如今，财务团队如何让人工智能发挥作用（英）

putting AI to work today Real-world examples reveal how finance functions apply AI to deliver fasterinsights, stronger controls, and measurable results. This article is a collaborative effort by Alexander Sukharevsky, Andy West, Cristina Catania, and Davide Grande,with Andrea Tricoli, representing views from McKinsey’s Strategy & Corporate Finance Practice. AI has dominatedbusiness headlines for the past two years, and finance functions are noexception. In a proprietary McKinsey survey of 102 CFOs across industries and global regions,44 percent of respondents said they used gen AI for over five use cases in 2025, up from 7percent in the previous year’s survey. Investment in AI tools is also growing: 65 percent of Yet the reality across the corporate landscape underscores how elusive tangible value remains:According to one study, only about 5 percent of AI pilots have translated into meaningful P&L impact.1Poor outcomes are largely due to pilots breaking down under real-world conditions,failing to adapt as new data emerges, and remaining poorly integrated into core processes. Some finance teams, however, are successfully using AI, gen AI—and increasingly, agenticAI—to boost efficiency, improve insights, and offload time-consuming manual tasks (see sidebar,“A guide to automation and AI terms.”) Rather than relying on isolated pilots, these organizations apply AI across foundational finance domains. We have observed some CFOs and their teamsusing AI to forecast more accurately, monitor working capital in real time, speed up reporting A guide to automation and AI terms As automation and AIbecome more embedded in finance, understanding the nuances among thesetechnologies is increasingly important. Below are some of the major terms shaping the field today. Automation:Rule-based technology that follows predefined instructions to complete repetitivetasks. Finance functions commonly use automation for processes such as checking and paying Artificial intelligence (AI):A broad category of technologies that augment human intelligence, suchas recognizing patterns, making predictions, or learning from data. AI is often used in forecasting, Generative AI (gen AI):A subset of AI that understands unstructured data—audio, code, images, andtext—and creates new content using foundational models. In the finance function, gen AI can handletasks including drafting commentary, summarizing performance, and supporting scenario modeling. Agentic AI:An emerging class of AI that can independently pursue goals, make decisions, and takeactions with limited human input. In the finance function, agentic AI can orchestrate time-consuming This article examines three areas where, based on our experience, finance teams aredeliveringthe most value with AI: strategic planning and control, cash and working-capital management,and cost optimization. Each section includes case studies that illustrate how leadingorganizations use gen AI and agentic systems to improve how finance functions operate. Finally, Strategic planning and control: How AI can deliver better insights Decision support tools, powered by a combination of predictive analytics and gen AI, make itfaster and easier to access company data, generate reports, and run forecasts or scenarios.These tools support finance leaders and their teams while also making data more accessible todecision-makers across the business. Typically, AI tools combine a few common capabilities: For example, at a global consumer goods company, a gen AI assistant helps financeprofessionals deliver insights on budget variances to business leaders in different divisions and In another example, a global biopharma company’s decision support agent, enabled by gen AIand agentic AI, cuts in half the time the finance team needs to make resource allocationdecisions. Instead of manually pulling reports and stitching together insights across functions,the team now generates complex scenarios using natural language during monthly planningsessions. The AI tool integrates data from multiple sources—including customer-relationship-management systems, financials, and marketing mix analytics—to surface performance alerts Finally, at a large North American financial institution, a gen AI tool helps generate first drafts ofreports that document internal risk model requirements and updates. The tool also assists ingenerating market-specific risk models by combining internal data with public sources, Specific AI implementation varies by organization, of course. Across a handful of financefunctions where it has been adopted robustly, we have observed that finance professionalsspend 20 to 30 percent less time crunching data. They devote the saved time to their role as that maintain appropriate security and hierarchical-access controls, AI tools also make it easier Cash and working capital management: How AI scrutinizes terms andinvoices for greater accuracy AI-powered agentic workflowsare enabling the next level o