For many Chief Financial Officers, direct spend – the money spent on direct materials and services that go into a company’s products – remains an underappreciated lever. Historically, direct spend has been viewed as a cost of goods sold to control, largely managed by procurement and operations. Yet this perspective is changing, and for good reason. In product-centric industries, direct materials can represent the largest portion of total expenditures – often up to 80% of overall spend. Ignoring such a substantial cost driver is a missed opportunity. By elevating direct spend from a mere cost center to a strategic focus, CFOs can unlock new margin improvements, optimize cash flow, and strengthen supply chain resilience.
Direct Spend: The CFO’s Overlooked Priority
CFOs are increasingly recognizing that direct spend deserves more attention at the executive level. According to a recent Coupa Strategic CFO survey, 39% of CFOs still view direct spend as a challenge or basic cost center, while about 60% acknowledge it as strategic but in need of better alignment with business goals. In other words, virtually all finance leaders know there is untapped value in this area, but many have yet to actively seize it. This gap in focus represents a critical blind spot. Direct spend is the largest and most influential cost driver on the income statement, impacting gross margins, cost of goods sold (COGS), and ultimately the bottom line. Treating it with a “blind eye” or leaving it solely to the operational side means leaving money on the table and exposing the company to avoidable risks.
Why has direct spend historically been overlooked by CFOs?
One reason is organizational silos: procurement and supply chain teams traditionally manage supplier negotiations, bills of materials, and production inputs, while Finance tracks financial outcomes. CFOs have tended to focus on indirect spend (SG&A and overhead costs) where they have more direct control and visibility. Indirect procurement improvements (e.g. cutting discretionary spend or automating procure-to-pay) have been championed by Finance in many firms. Meanwhile, direct spend processes often run on legacy ERP systems or spreadsheets, with CFO involvement limited to approving budgets or reviewing variances. This separation can make direct spend feel “out of sight, out of mind” for finance leaders.
However, the volatility of recent years – from supply disruptions to commodity price swings – has underscored that direct spend is far from a fixed cost of doing business. CFOs who prioritize direct spend management can transform it from an operational necessity into a strategic performance lever. The potential upsides are significant: even a few percentage points reduction in direct material costs can translate into substantial margin expansion. Improved procurement of direct inputs can free up cash, reduce balance sheet inventory, and avert expensive production delays. In short, direct spend isn’t just about cost control – it’s about value creation and risk mitigation at an enterprise level.
From Blind Spot to Strategic Driver: The Business Case for Focus
Leading organizations are now turning their attention to direct spend as a frontier for financial improvement. What can CFOs gain by shining a spotlight here? In Coupa’s recent CFO Direct Spend Masterclass, experts outlined how refocusing on direct spend can turn this area from a “blind spot” into a strategic growth driver. Key benefits include:
Visibility & Cost Control: Gaining end-to-end visibility into direct spend helps find hidden costs – from procurement process inefficiencies to unexpected freight charges or supplier price hikes – before they hit the financials. With better data, CFOs can identify and eliminate waste, ensuring that every dollar spent on raw materials or components is competitive and justified. This proactive cost management directly protects profit margins. Working Capital Optimization: Tight oversight of direct spend can reduce the cash-to-cash cycle. Often, lack of coordination in purchasing and production leads to overstocked inventory or obsolete materials, which tie up cash and increase holding costs. By aligning procurement with demand and eliminating excess stock, companies reclaim trapped working capital and free up cash for strategic initiatives. In financial terms, this means lower Days Inventory Outstanding and a stronger liquidity position – outcomes any CFO can applaud. Supply Continuity & Risk Reduction: Direct spend focus goes hand-in-hand with supply chain resilience. CFOs who engage in direct procurement strategy push for stronger supplier relationships and diversification of sources for critical materials. This ensures supply continuity and reduces the risk of costly disruptions (like line shutdowns or expedited shipping fees due to shortages). The financial translation is fewer surprise expenses and more stable revenue delivery. In an unpredictable global environment, such resilience planning is a strategic asset. Improved Forecasting & Predictability: When Finance works closely with procurement on direct spend, it enhances forecasting accuracy for COGS and margins. CFOs can get ahead of commodity price fluctuations or foreign exchange impacts on input costs. With integrated data and scenario planning, leaders make more confident, data-driven decisions about pricing, sourcing, and inventory. The result is greater predictability in financial outcomes, which translates to more reliable earnings forecasts and reduced volatility – a key concern for boards and investors.
In sum, by treating direct spend as a strategic driver, CFOs can cut inefficiencies, boost cash flow, and safeguard the business’s profitability. The conversation shifts from “How do we minimize this cost?” to “How do we leverage this spend for competitive advantage?” This is the essence of turning direct spend from a mere cost center into a growth lever.
Speaking the CFO’s Language: Translating Procurement Value into Financial Impact
A crucial element in bringing focus to direct spend is financial translation – the ability of procurement leaders to frame their initiatives in terms that resonate with CFOs and finance teams. Procurement may inherently understand the operational value of, say, qualifying a second-source supplier or negotiating longer payment terms. But to get full C-suite buy-in, those efforts must be expressed in financial outcomes like margin improvement, risk reduction, or cash flow enhancement. In other words, procurement needs to speak the CFO’s language.
Consider the following examples of how procurement initiatives around direct spend can be translated into finance-centric metrics:
Negotiated 5% cost reduction on key raw materials Lower Cost of Goods Sold, boosting gross margin and EBITDA.
Consolidated suppliers for volume advantages Improved pricing and reduced vendor management overhead, directly improving profitability.
Improved on-time delivery with key suppliers Fewer production delays and expedite costs, protecting revenue and avoiding unexpected expenses.
Optimized inventory levels through better planning Freed-up cash from inventory (lower working capital requirements), improving cash flow and liquidity.
Extended payment terms (or dynamic discounting) Better cash conversion cycle – either by holding cash longer or earning early pay discounts, contributing to interest savings and higher free cash flow.
In each case, the procurement action is mapped to a tangible financial result. This kind of translation is powerful. It not only helps the CFO understand the value of direct spend initiatives, but also ensures that procurement and finance are aligned on common goals. For instance, a procurement team’s success in negotiating savings should visibly move the needle on gross margin or EBITDA – and if it doesn’t, both sides can investigate why (e.g. leakage, demand changes, etc.). By establishing this shared language, CFOs are more likely to support investment in procurement tools or process improvements, because the ROI is clear in financial terms.
Procurement leaders can facilitate this by developing dashboards and reports that bridge operational metrics with financial KPIs. Instead of reporting “savings achieved” in procurement terms, they can report impact on COGS or working capital in finance terms. Likewise, risk mitigation efforts (like qualifying backup suppliers for a sole-sourced component) can be translated into avoided revenue loss or quantified risk reduction. The more procurement can illustrate direct spend management as driving business outcomes – not just procurement department outcomes – the more attention and resources CFOs will devote to it.
A Path Forward: Aligning Finance and Procurement (the S2P Framework)
How can CFOs and procurement leaders put these ideas into practice? It requires a collaborative approach and often, enabling technology. One strategic move is adopting an integrated Source-to-Pay (S2P) framework that unifies processes from sourcing all the way through procurement and payment. In the past, direct procurement activities (like supplier selection, contract management, purchase planning) often lived in separate systems from the financial side (purchase orders, invoices, payments). Today, modern S2P platforms are breaking down these silos. For example, Coupa’s unified design-to-pay platform provides one place to manage all spend – direct and indirect – with end-to-end visibility. Such a system connects the dots: sourcing events, contracts, and purchase orders for direct materials flow seamlessly into the accounts payable and spend analysis process.
The S2P approach means CFOs can finally get a comprehensive view of total spend. With guided workflows and real-time data, finance and procurement teams are literally on the same page – seeing the same numbers, trends, and risks. An integrated platform enables prescriptive insights: for instance, AI-driven analytics might flag that a spike in commodity price is driving up costs in a certain category, prompting procurement to act before it impacts the P&L. Or it could show that inventory on hand for a critical item is above optimal levels, prompting a strategic review of purchasing frequency. In short, S2P tools help translate operational data into the financial impact quickly, which aligns everyone on priorities.
Of course, technology alone isn’t a silver bullet. CFOs should also foster a culture of partnership with procurement. This means involving procurement leaders in strategic planning and budgeting discussions, and vice versa – letting finance have insight into procurement’s supplier strategies and challenges. Joint KPI setting is useful: for example, target a certain reduction in COGS % or a boost in inventory turns, and make it a shared objective for both finance and procurement. Regular executive reviews of direct spend performance (just as many companies do for indirect spend or SG&A budgets) can keep the focus sharp.
Ultimately, making direct spend a CFO priority is about connecting the dots between the shop floor and the balance sheet. When CFOs treat direct expenditures not as a black box to be managed by others, but as a strategic domain where they can apply financial leadership, the business stands to gain. The biggest cost line item becomes a source of competitive advantage – driving cost efficiency, supporting growth, and insulating the company from shocks.
These insights are drawn from Coupa’s Source-to-Pay framework and a recent CFO Direct Spend Masterclass session (available here). By translating operational improvements into financial outcomes, CFOs and procurement leaders together can turn direct spend from a blind spot into a bright spot on the executive agenda – one that delivers real dollars-and-cents value to the enterprise.
By Nari Viswanathan – Sr. Director, Product Segment Marketing, Coupa
Nari is currently Sr. Director of Product Segment Marketing at Coupa, where he brings products to markets in the areas of Direct Material Procurement and Supply Chain Design and Planning. Over the past 20 years, Nari has held VP and Director of Product Management, Research and Marketing roles at Aberdeen Group, River Logic, Steelwedge and E2open. He has significant experience building products from the ground up and managing the P&L for a product suite. He is a proven B2B marketer with expertise in content marketing, competitive intelligence, and positioning. He has published numerous thought leadership articles, whitepapers, blogs and delivered dozens of webinars during his career. Nari Viswanathan is a six times SDCExec Supply Chain Pro to Know award winner. Nari holds a master’s degree in Manufacturing Systems Engineering at the University of Wisconsin-Madison and a bachelor’s degree in Mechanical Engineering at the Indian Institute of Technology, Chennai.
Supply Chain and Logistics News February 23rd- 26th 2026
Published
24 heures ago
on
27 février 2026
By
This week’s supply chain landscape is defined by a massive push to bridge the gap between having data and actually using it. From the high-stakes legal battle over billion-dollar tariffs to a radical AI-driven workforce restructuring at WiseTech Global, the industry is moving past simple visibility toward a period of high-consequence execution. Whether it is the Supreme Court’s intervention in trade policy or the operationalization of decision intelligence showcased at the 30th Annual ARC Forum, the recurring theme is clear: the next competitive advantage belongs to those who can synchronize their technology, their inventory, and their legal strategies in real time. In this edition, we break down the four critical shifts—architectural, legal, operational, and structural—shaping the final days of February 2026.
Richard Stewart of Infios argues that the primary technology gap in modern supply chain execution is not a lack of ambition or budget, but rather an architectural failure. Most existing systems, such as WMS and TMS, are designed to optimize within their own silos, leaving a critical disconnect during real-time disruptions where manual workarounds and spreadsheets are still required to coordinate responses. Citing the Supply Chain Execution Readiness Report, Richard highlights that 69% of leaders struggle with data quality and integration, driving a shift in buying criteria toward interoperability and real-time visibility. Ultimately, Richard suggests that the next competitive advantage will belong to organizations that move beyond simple visibility toward “connected execution,” prioritizing modular architectures that synchronize decisions across the entire operational landscape rather than just reporting on them.
FedEx has officiallyfiled a lawsuit against the US government, seeking a full refund for duties paid under the Trump administration’s recent tariff policies. The move follows a landmark 6-3 Supreme Court ruling that found the president overstepped his authority by using emergency powers to bypass Congress’s sole power to levy taxes. While the court’s decision stopped the specific enforcement mechanism, it left the status of the estimated $175 billion already collected in limbo. As the first major carrier to seek reimbursement, FedEx’s legal challenge could set a precedent that could affect the logistics industry and thousands of other importers currently navigating a volatile trade environment.
Hiu Wai Loh sheds light on the hidden inventory crisis and the costly returns black hole that plagues supply chains long after peak season ends. The research reveals that a staggering number of organizations suffer from fragmented data, leading to false stockouts and millions of dollars trapped in reverse logistics limbo. To overcome these operational blind spots, the author argues that companies must tear down silos and adopt a unified, real-time inventory model. By leveraging AI-driven smart disposition, businesses can efficiently route returns to their most profitable next destination, transforming a traditional cost center into a powerful engine for full-price recovery and year-round agility.
Avantor and Aera Technology were present at the 30th Annual ARC Forum and presented on how they are operationalizing Decision Intelligence. They explore how modern supply chains are navigating the paradox of increasing global disruptions alongside record-breaking operational efficiency. By highlighting a case study from Avantor, the presentation demonstrated how Decision Intelligence (DI) can move beyond theoretical AI to automate thousands of routine daily decisions, such as stock rebalancing and purchase order prioritization. The key takeaway from the ARC Advisory Group’s 30th Leadership Forum is that companies should focus on “change-ready” solutions that solve immediate, high-impact problems rather than waiting for perfect data or fully autonomous systems.
WiseTech Global, the developer of the CargoWise platform, has announced a major two-year restructuring plan that will involve cutting approximately 2,000 jobs, or 29% of its global workforce. This strategic pivot aims to integrate artificial intelligence deeper into both its internal operations and its customer-facing software, which currently handles a massive 75% of global customs transaction data. The layoffs are expected to hit the company’s U.S. cloud division, E2open, particularly hard, with some reports suggesting cuts of up to 50% there. This move comes at a turbulent time for the Australian tech giant, as it seeks to regain investor confidence following a 68% drop in share price since late 2024 amid leadership controversies and shifting market dynamics.
Burger King’s AI “Patty” Moves AI Into Frontline Execution
Published
2 jours ago
on
26 février 2026
By
Burger King is piloting an AI assistant called “Patty” inside employee headsets as part of its broader BK Assistant platform. This is not a marketing chatbot. It is an operational system embedded into restaurant execution.
Patty supports crew members with preparation guidance, monitors equipment status, and analyzes customer interactions for defined service language such as “please” and “thank you.” Managers can query performance metrics tied to service quality in real time.
The architecture matters more than the novelty.
AI Inside the Operational Core
Patty is integrated with a cloud based point of sale system. That connection allows:
near real time inventory updates across channels equipment downtime alerts synchronized digital menu adjustments structured service quality measurement
If a product goes out of stock or a machine fails, availability can be updated across kiosks, drive through boards, and digital systems within minutes.
This is AI operating inside the transaction layer, not sitting above it.
Earlier fast food AI experiments focused on automated drive through ordering. Burger King is more measured there. The more consequential shift is internal execution intelligence.
Efficiency, Visibility, and Risk
Across retail and logistics sectors, AI agents are being embedded directly into workflows to standardize performance and compress response times. The value comes from integration and coordination, not conversational capability.
At the same time, customer sentiment toward fully automated service remains mixed. Privacy, workforce implications, and over automation risk are active concerns. As AI begins monitoring tone and behavior, governance becomes part of the deployment decision.
Operational AI improves visibility. It also expands accountability.
Implications for Supply Chain and Operations Leaders
Three themes emerge:
Execution instrumentation – AI is now measuring soft metrics and converting them into structured operational data. Closed loop response – When connected to POS and inventory systems, AI can both detect issues and trigger corrective updates. Governance at scale – Embedding AI at the edge requires clear oversight, performance auditability, and workforce alignment.
Burger King plans to expand BK Assistant across U.S. restaurants by the end of 2026, with Patty currently piloting in several hundred locations.
This is not a fast food curiosity. It is a signal.
AI is moving from analytics to execution. From dashboards to headsets. From advisory tools to operational participants.
For supply chain leaders, the question is no longer whether AI will enter frontline operations. The question is how intentionally it will be architected and governed once it does.
AI and Enterprise Software: Is the “SaaSpocalypse” Narrative Overstated?
Published
2 jours ago
on
26 février 2026
By
Capital is rotating. Growth has given way to value, and within technology the divergence is increasingly pronounced. While broad indices have stabilized, many software names have not. Since late 2025, software equities have materially underperformed other parts of the technology complex. Forward revenue growth across many mid-cap SaaS firms has slowed from prior expansion levels, net retention rates have edged down in several categories, and valuation multiples have compressed accordingly. Markets are repricing both growth durability and margin structure.
The prevailing explanation is straightforward. Generative AI lowers barriers to entry, reduces the cost of building applications, and compresses differentiation. If application logic becomes easier to produce, competitive intensity increases and pricing power weakens. The result is visible not only in equity valuations, but in moderated expansion rates and tighter forward guidance. There is substance behind that concern. But reducing enterprise software economics to code production misses where the structural leverage in these platforms actually resides.
The Core Bear Case
The bearish thesis rests on three related propositions: AI commoditizes application logic, accelerates competitive entry, and pressures margins. If enterprises can generate software dynamically, recurring subscription models face structural pressure. If workflows can be automated through agents, reliance on fixed applications may decline. If code becomes less scarce, incumbents may struggle to defend premium multiples.
The repricing in software reflects these risks. Multiples have compressed meaningfully, and growth expectations have moderated across several verticals. In certain categories, retention softness suggests substitution pressure is already emerging. These signals should not be dismissed as temporary volatility.
At the same time, equating software value solely with feature output or code generation is a simplification. Enterprise software durability rarely rests on feature sets alone.
What Enterprise Software Actually Represents
In supply chain environments, systems function as operational coordination layers rather than isolated applications. Transportation management systems, warehouse platforms, planning suites, and multi-enterprise visibility networks sit at the center of integrated transaction flows. They embed years of configuration, exception handling logic, compliance mappings, and cross-functional workflows. Over time, they accumulate operational data that informs sourcing, forecasting, transportation optimization, and execution decisions across the enterprise.
Replacing those systems is not equivalent to generating new code. It requires rebuilding institutional memory, re-establishing integration points, and re-validating compliance controls across internal and external stakeholders. The switching cost is not interface retraining; it is operational re-architecture.
In our research on AI system design in supply chains
AI in the Supply Chain-sp
, the recurring conclusion is that structural advantage stems from coordination, persistent context, and integration density. Model capability matters. Economic durability flows from how systems connect and govern activity across distributed networks. That distinction is central to evaluating enterprise software in the current environment.
Where Risk Is Real
Not all software categories have equivalent structural protection. Risk is most evident in narrowly defined vertical tools, lightweight workflow utilities, and productivity-layer applications with limited proprietary data accumulation. In these segments, generative models can replicate core functionality with relatively low switching friction. Pricing pressure can intensify quickly, and margin compression may prove structural rather than cyclical.
By contrast, enterprise workflow orchestration platforms deeply embedded in core business processes create operational dependency. Replacing them requires redesigning process architecture, not simply substituting interfaces. Systems that accumulate years of transaction data, customization layers, and ecosystem integrations generate switching costs that extend beyond feature parity. Observability and monitoring platforms that collect continuous telemetry function as operational infrastructure; as AI agents proliferate, the need for measurement, traceability, and governance increases rather than declines.
In supply chain software specifically, planning platforms and transportation orchestration systems accumulate integration density over time. That density represents economic friction against displacement and reinforces durability when market volatility increases.
AI as Architectural Pressure
AI will alter software economics. It will increase development intensity, shorten product cycles, and compress margins in commoditized segments. Vendors operating at the surface layer of functionality will face sustained pressure.
However, AI simultaneously increases coordination complexity. As autonomous agents proliferate, enterprises require more governance controls, more integration layers, and more persistent contextual memory. The economic question shifts from “Who can build features fastest?” to “Who can coordinate distributed intelligence most reliably?”
Agent-to-agent communication, contextual memory frameworks, retrieval-based reasoning, and graph-aware modeling are becoming foundational design considerations in supply chain environments, as described in ARC’s white paper AI in the Supply Chain: Architecting the Future of Logistics. Vendors capable of governing these interactions at scale may strengthen their structural position. Vendors confined to interface-layer differentiation may see pricing pressure intensify. The outcome is not uniform decline; it is structural differentiation within the sector.
Valuation vs. Structural Impairment
Markets reprice sectors quickly when uncertainty rises. The current adjustment reflects legitimate concerns: slower growth trajectories, reduced retention durability, increased competitive intensity, and rising research and development requirements. These are measurable economic factors.
The open question is whether valuations reflect permanent impairment across enterprise software broadly, or whether the market is failing to distinguish between commoditized applications and structurally embedded coordination platforms.
Some observers argue that AI may ultimately expand the addressable market for enterprise systems rather than compress it. As AI adoption increases, enterprises may require additional orchestration frameworks, governance layers, and system-level controls. In that scenario, platforms with embedded workflows and distribution reach could see increased strategic relevance. The impact will vary materially by category and architectural depth.
In supply chain markets, complexity is not declining. Cross-border regulation is tightening, network volatility remains elevated, and multi-enterprise coordination is becoming more demanding. Economic value accrues to platforms that integrate and govern transactions, not to those that merely present information.
Implications for Enterprise Buyers
For supply chain leaders, the relevant issue is not short-term equity performance but architectural positioning. Does the platform function as a system of record embedded in transaction flows, or as a reporting layer adjacent to them? How deeply is it integrated into compliance processes, procurement logic, and transportation execution? Does it accumulate proprietary operational data that reinforces switching costs over time? Is it evolving toward coordinated AI architectures, or layering assistive tools onto a static foundation?
AI will not eliminate enterprise systems. It will expose those whose economic value rests primarily on surface functionality rather than integration depth.
A Measured Conclusion
The current narrative captures real pressure within segments of the software sector, but it does not fully account for structural differentiation. Certain categories face sustained pricing compression where differentiation is shallow and switching friction is low. Others may strengthen as AI increases coordination demands, governance requirements, and integration complexity.
The decisive factor will not be branding or feature velocity. It will be integration density, data gravity, and the ability to coordinate distributed intelligence across enterprise and partner networks. In supply chain contexts, platforms that govern transactions, maintain contextual continuity, and orchestrate multi-node operations retain structural advantage. Platforms that merely automate isolated tasks face a more uncertain economic trajectory.
That distinction, rather than headline narrative, will determine long-term outcomes.
A2A is only one component of a broader intelligent supply chain architecture. For a structured analysis of how A2A integrates with context-aware systems, retrieval frameworks, graph-based reasoning, and data harmonization requirements, download the full white paper:
The paper outlines the architectural model, governance considerations, and practical implementation path for enterprises building connected intelligence across their supply networks.
Download the white paper to explore the complete framework.
