CFOs Have Seen the AI Demo—but Does It Work?
We get it. Artificial intelligence is impressive. But how is it saving CFOs money?
Prithwijit Chaki has a take. As Global Leader for Finance Advisory at Genpact, a global professional services firm, Chaki helps chief financial officers harness AI and data to drive measurable business outcomes. With more than two decades of experience advising companies on finance strategy and large-scale transformation, he has seen firsthand how enterprises are rewiring their finance operations for an AI-first era.
That perspective takes on new dimensions with Genpact’s alliance with Google Cloud, announced earlier this month. The partnership translates AI ambition into production-ready operations.
Global Finance asked Chaki how that vision is taking shape and whether the conversation is no longer just about how AI can enhance productivity, but about bottom-line business value.
Prithwijit Chaki, Global Finance Advisory Leader, Genpact
Global Finance: CFOs have spent the last two years experimenting with AI pilots. What’s different in 2026?
Prithwijit Chaki: CFOs are moving from AI experimentation to AI accountability. After years of pilots, the question is no longer whether AI can improve individual productivity, but whether those gains translate into enterprise value across the finance function: faster close cycles, better working capital, lower manual review burden, stronger controls, or measurable business outcomes.
According to a Genpact/HFS Research report, investment in agentic AI is expected to rise 38% over the next year. However, 67% of enterprises still rely on outdated productivity metrics that fail to capture the value of autonomous decision-making. That’s the gap CFOs are trying to close in 2026: cutting through the ‘sea of sameness’ in the AI market to determine which applications can deliver real, achievable value versus which are simply adding to the noise.
GF: How does agentic AI change day-to-day finance operations?
Chaki: Traditional automation follows basic rules, and generative AI can help an individual complete a task faster. Agentic AI goes even further. It operates inside finance workflows — deciding, acting, learning, and orchestrating work across processes with people still in the loop where needed. In practical terms, that could mean moving from someone using a copilot to draft a dunning letter faster to a more integrated workflow that identifies the right action, drafts the communication, routes exceptions, applies policy guardrails, and connects the work back to measurable enterprise value.
GF: What’s one example of cost savings or business impact that CFOs see from implementing agentic AI?
Chaki: A good example is a global supply chain and distribution company processing close to 3.5 million invoices a year. After a major merger, their finance team was dealing with disconnected ERP systems, heavy manual intervention, and slow exception resolution—the kind of last-mile complexity that generic automation can’t solve. Working with Genpact, they deployed our AI-powered Genpact AP Suite combined with our agentic operations model — 21 pretrained, domain-specific AI agents that autonomously route, prioritize, and resolve invoice exceptions, with human experts validating where needed.
GF: What were the results?
Chaki: Significant. Touchless invoice processing went from 7% to 65%. Invoice cycle times were nearly halved — from 18–29 days down to 9–14 days. On-time payment rates jumped from 60% to 95%. Data extraction accuracy improved from 40% to 92%. And the system identified approximately $350 million in duplicate invoices, while early-payment discounts captured grew from $35 million to $44 million — real dollars added to the bottom line.
This isn’t a pilot or a proof of concept. It’s agentic AI operating at scale inside a core finance workflow, delivering measurable cost savings, stronger cash flow, and a fundamentally better supplier experience. That’s the kind of outcome CFOs are looking for.
GF: Which finance function is currently seeing the fastest returns from AI deployment—and why?
Chaki: Accounts payable is one of the clearest areas where finance teams can see tangible value. The process has high volume and repeatable workflows, but it also has a clear ‘last mile’ problem. Invoices, approvals, exceptions, regulatory nuances, and fragmented systems still require heavy manual intervention. Generic AI can automate a large share of structured work. However, the final 20% requires domain-driven AI that understands real-world complexity, from vendor history and regional rules to exception patterns, approval chains, and master data issues. That is where agentic AI can move beyond simple extraction or automation. It can start resolving mismatches, escalating exceptions, improving first-pass yield, reducing manual touchpoints, and shortening cycle times.
GF: Through Genpact’s expanded work with Google Cloud, what are CFOs specifically asking for from hyperscalers right now? Is the conversation more about cost reduction or something else?
Chaki: The CFO conversation with hyperscalers has moved beyond ‘what’s the cheapest cloud?’ or ‘show me another AI demo.’ CFOs want production-ready finance operations that deliver real, measurable business outcomes. That’s what Genpact’s alliance with Google Cloud aims to address. By pairing Google’s AI infrastructure with Genpact’s finance expertise, CFOs can improve forecasting accuracy, strengthen cash flow, and scale AI within their existing cloud environments.
The goal is not just to reduce costs. It’s about boosting process efficiency and accuracy, freeing finance teams from manual work, improving decision-making, and giving CFOs a clearer path from AI investment to strategic value.
GF: Are there any guardrails that must be in place before agentic AI can be trusted within core financial workflows?
Chaki: Think of the guardrails for agentic AI as needing to scale alongside the technology itself. The more finance use cases it touches, the more important it becomes to build controls directly into the workflow. What we’re seeing today is the first wave of “agent-ification.” It operates on a machine-led, human-validated model, combining automation efficiency with expert oversight to ensure quality and compliance. Companies will build tools with that future standard in mind—where the guardrails and technology scale together—will be the ones who truly innovate what finance is capable of.
GF: Are there specific examples you can share of how you see AI augmenting finance teams?
Chaki: We’re already seeing AI reshape how finance teams spend their time. In accounts payable, for example, AI agents are handling invoice extraction, three-way matching, and exception routing. This work used to consume entire teams. In financial planning and analysis, AI is accelerating variance analysis, generating narrative commentary on actuals, and enabling rolling forecasts that would have been extremely time-consuming and practically impractical to run manually. When it comes to record-to-report, it’s compressing close cycles by automating reconciliations and surfacing anomalies before they become audit issues.
GF: Do you expect job cuts?
Chaki: The shift this creates is less about job cuts and more about role evolution. Finance teams won’t shrink overnight, but the composition will change. You’ll see fewer people doing repetitive transactional work and more people in roles that require judgment, such as interpreting AI-generated insights, 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 a processor.
Over the next three to five years, as agentic AI matures and enterprise vendors begin offering subscription-based finance capabilities built on entire agentic libraries, the operating model will shift. Finance functions will become leaner, faster, and more insight-driven but the organizations that get there first will be the ones investing now in both technology and the talent to work alongside it.
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