We get it. Artificial intelligence is impressive. But how does it save money for CFOs?
Prithwijit Chaki has a take. As the Global Leader of Financial Advisory at Genpact, a global professional services company, Chaki helps finance executives use AI and data to drive measurable business results. With over two decades of experience advising companies on financial strategy and transformation, he has seen firsthand how businesses are reshaping their financial operations in the early AI era.
That vision takes on new dimensions with Genpact's alliance with Google Cloud, announced earlier this month. The partnership translates AI aspirations into production-ready tasks.
Global Finance asked Chaki how that idea stands and how the conversation is no longer just about how AI can improve productivity, but about the bottom-line business value.
Global Finance: CFOs have spent the past two years testing AI pilots. What is different in 2026?
Prithwijit Chaki: CFOs move from AI evaluation to AI accountability. After many years of piloting, the question is no longer whether AI can improve individual productivity, but whether those benefits translate into business value for every financial activity: faster close cycles, better working capital, a lower burden of manual revisions, stronger controls, or measurable business results.
According to Genpact/HFS researchreportinvestment in agent AI is expected to increase by 38% next year. However, 67% of businesses still rely on outdated productivity metrics that fail to capture the value of independent decision-making. That's the gap CFOs are trying to close in 2026: cutting through the 'sea of uniformity' in the AI market to determine which applications can deliver real, achievable value versus what adds noise.
GF: How is agent AI changing day-to-day financial operations?
Chalk: Traditional automation follows basic rules, and productive AI can help a person complete a task faster. Agent AI is moving forward. Works within the financial workflow – deciding, executing, learning, and planning work across processes and people who are knowledgeable where needed. In practical terms, that would mean moving from someone using a pilot to write a quick search letter to an integrated workflow that identifies appropriate action, drafts communications, routes exceptions, implements policy guidelines, and links work back to measurable business value.
GF: What is one example of cost savings or business impact that CFOs see from implementing agent AI?
Chalk: A good example is a global chain that a logistics company processes close to 3.5 million invoices per year. After a major consolidation, their finance team was dealing with disconnected ERP systems, heavy manual intervention, and slow differential maintenance—the kind of last-mile complexity that automation couldn't solve. Working with Genpact, they used our AI-powered Genpact AP Suite combined with our agent operating model – 21 pre-trained, domain-specific agents that automatically route, prioritize, and resolve invoice exceptions, with human experts confirming when needed.
GF: What were the results?
Chalk: Important. Processing untouchable invoices ranged from 7% to 65%. Invoicing cycle times are almost cut in half – from 18-29 days down to 9-14 days. On-time payment rate increased from 60% to 95%. Data extraction accuracy improved from 40% to 92%. And the program identified nearly $350 million in duplicate invoices, while early payment discounts taken grew from $35 million to $44 million — real dollars added to the bottom line.
This is not a pilot or a proof of concept. Agent AI that works at scale within the core workflow, delivers measurable cost savings, stronger cash flow, and a fundamentally better supplier experience. That's the kind of result that CFOs want.
GF: What financial activity is currently seeing the fastest returns from the use of AI—and why?
Chalk: Accounts payable is one of the clearest areas where finance teams can see tangible value. The process has a high volume and repeatable workflow, but also has a clear 'last mile' problem. Invoices, authorizations, exceptions, regulatory nuances, and disparate systems still require heavy manual intervention. Generic AI can automate most of the scheduled work. However, the final 20% requires domain-driven AI that understands real-world complexities, from merchant history and regional regulations to unique patterns, authorization chains, and master data issues. This is where agent AI can go beyond simple or automated extraction. It can begin to resolve discrepancies, increase variance, improve first-pass yields, reduce manual touch points, and shorten cycle times.
GF: With Genpact's expanded role with Google Cloud, what are CFOs asking hyperscalers right now? Is the discussion about cost reduction or something else?
Chalk: The CFO's conversation with hyperscalers has moved beyond 'what is the cheapest cloud?' or 'show me another AI demo.' CFOs are looking for productive financial operations that deliver real, measurable business results. That's what Genpact's alliance with Google Cloud aims to address. By pairing Google's AI infrastructure with Genpact's financial expertise, CFOs can improve forecasting accuracy, strengthen cash flow, and scale AI within their existing cloud environments.
The goal is not just to cut costs. It's about improving process efficiency and accuracy, freeing finance teams from manual work, improving decision-making, and giving CFOs a clear path from AI investments to strategic value.
GF: Are there safeguards that should be in place before agent AI can be trusted within a basic financial workflow?
Chalk: Think of the monitoring features of an agent's AI as needing to scale closely with the technology itself. If it affects the conditions of the use of funds more, it is more important to build controls directly into the work flow. What we are seeing today is the first wave of “agent authentication.” It operates on a machine-led, human-authorized model, combining efficient automation with professional oversight to ensure quality and compliance. Companies that will build tools with that future standard in mind—where monitoring and technology are measured together—will be the ones that truly innovate what finance is capable of.
GF: Are there any specific examples you can share of how you see AI empowering finance teams?
Chalk: We are already seeing AI reshaping the way finance teams spend their time. For accounts payable, for example, AI agents handle invoicing, three-way matching, and different routes. This work was used to feed all groups. In financial planning and analytics, AI accelerates variance analysis, generates narrative commentary about real-world events, and enables continuous forecasting that would otherwise be too time-consuming and impossible to implement manually. When it comes to recording for reporting, it compresses close cycles by making reconciliations and revealing anomalies before they become research problems.
GF: Are you expecting a layoff?
Chalk: The change this creates is less about job cuts and more about role evolution. Financial groups will not shrink overnight, but the composition will change. You'll see fewer people doing repetitive work and more people in judgmental roles, such as interpreting AI-generated data, managing agent workflows, overseeing controls, and partnering with the business on strategic decisions. The finance professional of the future looks more like a combination of business partner and orchestrator than processor.
Over the next three to five years, as agent AI matures and enterprise vendors begin to offer subscription-based financial capabilities built into all agent libraries, the operating model will change. Finance functions will become leaner, faster, and more intuitively motivated but the organizations that get there first will be the ones investing now in both technology and talent to work with them.
The post CFOs Have Seen the AI Demo—but Does It Work? appeared first on Global Finance magazine.
