We get it. Synthetic intelligence is spectacular. However how is it saving CFOs cash?
Prithwijit Chaki has a take. As World Chief for Finance Advisory at Genpact, a world skilled providers agency, Chaki helps chief monetary officers harness AI and knowledge to drive measurable enterprise outcomes. With greater than twenty years of expertise advising firms on finance technique and large-scale transformation, he has seen firsthand how enterprises are rewiring their finance operations for an AI-first period.
That perspective takes on new dimensions with Genpact’s alliance with Google Cloud, introduced earlier this month. The partnership interprets AI ambition into production-ready operations.
World Finance requested Chaki how that imaginative and prescient is taking form and whether or not the dialog is now not nearly how AI can improve productiveness, however about bottom-line enterprise worth.
World Finance: CFOs have spent the final two years experimenting with AI pilots. What’s completely different in 2026?
Prithwijit Chaki: CFOs are shifting from AI experimentation to AI accountability. After years of pilots, the query is now not whether or not AI can enhance particular person productiveness, however whether or not these positive aspects translate into enterprise worth throughout the finance perform: sooner shut cycles, higher working capital, decrease handbook evaluation burden, stronger controls, or measurable enterprise outcomes.
In keeping with a Genpact/HFS Analysis report, funding in agentic AI is predicted to rise 38% over the subsequent yr. Nevertheless, 67% of enterprises nonetheless depend on outdated productiveness metrics that fail to seize the worth of autonomous decision-making. That’s the hole CFOs try to shut in 2026: reducing via the ‘sea of sameness’ in the AI market to find out which purposes can ship actual, achievable worth versus that are merely including to the noise.
GF: How does agentic AI change day-to-day finance operations?
Chaki: Conventional automation follows primary guidelines, and generative AI might help a person full a activity sooner. Agentic AI goes even additional. It operates inside finance workflows — deciding, appearing, studying, and orchestrating work throughout processes with individuals nonetheless in the loop the place wanted. In sensible phrases, that would imply shifting from somebody utilizing a copilot to draft a dunning letter sooner to a extra built-in workflow that identifies the proper motion, drafts the communication, routes exceptions, applies coverage guardrails, and connects the work again to measurable enterprise worth.
GF: What’s one instance of price financial savings or enterprise influence that CFOs see from implementing agentic AI?
Chaki: A great instance is a world provide chain and distribution firm processing shut to three.5 million invoices a yr. After a significant merger, their finance crew was coping with disconnected ERP programs, heavy handbook intervention, and gradual exception decision—the type of last-mile complexity that generic automation can’t remedy. Working with Genpact, they deployed our AI-powered Genpact AP Suite mixed with our agentic operations mannequin — 21 pretrained, domain-specific AI brokers that autonomously route, prioritize, and resolve bill exceptions, with human consultants validating the place wanted.
GF: What had been the outcomes?
Chaki: Important. Touchless bill processing went from 7% to 65%. Bill cycle occasions had been almost halved — from 18–29 days right down to 9–14 days. On-time cost charges jumped from 60% to 95%. Knowledge extraction accuracy improved from 40% to 92%. And the system recognized roughly $350 million in duplicate invoices, whereas early-payment reductions captured grew from $35 million to $44 million — actual {dollars} added to the backside line.
This isn’t a pilot or a proof of idea. It’s agentic AI working at scale inside a core finance workflow, delivering measurable price financial savings, stronger money circulation, and a basically higher provider expertise. That’s the type of end result CFOs are searching for.
GF: Which finance perform is at the moment seeing the quickest returns from AI deployment—and why?
Chaki: Accounts payable is one in all the clearest areas the place finance groups can see tangible worth. The method has excessive quantity and repeatable workflows, nevertheless it additionally has a transparent ‘final mile’ drawback. Invoices, approvals, exceptions, regulatory nuances, and fragmented programs nonetheless require heavy handbook intervention. Generic AI can automate a big share of structured work. Nevertheless, the closing 20% requires domain-driven AI that understands real-world complexity, from vendor historical past and regional guidelines to exception patterns, approval chains, and grasp knowledge points. That’s the place agentic AI can transfer past easy extraction or automation. It can begin resolving mismatches, escalating exceptions, enhancing first-pass yield, decreasing handbook touchpoints, and shortening cycle occasions.
GF: By means of Genpact’s expanded work with Google Cloud, what are CFOs particularly asking for from hyperscalers proper now? Is the dialog extra about price discount or one thing else?
Chaki: The CFO dialog with hyperscalers has moved past ‘what’s the most cost-effective cloud?’ or ‘present me one other AI demo.’ CFOs need production-ready finance operations that ship actual, measurable enterprise outcomes. That’s what Genpact’s alliance with Google Cloud goals to handle. By pairing Google’s AI infrastructure with Genpact’s finance experience, CFOs can enhance forecasting accuracy, strengthen money circulation, and scale AI inside their current cloud environments.
The purpose is not only to scale back prices. It’s about boosting course of effectivity and accuracy, liberating finance groups from handbook work, enhancing decision-making, and giving CFOs a clearer path from AI funding to strategic worth.
GF: Are there any guardrails that should be in place earlier than agentic AI could be trusted inside core monetary workflows?
Chaki: Consider the guardrails for agentic AI as needing to scale alongside the expertise itself. The extra finance use circumstances it touches, the extra essential it turns into to construct controls straight into the workflow. What we’re seeing as we speak is the first wave of “agent-ification.” It operates on a machine-led, human-validated mannequin, combining automation effectivity with knowledgeable oversight to make sure high quality and compliance. Firms will construct instruments with that future normal in thoughts—the place the guardrails and expertise scale collectively—might be the ones who really innovate what finance is able to.
GF: Are there particular examples you’ll be able to share of the way you see AI augmenting finance groups?
Chaki: We’re already seeing AI reshape how finance groups spend their time. In accounts payable, for instance, AI brokers are dealing with bill extraction, three-way matching, and exception routing. This work used to devour whole groups. In monetary planning and evaluation, AI is accelerating variance evaluation, producing narrative commentary on actuals, and enabling rolling forecasts that might have been extraordinarily time-consuming and virtually impractical to run manually. Relating to record-to-report, it’s compressing shut cycles by automating reconciliations and surfacing anomalies earlier than they turn out to be audit points.
GF: Do you anticipate job cuts?
Chaki: The shift this creates is much less about job cuts and extra about position evolution. Finance groups received’t shrink in a single day, however the composition will change. You’ll see fewer individuals doing repetitive transactional work and extra individuals in roles that require judgment, corresponding to deciphering AI-generated insights, managing agent workflows, overseeing controls, and partnering with the enterprise on strategic choices. The finance skilled of the future seems extra like a mix of enterprise companion and orchestrator than a processor.
Over the subsequent three to 5 years, as agentic AI matures and enterprise distributors start providing subscription-based finance capabilities constructed on whole agentic libraries, the working mannequin will shift. Finance capabilities will turn out to be leaner, sooner, and extra insight-driven however the organizations that get there first might be the ones investing now in each expertise and the expertise to work alongside it.
The publish CFOs Have Seen the AI Demo—but Does It Work? appeared first on World Finance Journal.
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