Explore the most valuable generative AI applications transforming finance workflows, reporting, and decision-making.
Corporate finance teams are under pressure to deliver faster insights, tighter controls, and more agile forecasting—without expanding headcount or compromising accuracy. Generative AI offers a way to meet these demands, but only if deployed in the right places. The goal isn’t automation for its own sake—it’s measurable impact on cost, speed, and decision quality.
This framework outlines seven high-ROI use cases where generative AI can materially improve finance operations. Each reflects a repeatable pattern of value across enterprise environments, from shared services to FP&A and treasury.
1. Automating Month-End Close Narratives
Month-end reporting is a recurring bottleneck. While numbers are often available quickly, the narratives that explain them—variance analysis, commentary, executive summaries—require manual effort and cross-functional input. Generative AI can produce first-draft narratives based on structured data, historical trends, and business context.
This reduces reporting latency and frees teams to focus on interpretation rather than transcription. It also improves consistency across business units and geographies.
Use generative AI to accelerate close cycles and improve the clarity of financial narratives.
2. Drafting Budget and Forecast Commentary
Budgeting and forecasting require more than numbers—they demand context. Generative AI can assist by generating commentary that explains assumptions, highlights risks, and compares scenarios. This is especially useful in rolling forecasts or driver-based models where inputs shift frequently.
The impact is faster iteration and better alignment across stakeholders. It also supports more transparent planning cycles, especially in decentralized organizations.
Apply generative AI to streamline forecast documentation and improve planning transparency.
3. Summarizing Audit and Compliance Findings
Audit documentation is dense and repetitive. Generative AI can summarize findings, flag inconsistencies, and draft remediation plans based on historical reports and control frameworks. This reduces manual effort and improves traceability.
In regulated industries like financial services, this also supports better readiness for internal and external audits. It helps teams surface issues earlier and respond more effectively.
Use generative AI to compress audit cycles and improve documentation quality.
4. Accelerating Vendor Invoice Review and Dispute Resolution
Invoice processing is often automated—but dispute resolution is not. When discrepancies arise, finance teams must review contracts, purchase orders, and correspondence to determine next steps. Generative AI can assist by summarizing relevant documents and drafting response templates.
This reduces resolution time and lowers friction with vendors. In high-volume environments, it also improves cash flow visibility and working capital management.
Deploy generative AI to reduce invoice dispute resolution time and improve vendor relationships.
5. Generating Treasury and Liquidity Briefings
Treasury teams manage complex portfolios of cash, debt, and investments. Generative AI can produce daily or weekly briefings that summarize positions, highlight variances, and flag exposures—based on internal systems and market data.
This improves decision speed and supports better risk management. It also enables more frequent updates without increasing workload.
Apply generative AI to scale treasury reporting and improve liquidity oversight.
6. Drafting Policy Updates and Internal Controls Documentation
Finance policies and controls must evolve with regulation, risk posture, and business needs. Generative AI can assist by drafting updates based on regulatory changes, audit findings, or internal feedback. This reduces drafting cycles and improves consistency across documents.
In large enterprises, it also supports better version control and cross-functional alignment. It helps teams maintain clarity without sacrificing speed.
Use generative AI to streamline policy maintenance and improve control documentation.
7. Supporting M&A Due Diligence Summaries
Due diligence involves reviewing large volumes of financial data, contracts, and operational metrics. Generative AI can assist by summarizing key findings, drafting risk assessments, and generating comparison tables across targets.
This improves throughput and reduces reliance on manual synthesis. In time-sensitive transactions, it also supports faster decision-making and better deal execution.
Deploy generative AI to accelerate due diligence and improve deal readiness.
Generative AI is reshaping corporate finance—not by replacing expertise, but by amplifying it. The highest ROI comes from use cases that reduce friction, improve clarity, and scale insight across workflows. The key is to focus on repeatable tasks where AI can deliver measurable gains in speed, quality, and cost.
What’s one finance workflow where generative AI has helped improve speed or reduce manual effort? Examples: drafting forecast commentary, summarizing audit findings, generating treasury briefings.