Generative AI is ready to partner with your finance team, boosting efficiency and insight across corporate finance. Let's explore the top ways finance leaders are deploying it to drive measurable impact.
In this article we discuss:
Generative AI (genAI) adoption is growing rapidly across the business world. Thanks to the ability to generate new content quickly and at scale, it’s saving teams across industries and functions hours of time they can reallocate to higher-level work.
One area where genAI is making the most impact is corporate finance, where AI is being used to synthesize vast datasets into predictive insights, scenarios, reports, and audit-ready documentation.
Finance leaders are watching closely because generative AI speaks directly to their toughest challenges: delivering insights at greater speed, raising the sophistication of forecasts and analyses, and doing so without expanding headcount.
A recent study by Bain Capital found that support for generative AI adoption at the CFO level is nearly unanimous—94% say they see it strongly benefitting at least one activity within the finance function in the next 12 months, and three-quarters plan to increase their AI budgets accordingly.
Today, generative AI in finance is finding traction across use cases that cover everything from forecasting and planning to financial reporting to enterprise risk management to treasury and more.
By its nature, the finance function is positioned for strong AI adoption due to the amount of structured data and demand for speed and accuracy to drive decisions. Accordingly, the industry is already experiencing adoption at a high rate. Respondents surveyed for the Workday Global CFO AI Indicator Report identified essential skills like data literacy, the ability to work closely with new technology, and advanced analytics as skills in full alignment with AI solution capabilities.
Across corporate finance use cases, genAI is emerging as a critical enabler by:
Automating routine tasks: Eliminating repetitive work like reconciliations and data entry so finance teams have bandwidth for high-impact projects.
Synthesizing vast datasets: Handling large datasets and translating them into narratives, dashboards, and insights non-technical leaders can act on.
Generating predictive scenarios: Modeling business outcomes, from revenue shocks to cost fluctuations, so decision-makers can stress-test their strategies before committing.
Enhancing compliance monitoring: Continuously scanning transactions, flagging anomalies, and generating audit-ready records that help strengthen oversight and reduce regulatory risk.
Supporting strategic analysis: Giving analysts time to analyze scenarios, track trends, and pursue opportunities rather than spending hours on clerical work.
With these capabilities come new levels of responsibility. Finance teams must balance the speed and efficiency of AI with commitment to accuracy, privacy, and explainability.
But when applied thoughtfully, generative AI doesn’t just make existing processes faster—it elevates the role of finance as a trusted partner for shaping strategy and guiding the business forward.
CFOs report that top essential finance skills—data literacy, advanced analytics, and more—already align with AI solution capabilities.
Generative AI in finance is moving quickly from experimentation to full deployment. It’s leveling up data analysis capabilities and delivering sophisticated insights that drive smarter decisions across the enterprise. Here are 7 key areas where it’s making the most impact.
Forecasting is at the heart of corporate finance, but too often projections are outdated by the time they reach leadership. Teams spend weeks reworking assumptions only to find they’ve missed critical shifts in the market or supply chain. As a result, leaders make decisions on stale information.
How generative AI helps:
Dynamic scenario modeling: Builds forecasts that refresh as soon as new data arrives, keeping projections current.
Stress testing assumptions: Simulates shocks—such as rate hikes, supply shortages, or demand spikes—to show how plans hold up under pressure.
Automated updates: Reduces manual spreadsheet work by pulling in live data and recalibrating models continuously.
With genAI in place, finance can move toward more continuous planning models and guide the business with forward-looking insights that are always accurate and current.
Closing the books is a notoriously time-consuming process. Teams spend countless hours consolidating data, reconciling inconsistencies, and formatting reports for executives. Often, too much effort goes into producing reports and not enough into analyzing what they actually mean.
How generative AI helps:
Automated drafting: Creates first-pass management reports, variance analyses, and executive summaries in minutes.
Driver analysis: Surfaces the real factors behind changes in revenue, costs, or margins instead of just showing raw numbers.
Data consolidation: Pulls information from multiple systems into a single, consistent output.
The payoff is a faster and more insightful close process. Finance teams can shift their focus from clerical assembly to interpreting results and advising leadership. Artificial intelligence accelerates the mechanics, while human teams add judgment and context.
Payment cycles are a frequent source of friction in finance. Manual invoice handling slows down approvals, mismatched records create delays, and inconsistent outreach can damage vendor and customer relationships. The net effect is weaker liquidity and strained trust across the value chain.
How generative AI helps:
Invoice capture: Digitizes and categorizes invoices with greater speed and fewer errors, reducing the bottleneck at the start of the cycle.
Automated matching: Compares invoices against purchase orders and payments in real time, cutting down on disputes and approval delays.
Tailored outreach: Generates payment reminders or follow‑ups that reflect account history and tone, supporting smoother customer and vendor interactions.
GenAI-powered automated invoice processing software gives finance teams more predictable cash flow and stronger working capital. Leaders still need to monitor accuracy and alignment with policy, but genAI takes the repetitive administrative burden from AP and AR so staff can focus on exceptions and higher‑value activities.
Risk and compliance functions are under more strain than ever. Finance teams must track rising transaction volumes while proving to regulators and auditors that controls are tight and documentation is complete. The challenge is scale: Manual reviews can’t keep up, and issues are often only spotted after the fact.
How generative AI helps:
Real-time anomaly detection: Monitors every transaction continuously and flags activity that falls outside expected patterns.
Regulatory documentation: Automatically compiles compliance-ready reports, reducing the time and cost of audit preparation.
Scenario modeling: Tests potential fraud schemes or regulatory changes to identify vulnerabilities before they become real problems.
Used well, these capabilities allow finance leaders to catch problems earlier and strengthen compliance posture while saving time spent on routine checks.
Treasury functions sit at the intersection of liquidity, risk, and market volatility. The challenge is that traditional forecasting tools often lag behind reality, leaving treasurers reacting after conditions have already shifted.
How generative AI helps:
Liquidity forecasts: Refresh projections automatically as new inflows and outflows occur, giving leaders a real-time view of cash positions.
Market shock simulations: Model sudden events—currency swings, rate changes, or geopolitical shocks—to stress test strategies before they are needed.
Decision support: Present side-by-side scenarios that allow treasurers to compare options and choose the most resilient path forward.
With these capabilities, treasury leaders gain the foresight to act ahead of disruptions instead of responding after the fact. Finance leaders can refine funding and hedging moves continuously to align actions with market conditions as they unfold.
Investor relations is one of the most visible finance functions, where accuracy and speed directly shape market confidence. The pressure point is clear: IR teams must respond quickly to analyst questions and investor concerns while ensuring every statement is consistent and compliant.
How generative AI helps:
Script drafting: Produces early versions of earnings call scripts and presentations, reducing prep time.
Q&A preparation: Anticipates likely analyst questions and proposes structured, compliant responses.
Segment-specific updates: Creates tailored communications for different investor groups, from institutions to retail shareholders.
Handled well, generative AI allows IR teams to deliver information faster without compromising quality. The technology speeds up drafting, while professionals refine tone, context, and accuracy to protect credibility and build stronger investor relationships.
Audit and controls safeguard organizational integrity, but teams are often buried in documentation and manual reviews. Strain occurs from balancing the need for thorough oversight with the volume of transactions and evidence required.
How generative AI helps:
Workpaper creation: Drafts audit documentation automatically, reducing administrative burden.
Exception alerts: Flags unusual transactions in real time for deeper investigation.
Continuous audit enablement: Supports ongoing monitoring rather than relying solely on periodic checks.
These capabilities change the audit process from being bogged down in documents to primarily insights-driven. Routine paperwork and exception tracking can be automated, which gives auditors more time to dig into the root causes of anomalies and evaluate the effectiveness of controls.
The smartest path toward generative AI adoption is a phased approach that builds momentum on small wins.
For most finance organizations, the question isn’t whether or not to explore generative AI but how to begin responsibly. The smartest path forward is a phased approach that builds momentum on small wins.
Pilots are an effective entry point. Choose a focused use case where benefits can be measured quickly. Define clear success metrics at the outset, from cycle time reductions to accuracy improvements, so you can calculate ROI with confidence.
Preparing your people for AI adoption is equally important. Generative AI changes how finance teams work and collaborate with technology in their roles. Change management, communication, and training must all be built into your generative AI rollout plan.
Finally, investing in upskilling initiatives for your team is crucial for making the most of your AI tools from day one. When teams are comfortable and confident with genAI, they use it more readily and in ways that actually make an impact.
AI holds huge potential, 98% of CEOs foresee immediate business benefits. Download this report to discover the potential positive impact on your company, with insights from 2,355 global leaders.
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