AI for the CFO: Strengthening Trust and Strategic Clarity in Finance

Introduction

AI has become a structural capability within finance. It underpins forecasting, reconciliation, disclosure, and the way finance communicates performance to the business and the market — not as isolated pilots but as part of the established operating model. Leading finance teams are integrating AI into their processes with the same assurance, auditability, and governance that define financial control.

For CFOs, the mandate is clear: extend AI across finance without eroding credibility. Regulatory scrutiny continues to intensify around model governance and data transparency, while boards and investors expect faster, forward-looking insight that stands up to challenge. The opportunity lies in scaling AI within governed frameworks so that speed and rigour advance together.

When built on a single, trusted data spine, AI enhances finance’s ability to deliver measurable outcomes: more reliable forecasts, shorter close cycles, reduced reconciliation effort, and higher-quality disclosures. It strengthens the reach of finance — not by changing what it does, but by improving how consistently and confidently it delivers.

The sections that follow outline where CFOs are realising this impact: in forecasting and scenario analysis, close and reconciliation, profitability and capital insight, ESG disclosure, and the leadership disciplines that sustain growth at scale.

Forecasting and scenario planning with greater precision

Forecasting remains a defining test of finance credibility — how effectively the organisation anticipates change, manages risk, and guides investor expectations. CFOs are embedding AI within planning environments to make forecasts more adaptive, explainable, and transparent across business and regulatory contexts.

Advanced finance teams are using predictive and generative models to connect external indicators with internal performance drivers, producing continuously refined outlooks that align finance, risk, and treasury perspectives. Scenario engines extend this capability by testing earnings, capital, and liquidity positions under multiple macroeconomic assumptions — all within governed environments that retain traceability and control.

The result is a forecasting process that combines agility with assurance. Updates can be produced more frequently, underlying drivers are clearer, and variance explanations are better supported by evidence. Boards gain a more coherent view of performance, and CFOs can guide the enterprise with greater precision and confidence — even under volatile conditions.

Financial close and reconciliation: achieving assurance at scale

The financial close remains one of the clearest measures of finance discipline. Despite continued investment in automation, many teams still face delays and late adjustments caused by manual reconciliations and fragmented data. The question for CFOs is how to maintain precision and assurance as the scale and complexity of information continue to rise.

AI is helping address that challenge by giving finance earlier visibility and greater predictability across the close cycle. Trained models can flag exceptions before they become material, highlight recurring patterns in reconciliation issues, and draw connections between underlying drivers and reported results. Generative tools are supporting variance analysis and management reporting, extracting insights directly from governed data.

The impact is felt in both control and consistency. Close cycles become more stable, late manual interventions decline, and confidence in reported results strengthens. CFOs gain the ability to uphold accuracy and transparency even under tighter timelines — sustaining the assurance that underpins every external disclosure and internal decision.

Profitability and capital insight: linking financial performance to strategic allocation

Understanding where and how value is created remains central to the CFO’s mandate. Profitability, cost, and capital use must be assessed with precision across products, portfolios, and markets — especially as margins tighten and investor expectations rise. AI is becoming instrumental in enabling this level of visibility.

By unifying finance, risk, and treasury data, AI-driven analysis reveals the true economics of performance — clarifying how revenues, costs, and capital interact under different market and regulatory conditions. Finance leaders can assess which activities generate sustainable returns, where inefficiencies persist, and how changes in business mix or funding strategy would affect profitability and capital resilience.

The benefit is a sharper view of enterprise performance and a stronger foundation for decision-making. CFOs gain the insight needed to allocate capital with confidence, anticipate shifts in portfolio value, and demonstrate to boards and investors that financial strategy is both data-driven and disciplined.

Board and investor engagement: strengthening the credibility of the finance narrative 

CFOs already carry accountability for the coherence of the finance narrative — ensuring that disclosures, board updates, and investor materials remain aligned and defensible under scrutiny. The challenge lies in sustaining that consistency across multiple reporting cycles and stakeholder demands, while the volume and speed of information continue to increase.

CFOs are using AI to reinforce the consistency and integrity of the finance narrative. By embedding AI into governed reporting environments, data and commentary remain aligned across disclosures, and inconsistencies are identified before they reach the boardroom. The result is a more coherent, defensible story of performance that strengthens confidence in every number communicated.

These capabilities support faster preparation and review while maintaining the assurance expected in external communication — creating a single version of truth across every disclosure and discussion. CFOs can engage with boards and investors backed by data that is current, consistent, and verifiable, reinforcing both credibility and trust in the numbers.

ESG and sustainability reporting: reinforcing credibility in a data-driven agenda

Sustainability and climate disclosures sit firmly within enterprise reporting, held to the same standards of accuracy and assurance as financial statements. For CFOs, the objective is clear: to produce sustainability data that is complete, traceable, and directly connected to the financial view of performance.

AI is strengthening this connection. Intelligent validation tools continuously test data quality and reconcile operational and financial metrics, while scenario-modelling techniques help quantify the potential financial impact of transition and physical risks. Generative technology supports alignment between narrative and underlying data, reducing the manual effort required to maintain consistency across disclosure frameworks.

The outcome is sustainability reporting that carries investor-grade credibility — data that is auditable, analysis that is repeatable, and disclosures that align with the enterprise’s broader financial story. By embedding ESG analytics within existing governance structures, CFOs reinforce confidence across financial and non-financial reporting — creating a coherent basis for capital allocation and strategic decision-making.

How CFOs can lead in 2026

CFOs are shaping the next phase of AI maturity in finance — one defined by depth of integration and the quality of outcomes it delivers. The priority is disciplined stewardship: ensuring that AI reinforces every stage of planning, close, and reporting with the same reliability and assurance that define financial control.

Leading finance teams are building unified performance architectures that connect finance, risk, and business data in real time. This foundation allows forecasts, reconciliations, and disclosures to be produced with greater consistency and transparency, supported by clear lineage and governed oversight. The result is insight that is faster to generate, easier to explain, and trusted across the enterprise.

CFOs leading this next phase are setting a new benchmark for financial credibility — demonstrating how well-governed AI strengthens both agility and assurance. The measure of leadership in 2026 will be finance functions that apply AI with rigour and clarity, raising the standard of decision-making, control, and investor confidence across the enterprise.

Revvence point of view

CFOs are refining how AI operates within finance — focusing on governance, reliability, and measurable value. The priority is to ensure AI supports every process with the same standards of accuracy, transparency, and assurance that define financial control.

At Revvence, our experience in banking and insurance — combined with deep Oracle expertise — informs a governance-led approach to AI in finance. We design architectures that embed AI directly within Oracle EPM and reporting platforms, extend PRA, ECB, and EBA model-risk standards into daily operations, and align IFRS, Basel, Solvency, and ESG/CSRD reporting on a unified control backbone. Each engagement is built around repeatability and measurable outcomes: shorter closes, fewer audit adjustments, and greater forecasting confidence.

Closing thought

AI is now a permanent layer of the finance operating model. The next horizon for CFOs is to refine its governance and measurement — ensuring that AI becomes a dependable source of precision, speed, and insight. Finance functions that achieve this maturity will set the benchmark for 2026 — delivering numbers that are timely, explainable, and trusted by boards, regulators, and investors.