At this year’s World Economic Forum, AI leaders were out to prove that their investments are paying off. Slogans plastered across the Davos promenade promised that companies like Cisco and IBM had found the formula for AI returns. Merantix declared 2026 the “year of AI ROI.”
They’re not wrong about the pressure. After three years of rapid adoption, the patience window is closing. Teams everywhere report efficiency gains, but those gains aren’t showing up in margins, throughput, or earnings. McKinsey found that while 88% of organizations use AI, only 39% can point to any EBIT impact.
The “invest and learn” phase is over. Financial markets will begin demanding payback, and the patience window will close. Most organizations have no mechanism to redeploy saved time into activities that meaningfully improve performance. Productivity gains are being absorbed into the system, not transformed into results.
Finance teams use AI to accelerate month-end close, then fill the recovered hours with more variance analysis. Marketing teams generate content faster, then produce more content. HR departments automate resume screening, then interview more candidates without improving quality of hire. The work expands to consume whatever time becomes available. Economists have a term for this — Jevons Paradox — where efficiency improvements lead to increased consumption rather than net savings.
Even in operations-heavy environments, AI has struggled to deliver financial returns that match the investment. Predictive maintenance systems generate better forecasts but don’t prevent breakdowns if the recommended actions never get executed. Demand planning tools improve forecast accuracy but can’t fix the inventory positioning or procurement decisions needed to capitalize on those insights. The intelligence exists. The execution mechanism doesn’t.
This sets the stage for a major shift in 2026. With AI budgets under scrutiny and CFOs demanding real ROI, the next wave of investment will shift away from “productivity AI” toward “operational AI” — systems that eliminate the hard-dollar friction baked into the physical economy. Supply chain is where this shift becomes real.
Unlike most corporate functions, supply chain operations carry recurring, measurable cash costs like detention, demurrage, rehandling, expedites, labor overruns, spoilage, chargebacks, and stockouts. These are line items that show up every month on the P&L. When AI reduces delays, prevents exceptions, or improves flow, the financial impact is direct and undeniable. Avoiding detention fees puts hundreds of thousands, sometimes millions, of dollars back in the budget. A prevented stockout means revenue that would have been lost. An optimized shipment consolidation reduces freight spend by thousands of dollars per week.
The redeployment mechanism here is fundamentally different. In supply chain, reducing the amount of time it takes to respond to a disruption translates directly into prevented costs because the operational context provides natural enforcement. When an AI system predicts a shipment will arrive late to an appointment and autonomously reschedules the delivery window, it doesn’t just save a planner 15 minutes. It prevents the carrier from incurring detention charges because the truck isn’t sitting idle at the dock. The rapid response enabled by AI-automated actions has a direct impact on the P&L.
Supply chain has three characteristics that make operational AI viable in ways that productivity AI wasn’t. First, the cost structure is transparent and repetitive. Every company knows what detention costs per incident, what expedited freight premiums look like, what spoilage rates run. Second, the operational leverage is high. A single prevented disruption can save more than the annual cost of the system that prevented it. Third, the feedback loops are tight. You know within days, if not hours, whether the intervention worked.
Consider a manufacturer using AI to manage inbound parts flow, with a system that prevents a $22,000-per-minute production line stoppage by triggering the expedite before the shortage happens. For companies holding hundreds of millions in inventory, preventing stockouts or reducing safety stock by even 5% releases working capital that dwarfs any potential headcount reduction. This isn’t a productivity gain that might eventually show up in the financials. It’s direct cost avoidance and capital efficiency that hits the bottom line immediately.
As LLMs continue to improve, the intelligence of the model or the elegance of the workflow will matter less. What matters is whether organizations can redeploy efficiency gains into activities that prevent real operational losses. This requires implementing AI into their functions that has the correct context, takes the right actions, and can produce operational results that can’t be replicated using a traditional workforce. Every prevented disruption has a dollar value. Every improved decision has a measurable outcome.
AI investment in 2026 will shift toward functions where value is both visible and verifiable. Supply chain moves from a support function to the proving ground for enterprise AI ROI. Any domain where AI can directly prevent recurring costs, not just save time, will attract capital and attention. But supply chain gets there first because the costs are higher, the frequency is greater, and the measurement infrastructure already exists.
Time saved has never been the goal. Money saved always was.
CFOs demanding ROI need metrics that capture what AI prevents, not just what it produces. Traditional KPIs weren’t designed for proactive intervention. Our executive guide introduces five next-generation metrics — including Revenue-at-Risk Mitigation and Mean Time to Recovery — with formulas you can implement today.
