CFOs

Commodity Hedging: Navigating Price Volatility

Lock in today’s painful prices, or bet that volatility breaks your way? CFOs are being forced to choose.

Commodity hedging is no longer a technical exercise buried in the treasury function. As price volatility spreads across energy, metals, and agricultural commodities, CFOs are forced to make explicit, high-stakes bets about the future — locking in costs at today’s elevated levels or staying exposed in the hope that markets turn. What was once a risk management tool has become a strategic decision with direct implications for margins, pricing, and competitive positioning.

That shift is showing up in earnings in a range of industries, a reminder that hedging decisions are increasingly tied to financial performance and investor expectations. “We are using our hedging to be able to offset against the volatility,” said Andrew Murray, CFO of Fonterra, a New Zealand farmer-owned cooperative, in the group’s March 2026 earnings call.

Others are treating volatility as an opportunity to act. In its latest earnings call, Infinity Natural Resources CEO Zack Arnold said the company had “taken this opportunity … to lock in attractive oil hedges,” stressing how companies are making deliberate market calls rather than waiting for conditions to stabilize. In some cases, the impact is measurable. In Siemens’ most recent earnings call, the global industrial giant’s CFO, Ralf P. Thomas, reported that commodity hedging contributed roughly 100 basis points to its margins, thanks to volatility in copper and silver prices.

New Visible, Strategic Role for Hedging

Power, it turns out, doesn’t come from military might anymore. It comes from metals and other elements, such as cobalt. That’s the argument threading through “The Elements of Power,” Nicolas Niarchos’ new book on the supply chains that hold modern civilization together — or fail to. Niarchos isn’t interested in geopolitics as it’s usually taught, the stuff of borders and aircraft carriers. He tracks something hard to see and now hard to ignore: the fragile networks of extraction, processing, and assembly that make electric vehicles move, smartphones think, AI infrastructure hum, and modern life move forward.

Those networks are long and exposed. Ore pulled from the ground in the Congo passes through Chinese processing facilities before it reaches a factory floor in Europe or America. A disruption anywhere, such as a mine shutdown, a trade restriction, or a sea strait closed by war, doesn’t stay local. It travels fast through prices and production timelines in ways that almost no one anticipated and fewer still knew how to hedge against.

What Niarchos documents is the moment supply chain risk graduated from a logistics problem to a strategic one. Hedging, once the quiet work of treasury and procurement desks, is becoming more like foreign policy.

Darrell E. Fletcher started his career hedging global energy for Alcoa and is now managing director of commodities at Bannockburn Capital Markets, the trading and advisory arm of First Financial Bank. He says the past two years have been “extraordinarily volatile” across energy and metals, forcing producers and fuel-consuming organizations to reassess their approach.

On the producer side, many firms are taking advantage of elevated prices to lock in forward revenues. “There has been a sharp increase in commercial hedgers … hedging the remainder of 2026 and into 2027,” Fletcher says, as companies secure cash flows above internal targets and support borrowing capacity. But strategies vary by size: the largest diversified oil majors often avoid hedging altogether, reflecting investor expectations that their equities provide direct exposure to commodity prices.

For organizations that consume fuel, the shift has been more reactive. Companies with established programs are extending hedges further in the future. Others are entering the market for the first time as price swings hit earnings. “Those who thought the exposure wasn’t meaningful realize it can be,” Fletcher says, noting a surge in conversations with CFOs and treasurers in recent months, some seeking help with a first-time hedging program.

The underlying issue may be less about timing the market than understanding exposure. “Eighty percent of any solid hedging program is: what is the exposure — and does it matter?” Fletcher says. He points to the importance of stress-testing cost sensitivity before implementing a strategy. He also warns that executives are being called out on earnings calls for failing to have a clear hedging rationale, backed by analysis. The mechanics of hedging are “the easy part,” he notes.

Plan vs. No Plan

That gap between companies with a plan and those without is something Charlie Macnamara sees firsthand. As head of commodity derivatives at US Bank — where his desk serves clients ranging from Permian Basin oil producers to auto manufacturers buying aluminum to EV companies sourcing lithium — Macnamara has a view of what separates hedging programs that work from those that don’t.

Charlie Macnamara,
US Bank

“The ones that get it wrong are the ones that don’t have a plan — and those are the ones where they let the movement of the market dictate what they need to do,” he says. The result can be a company that ends up buying the top, reacting to fear or surprise rather than executing a strategy, he adds.

Among the industries and organizations Macnamara describes as getting it right, oil and gas producers stand out. Despite the sharp swings in energy markets over the past year, industry players have remained notably disciplined, layering in hedges methodically, rather than chasing prices. “It’s been very cool, calm, and collected,” says Macnamara. That skill and maturity in hedging have been building for several years, he explains.

For CFOs considering a program for the first time, Macnamara suggests starting with the balance sheet rather than the market. “The plan should stem from how impactful the commodity is on their balance sheet and their cash flow volatility,” he says. From there, he says a finance team can define the level of volatility it wants to accept and structure derivatives or other market instruments accordingly.

A Boardroom Mindset Shift

Some organizations, and especially at the board level, need to rethink what hedging means, points out Macnamara. The people executing hedges on the ground, he says, often fear that if the hedge loses money, the C-suite will conclude they’ve done a poor job. He regards this view as misguided, and one that can paralyze programs before they get started.

“If you’re hedging 25% of your cash flow volatility and you lose money on that hedge, that means you’ve saved on 75% — you’ve just bought some insurance on the 25%,” he says. The philosophical hurdle is getting the entire organization to understand that a hedge is not meant to make money. It is meant to reduce volatility. “It sounds very simple, but that tends to be the biggest friction point,” he says.

Not everyone is convinced that locking in prices at today’s levels is the right move. Rob Handfield, Bank of America University Distinguished Professor of Supply Chain Management at NC State University and author of “Flow: How the Best Supply Chains Thrive,” urges caution about the assumption that financial hedging can adequately compensate for the unpredictability of physical supply chains. “Financial hedging assumes that individuals have a strong belief that supply and demand will move in one direction or another,” he says. “This is a challenging gamble.”

However, physical flows are difficult to forecast outside of periods of economic stability, according to Handfield. In the current environment, marked by geopolitical tensions, threats to key shipping lanes such as the Strait of Hormuz, and the resulting energy disruptions, the variables shaping commodity markets are too numerous and volatile to model confidently.

“Unless one has insider information on how governments are making decisions, these are very risky bets,” he says of positions in oil, gold, silver, copper, and other metals. And the consequences of disruption can be long-lasting. Handfield points out that rebuilding natural gas infrastructure alone could take at least a year.

A Matter of Restraint

On the critical question of whether to lock in today’s elevated prices, Handfield argues for restraint.

“I think locking in elevated prices is a mistake,” he says, expressing the view that once geopolitical tensions ease and supply routes normalize, volatility will likely diminish, thus rewarding companies that preserved optionality over those that locked in at the peak. The deeper conceptual issue, he argues, is that supply and demand are stochastic variables: “You can predict what might happen by what is happening today, but you don’t really know what will happen tomorrow.” Hedging makes most sense when prices are historically low, not in the middle of a supply chain crisis, Handfield believes.

That divide — between market practitioners who see today’s conditions as a hedging opportunity and supply chain strategists who warn against overconfidence in financial instruments — may be the central tension CFOs face heading into 2027. Fletcher and Handfield agree on at least one thing: most companies still underestimate how much commodity exposure matters to their bottom line. Where they diverge is on the remedy.

This article appears in the May 2026 issue of Global Finance Magazine.

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CFO Tariff Refunds: CFOs Expect a Long-Term Process

A massive $166 billion in corporate tariff refunds sounds nice, but could take years to process.

The U.S. Supreme Court’s ruling invalidating the Trump administration’s tariffs was a positive outcome for companies, but refunds may take years to materialize.

The Supreme Court decided in February that the U.S. Customs and Border Protection (CBP) agency illegally collected $166 billion from 300,000 importers. Logically, companies should get refunds, but lawyers don’t expect a smooth process. Importers should be prepared to wait for one year, even 18 months, according to TD Securities.

The federal agency set up an online portal called the Automated Commercial Environment to handle refunds. Once the agency accepts a company’s claim, it issues refunds within 60 to 90 days.

That’s the short-term optimistic resolution, but history shows a lot of things could go wrong. In 1998, the Supreme Court announced that the government had to return $750 million in fees collected between 1993 and 1998. It took years to get done. 

The CBP is set up to collect money quickly—but it doesn’t easily send it back. Companies must document a proper claim on the new portal. Some small business owners don’t understand the complex customs terminology, while others can’t even log in to the new portal due to technical glitches. Let’s say that the agency and the company don’t agree about the amount of the refund. The importer must submit new documentation and begin a second review process. Companies could even be forced to go to court.

CFOs should be ready for a long, fastidious process. The financial expert should set up a cross-functional task force—including tax, accounting, procurement, and supply chain experts—to review the data and audit all the company’s entries. When the time comes, the task force will be able to answer any CBP question.

The online portal created by the CBP agency focuses on importers, but they are not alone. Consumers could also say that they were overcharged because of the tariffs. The federal government ignores them, but some states don’t. Taking matters into his own hands, Illinois Democrat Governor JB Pritzker, in a letter to the Trump administration posted on soicial media, demanded an $8.7 billion refund—that’s $1,700 for each Illinois household affected.

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CFOs Have Seen the AI Demo—but Does It Work?

Finance leaders shift from AI experimentation to measurable ROI across corporate operations.

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
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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