University of Illinois Extension is guiding to help farmers understand their financial condition through balance sheet analysis as the Midwest agriculture sector faces another year of low profits.

A market-value balance sheet provides a snapshot of a farm’s financial condition by comparing current asset values to liabilities owed, according to Kevin Brooks, Extension educator in Havana.

Lenders use a traffic light system to evaluate farm financial health based on debt-to-asset ratios. Farms with debt ratios of 30% or less are considered financially strong, while ratios between 30% and 60% signal caution and may result in higher interest rates.

“A debt-to-asset ratio of more than 60% will make it challenging to secure a loan through traditional lenders,” Brooks said. Farms in this category may need to work with the Farm Service Agency as a lender of last resort.

Lenders also examine current ratios, calculated by dividing current assets by current liabilities. A ratio of at least 2.0 is considered strong, meaning the farm has $2 to pay each $1 of current debt.

Working capital provides another critical measure, representing the cash cushion farms have above expenses. Lenders typically require a 30% to 40% cushion to cover unexpected challenges.

Brooks emphasized the importance of honest financial reporting and maintaining strong lender relationships, especially during challenging economic conditions.

“Falsifying information on the balance sheet is a criminal offense,” he said. “Farmers have been convicted and imprisoned for bank fraud.”

Brooks advised farmers to keep lenders informed about purchase and debt plans, use realistic asset values and ensure balance sheets are consistent across all lenders.

For more information, contact Brooks at kwbrooks@illinois.edu or visit the Extension Farm Coach blog.

In covering AI, I’ve spoken with CFOs across industries who are focused on value creation and developing real-world use cases for AI to reshape everything from forecasting and financial planning to strategic decision-making. As data moves faster than ever, finance leaders are asking a new question: not what AI could do, but how it can truly transform the enterprise. I’ve also talked with industry experts and researchers about topics ranging from the ROI of AI to “prompt-a-thons” and debates over whether AI will turn CFOs into chief capital officers.

Finance chiefs are signaling the next big evolution—2026 will be the year of enterprise-scale AI. Pilot programs and proofs of concept are giving way to avenues for full-scale deployment as CFOs expect AI to deliver measurable value: faster decisions, leaner operations, and predictive insights that can provide a competitive edge. However, that level of transformation comes with new demands—governance, data integrity, and human oversight matter more than ever.

I recently asked finance chiefs from leading companies how they expect AI to redefine what it means to lead in finance. For instance, Zane Rowe, CFO at Workday, told me: “There has never been a more exciting time to be a CFO with AI unlocking new opportunities for value creation through unprecedented data and insights. Most of the focus has been on experimentation and discovering the art of the possible, but this year, leaders will shift from ‘What can AI do?’ to ‘How do we build the foundation for scale?’ They will manage a more nuanced AI portfolio that balances launching pilots with rolling out proven solutions, and they will prioritize the unglamorous but critical work of data governance, process redesign, and maintenance of new technologies. Success in 2026 will be defined by how we mature our AI strategy to ensure it is both agile, durable, and enterprise-grade.”

Shifting from the perspective of a major tech company to a beauty and cosmetics leader, Mandy Fields, CFO at e.l.f. Beauty offered this prediction: “From where a CFO sits, AI simultaneously helps broaden our view to get a better macro picture and can help put a sharper focus on very specific points of interest. e.l.f. Beauty is growing globally, and AI has visibility across it all. Going into next year, we’ll continue to explore how we best leverage AI in finance to lean into its strengths. It’s a pretty similar approach to our high-performance teamwork culture in which we encourage the team to pursue and thrive in the areas where they have expertise, learn continuously and move at e.l.f. speed.”

You can read more insights from over a dozen CFOs on how AI will shape finance in 2026 in my complete article here.

This is the final CFO Daily of 2025. The next issue will land in your inbox on Jan. 5. Thank you for your readership—and wishing you a wonderful holiday season. See you in 2026!

Sheryl Estrada
sheryl.estrada@fortune.com

Leaderboard

Greg Giometti was appointed interim CFO of Alight, Inc. (NYSE: ALIT), a cloud-based human capital and technology-enabled services provider, effective Jan. 9, 2026. Giometti, Alight’s SVP, head of financial planning and analysis, will succeed Jeremy Heaton, who will depart Alight to pursue an opportunity outside of the benefits administration industry. Giometti joined Alight in 2020 and has held positions of increasing responsibility within the company’s finance organization.

Shelley Thunen, CFO of ophthalmic medical device company RxSight, Inc., is transitioning out of her role. She will remain with the company until the earlier of her successor’s appointment or Jan. 31, 2026, and will continue to support RxSight as a consultant following the transition.

Big Deal

Bank of America CEO Brian Moynihan shared his outlook on the economy and AI for 2026, saying he expects continued strength ahead. During an interview with Bloomberg TV on Monday, Moynihan noted that BofA’s research team projects a strong U.S. economy next year—not only in absolute terms, with growth trending above 2%, but also relative to other major economies, many of which are expected to remain flat or decline. “That is because, frankly, the great American engine is driving,” he said. “Markets are valuing the future growth rate, and that’s why they’ve been very constructive this year.”

On AI, Moynihan said investment has accelerated throughout the year and will likely become an even bigger contributor in 2026 and beyond. He pointed to data center expansion as one key driver, along with increased corporate spending on AI—including Bank of America’s own investments. Spending on AI is higher than last year, he said, and while overall spending levels aren’t growing at a mid-single-digit rate, capital is clearly shifting toward AI.

Moynihan added that this trend supports the bank’s optimistic outlook for next year. “We think AI spending continues,” he said. There are benefits to the American taxpayer from tax rebates and lower taxes as the new tax bill takes effect, and the incentives for businesses are positive, he explained. Altogether, Moynihan said, those factors underpin BofA’s forecast for GDP growth rising from about 2% this year to roughly 2.4% in 2026—with AI playing an increasingly important, if still marginal, role in driving that strength.

Going deeper

In an episode of Fortune’s Leadership Next podcast, cohosts Diane Brady, executive editorial director, and Kristin Stoller, editorial director of Fortune Live Media, talk with Dani Richa. Richa is the chairman and group CEO of Impact BBDO International. The three discuss how the ad agency inspired the hit show Mad Men; how to use AI to bring out the best of you; and optimism in the rapidly developing EMEA region.

Overheard

“This year, we watched teams use AI to tackle work that had long felt out of reach. What struck me most was how different each story was. Different industries. Different constraints. Same ambition.”

—Sarah Friar, CFO at OpenAI, wrote in a LinkedIn post on Monday.

For payments, fraud detection and identity verification, the milliseconds lost in “round-tripping” data to a distant server represent more than just lag — they are a structural vulnerability. As the industry matures, the competitive frontier is shifting toward edge AI, moving the point of decision-making from the data center to the literal edge of the network — the ATM, the point-of-sale (POS) terminal, and the branch server.

From Batch Processing to Instant Inference

At the heart of this shift is inference, the moment a trained model applies its logic to a live transaction. While the cloud remains the ideal laboratory for training massive models, it is an increasingly inefficient theater for execution.

Financial workflows are rarely “batch” problems; they are “now” problems. Authorizing a high-value payment or flagging a suspicious login happens in a heartbeat. By moving inference into local gateways and on-premise infrastructure, institutions are effectively eliminating the “cloud tax” — the combined burden of latency, bandwidth costs and egress fees. This local execution isn’t just a technical preference; it’s a cost-control strategy. As transaction volumes surge, edge deployments offer a more predictable total cost of ownership (TCO) compared to the variable, often skyrocketing costs of cloud-only scaling.

Coverage from PYMNTS highlights how financial firms are transitioning from cloud-centric large models toward task-specific systems optimized for real-time operations and cost control.

From Cloud-Centric AI to Decision-Making at the Edge

The first wave of enterprise AI adoption leaned heavily on cloud infrastructure. Large models and centralized data lakes proved effective for analytics, forecasting and customer insights. But financial workflows are not batch problems. Authorizing a payment, flagging fraud or approving a cash withdrawal happens in milliseconds. Routing every decision process through a centralized cloud introduces latency, cost and operational risk.

Edge AI moves inference into branch servers, payment gateways and local infrastructure, enabling systems to decide without every query circling back to a central cloud. That local execution is especially critical in finance, where latency, privacy and compliance are business requirements.

Real-time processing at the edge trims costly round trips and avoids the cloud bandwidth and egress fees that accumulate at scale. CIO highlights that as inference volumes grow, edge deployments often deliver lower and more predictable total cost of ownership than cloud-only approaches.

Banks and payments providers are identifying specific edge use cases where local intelligence unlocks business value. Fraud detection systems at ATMs can use facial analytics and transaction context to assess threats in real time without routing sensitive video data, keeping customer information on-premise and reducing exposure.

Edge AI also supports smart branch automation, real-time risk scoring and adaptive security controls that respond instantly to contextual signals, functions that centralized cloud inference cannot economically replicate at transaction scale.

Edge AI delivers clear operational and governance advantages by reducing bandwidth use, cloud dependency and attack surface. Keeping decision logic local also simplifies compliance by limiting unnecessary data movement, a priority for regulated financial institutions.

Edge AI Stack Is Coalescing Across the Tech Industry

The broader tech ecosystem reinforces this trend. As reported by Reuters, chipmakers such as Arm are expanding edge-optimized AI licensing programs to accelerate on-device inference development, reflecting growing conviction that distributed AI will capture a larger share of enterprise compute workloads. Nvidia is advancing that shift through platforms such as EGX, Jetson and IGX, which bring accelerated computing and real-time inference into enterprise, industrial and infrastructure environments where latency and reliability matter.

Intel is taking a similar approach by integrating AI accelerators such as its Gaudi 3 chips into hybrid architectures and partnering with providers including IBM to push scalable, secure inference closer to users. IBM, in turn, is embedding AI across hybrid cloud and edge deployments through its watsonx platform and enterprise services, with an emphasis on governance, integration and control.

In financial services, these converging moves make edge AI more than a deployment option. It is increasingly the infrastructure layer for enterprise AI, enabling institutions to embed intelligence directly into transaction flows while maintaining discipline over cost, risk and operational continuity.