They're Early Majority
Deloitte's CFO Signals shows GenAI is already a top internal risk and talent concern, but only about a quarter of CFOs feel it's a good time to take on more risk overall. The opportunity: be the CFO who experiments in a controlled 90-day window, with tight scope and measurable outcomes, rather than waiting for ungoverned adoption to happen around you.
High-Value AI Use Cases for CFOs
Below are practical, high-ROI use cases you can run in a mid-market finance team (say 5–50 FTEs). Impact ranges are directional, based on industry benchmarks and realistic early goals for a 90-day pilot.
| Category | Use Case | Business Value | Typical Impact Range | Difficulty |
|---|---|---|---|---|
| AP / P2P | AI invoice capture & coding | Auto-extracts and codes invoices, reducing manual keying & errors | 20–40% reduction in manual AP workload; per-invoice cost down 20–50% | Low–Med |
| AP / P2P | Touchless approvals & exception routing | Routes invoices based on policy; flags exceptions and duplicates | Touchless rate up to 40–60% of volume in 90 days | Med |
| AR / O2C | Cash application & collections prioritization | Auto-matches payments and suggests who to chase, in what order | 20–30% reduction in unapplied cash; 5–15% improvement in DSO | Med |
| Close & Reporting | AI-assisted reconciliations & anomaly detection | Flags unusual journal entries, variances, or mispostings before close | Close cycle time reduced by 1–3 days; manual reconciliation effort cut 20–30% | Med |
| Close & Reporting | Narrative reporting & board-pack drafting | Drafts MD&A-style commentary and variance explanations | 30–50% time savings on narrative/report drafting | Low |
| FP&A | AI-assisted forecasting & scenario modeling | Generates baseline forecasts, runs scenarios, highlights key drivers | Forecast error reduced 5–10%; scenario turnaround time cut 50–70% | Med–High |
| Cash & Treasury | Short-term cash forecasting & covenant watchlist | Consolidates inflows/outflows, predicts cash, flags potential risks | 10–20% reduction in excess cash buffer; earlier visibility into covenant issues | Med |
| Risk & Compliance | Expense and T&E policy anomaly detection | Flags likely non-compliant expenses, duplicate claims, or risky vendors | 20–40% reduction in out-of-policy spend reviewed after the fact | Med |
| Finance Ops | Finance Q&A copilot over policies & reports | Lets business leaders ask natural-language questions on budgets, actuals, and policies | 30–50% fewer ad-hoc "Can you pull me X?" requests | Low–Med |
| Meta / Enablement | AI for finance team productivity (email, docs) | Drafts emails, memos, workpapers; summarizes contracts, vendor docs | 1–2 hours per FTE per week reclaimed | Low |
How a CFO Should Sequence Use Cases
For a 90-Day AI Finance Pilot, a sensible sequence:
- 1. Start with low-risk, high-volume workflows: AP invoice capture, close reconciliations, narrative reporting, or finance productivity copilots. These touch internal data, don't change revenue recognition, and are easy to roll back.
- 2. Layer in forecasting and cash once data is trusted: Use AI first to support FP&A (alternative scenarios, "second opinion" forecasts) rather than replacing team judgment.
- 3. Expand to AR and working capital once confidence is built: Collections prioritization, DSO reduction, and covenant early warning become easier when you've already proven the controls.
- 4. Only then tackle high-stakes automation: Anything that directly triggers payments, journal postings, or regulatory filings should follow 1–2 successful pilots with strong control testing.
Case Studies & Benchmarks
1. AP Automation for a $150M Industrial Manufacturer
Context: 4-person AP team, ~2,500 invoices/month, mostly PDFs from suppliers. Manual entry into ERP, average cycle time ~14 days.
Solution: Implemented AI-based invoice capture and coding, plus workflow for approvals. Integrated directly with ERP; no change to chart of accounts or approval policy.
Outcomes (12-month view):
- Touchless processing rate moved from ~10% to 55% of invoices
- Average cycle time dropped from 14 days to 5 days
- Cost per invoice reduced an estimated 35–40% initially
- Audit findings improved due to better duplicate and error detection
2. Close Acceleration at a PE-Owned Services Firm (~$300M Revenue)
Context: PE sponsor wanted faster reporting and more robust weekly flash numbers. Finance team struggled with late adjustments and manual reconciliations.
Solution: Deployed anomaly detection on GL data to flag unusual entries and missing reconciliations during the month, not just at close. Introduced AI-generated variance narratives for management reports.
Outcomes (6–9 months):
- Month-end close time reduced from 10 days to 6 days
- 20–30% fewer late adjustments after management review
- FP&A and CFO shifted more time to scenario analysis instead of cleaning data
3. AI-Assisted Forecasting for a Family-Owned Distributor (~$80M Revenue)
Context: Owner and CFO relied heavily on gut feel plus spreadsheets; forecasts were volatile. Inventory and working capital swings created stress on the banking relationship.
Solution: Implemented AI-assisted demand and revenue forecasting, using historical sales, seasonality, and pricing data. LLM-generated scenario narratives to brief owners and the bank.
Outcomes (first 12 months):
- Forecast accuracy improved by 5–10 percentage points vs. prior year
- Inventory buffers reduced gradually, improving working capital by low-single-digit % of revenue
- Owner confidence increased; banking covenants monitored with earlier warning
Implementation Playbook for CFOs – A 90-Day AI Finance Pilot
Think of this as a 90-day "mini-transaction": you're testing this just like any investment.
Phase 1 (Weeks 1–2): Target the Right Use Case
Objectives: Select 1–2 pilot areas (often AP, close, or narrative reporting). Confirm data readiness and constraints.
Activities:
- 2–3 stakeholder interviews (CFO, Controller, AP/AR or FP&A lead)
- Simple baseline metrics: invoices/month, cost per invoice, FTE hours, error rates, cycle time
Example 90-Day Pilot KPI Targets:
- AP: Reduce manual keying time by 20–30%; achieve 25–40% touchless processing; reduce cycle time by 3–5 days
- Close: Shorten close by 1–2 days; cut manual reconciliation effort by 15–25%
- FP&A: Improve forecast accuracy by 3–5 percentage points; cut scenario turnaround time by 50%
Phase 2 (Weeks 3–4): Design the Pilot & Controls
Objectives: Configure the tool(s) and integration. Define what the AI is allowed to do and what remains human-only.
Key design elements:
- Scope: which entities, business units, and data sources
- Roles: who reviews AI suggestions, who approves final postings/payments
- Control overlays: thresholds for auto-approval vs. mandatory review, dual control for overrides, logging of AI recommendations vs. human decisions
Governance deliverables:
- A 1–2 page AI Control Summary for internal audit and external auditors
- A simple risk register: data quality, model risk, cyber, vendor risk
Phase 3 (Weeks 5–12): Run the Pilot
Execution cadence: Weekly 30-minute standups with the project team (Finance + IT). Monitor KPIs against baseline; track user feedback and issues.
What "good" looks like during the pilot:
- Early weeks: AI suggestions are double-checked; expect higher review burden
- Mid-pilot: error rates fall, confidence rises; some workloads become semi-automated
- Late pilot: targeted KPI improvements (e.g., 20% less manual AP work or 1 day off close) are visible, even if not perfect
Phase 4 (End of 90 Days): Evaluate & Decide
Questions for the CFO:
- Did we hit at least 70–80% of our KPI targets?
- Did we maintain or improve control quality (no new audit issues)?
- Is the team more willing or less willing to use AI than at Day 1?
- Do we have a clear backlog of next use cases with similar or better ROI?
Decisions:
- Scale: Expand to more entities, suppliers, or accounts
- Adjust: Tune controls, threshold, or data sources
- Stop: If results are weak or risk is too high, you can roll back with minimal sunk cost
Risks, Controls & Governance
1. Data Quality & "Garbage In, Garbage Out"
AI does not fix bad data; it can amplify it. First step is always basic hygiene: chart of accounts discipline, vendor data quality, mapping tables.
2. Model & Process Transparency
Avoid black boxes where AI posts entries or releases payments with no explanation. Require:
- Explanations ("why this coding / match / forecast")
- Logs of AI suggestions vs. human actions
- Clear fallbacks when confidence is low
3. Segregation of Duties & Access Control
AI cannot collapse SoD: the fact that a model decides something does not remove the need for independent review where required. AI should propose, humans approve—especially for payments, journal postings, and external reporting.
4. Cybersecurity & Vendor Risk
If using cloud vendors, confirm certifications (SOC 2, ISO 27001, HIPAA as relevant). Clarify:
- How your data is stored and encrypted
- Whether it is used to train shared models
- How you can delete or export it
5. Change Management & Talent
Deloitte's CFO Signals found GenAI technical skills and fluency are top concerns, and most CFOs say GenAI has had limited impact on their talent model so far. That's a signal: successful AI programs in finance invest in upskilling the existing team, not just buying tools.
2–5 Year Outlook for the AI-Enabled CFO
Over the next 2–5 years, expect:
- Automation of 30–50% of routine finance work – In finance, AP, close, and basic forecasting are clear candidates.
- CFO as "Chief Analytics & Assurance Officer" – More time on scenario planning, capital allocation, and risk; less on chasing data and reconciling spreadsheets. Board and PE sponsors will expect CFOs to have a point of view on AI, not just to sign off budgets.
- Embedded AI in existing tools – Most CFOs will get AI from their current vendors first (ERP, FP&A, AP/AR platforms).
- Evolving Regulation & Audit Standards – Expect more explicit expectations from regulators and auditors on model governance, AI documentation, and explainability.
- Talent Mix Shifts – Finance and controllership teams will need tech-fluent analysts who can interrogate AI outputs. Upskilling will likely outpace net new AI hiring, especially in mid-market.
No sci-fi here. The CFO who wins is the one who:
- Runs controlled experiments now
- Documents controls and ROI as they go
- Uses AI to improve discipline—not dodge it
Ready to Scope Your 90-Day AI Finance Pilot?
Book a short working session to identify 1–2 high-ROI use cases in your finance org, with clear KPIs and guardrails.
In 30 minutes, we'll identify candidate use cases (AP, close, FP&A, or cash), define realistic KPI targets for your situation, and outline the guardrails required for your auditors, PE sponsor, or board.
Apply for Your 90-Day Sprint
Due to the hands-on nature of the Sprint, I work with a limited number of mid-market leaders each year. Tell me about your situation and I'll be in touch within 24 hours.
Current availability: Accepting applications for Q1 2026 engagements.
What to expect:
- ✓ 20-minute conversation to understand your context
- ✓ Quick assessment of AI opportunities in your operations
- ✓ Honest take on what's worth pursuing (and what's not)
- ✓ No obligation, just clarity on your next steps
Satisfaction guarantee: If the call doesn't provide value, I'll refund your time with actionable next steps at no charge.
Contact Information
Key Takeaways
- Finance is now one of the fastest adopters of AI: 58% of finance functions already use AI, and most laggards plan to follow.
- The most successful CFOs aren't chasing hype—they're starting with 1–2 controlled pilots in AP, close, or forecasting, with clear KPI targets.
- Benchmarks show AP automation alone can cut invoice cost by 30–70% and shrink cycle time from ~15 days to 3–5 days when done right.
- Governance and controls are not optional; they're a differentiator—CFOs who document AI controls now will be ahead of auditors and regulators later.
- A 90-Day AI Finance Pilot is the safest way for mid-market CFOs to test AI, prove ROI, and build a roadmap they can defend to boards and PE sponsors.