Sit in a sponsor business review and listen for five minutes. The CFO will mention AI. The board will nod. Nobody will name a metric.
AI has entered the finance conversation with enormous promise, but many CFOs are still operating in first gear. While boards and investors assume AI should help boost performance and strengthen financial discipline, the reality inside the finance function is far more restrained: scattered pilots, basic use cases, and a level of adoption that feels more exploratory than transformational. The pressure for return is very real, but the roadmap to achieving it remains blurry. AI will not fix a broken process, but it will expose one faster than anything else. The CFOs who are winning right now are not running pilots — they are systematically targeting the three core workflows (close, working capital, forecast) where precision creates compounding enterprise value. This article is your roadmap.
The real opportunity lies in applying AI where it can strengthen the foundations of finance itself, enhancing accuracy, accelerating insight, and reducing friction in the everyday work of running a business. As efficiency improves, the quality and speed of insight naturally follow, creating a more agile and responsive finance function. AI is the tool that helps the CFO deliver that judgment faster, with more confidence, and with clearer pathways to value.
Where AI Creates Compounding Value in Finance
Finance teams need practical, repeatable gains that improve workflow speed, accuracy, and control. Some of the most impactful wins are where AI accelerates the processes finance owns every single day, such as relieving pressure on close, strengthening cash conversion, and sharpening forward visibility.
Close Acceleration
- AI‑enabled accounting workflows have driven a 7.5‑day reduction in monthly close time.4
- Improved general‑ledger granularity (+12%) supports higher reporting quality and reduces post‑close adjustments.4
- Ideal applications: automated reconciliations, variance explanation, journal‑entry summarization, and exception surfacing.
Working Capital and Collections
- 99% of AR teams using AI report DSO improvement, with 75% reducing DSO by at least six days.5
- Accelerates matching, routing, and exception clearing, shrinking aging, improving cash predictability, and reducing manual touches.
- Ideal applications: AI‑driven cash application, anomaly detection for disputed invoices, automated outreach prioritization.
Forward‑Looking Intelligence and Forecast Accuracy
- CFOs project a 24% improvement in forecast accuracy by 2027 with AI‑enabled prediction and scenario capabilities.6
- AI also contributes to more continuous‑close readiness and improved working‑capital forecasting.
- Ideal applications: probabilistic forecasting, automated scenario generation, and rolling cash‑flow projections with increased confidence.
The CFO’s Path to Practical Wins
1. Identify the Friction Points That Matter Most
Begin by mapping where time, accuracy, or visibility issues create the greatest drag on performance. Focus on bottlenecks that directly influence, close timelines, cash conversion, or forecast reliability. Clarity on “where value lives” prevents AI from becoming a science experiment.
2. Assess Data Quality and Workflow Foundation
AI only performs as well as the data and processes that feed it. Evaluate consistency, completeness, and accessibility across GL, AR, AP, and forecasting datasets, along with the workflows that support them. You do not need perfection, just enough structure to ensure the model can learn and produce reliable outputs.
3. Select a Single, High‑Confidence Use Case to Prove Value
Start with one target area that has clear metrics and visible impact. A smaller, well‑scoped initiative builds momentum and confidence, both with your team and with your board or sponsor. Success here becomes the blueprint for broader adoption. For most PE-backed companies, the highest-confidence first use case is close acceleration or DSO reduction, both have measurable baselines, defined endpoints, and direct EBITDA impact.
4. Define Success Before You Begin
Establish the specific outcomes you expect, whether that is a day’s to close reduction, points of forecast variance reduced, or a measurable shift in DSO. Align these metrics with your investment thesis so the impact ties directly to enterprise value. Clear targets ensure the AI initiative avoids drift and stays grounded in financial reality.
5. Build Capability, Not Dependency
Ensure your internal finance team learns how to use, interpret, and challenge the AI outputs. This capability transfer is what turns AI from a point solution into a durable operating advantage. Over time, your team should mature from users to co‑pilots, integrating AI into daily judgment calls and decision cycles.
AI does not replace the fundamentals of finance, it strengthens them. By targeting the processes that matter most and building the capabilities to sustain them, CFOs can turn AI from a source of pressure into a tangible performance advantage. The organizations that move with intention, discipline, and clarity of purpose will be the ones that translate AI into real impact.
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Sources
[1] AI Transformation Opens Door for Finance Professionals | AICPA & CIMA
[2] Gartner Survey Shows Finance AI Adoption Remains Steady in 2025
[3] Forbes AI Study 2025: Why Enterprises Struggle to Measure AI ROI
[4] How generative AI can make accountants more productive | MIT Sloan
[5] AI is Reshaping Accounts Receivable | Billtrust Press Release
[6] AI-Powered Productivity: Finance | IBM
© Copyright 2026. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC, its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.
