Artificial intelligence is taking on a larger role in how companies plan inventory, route shipments, and maintain assets, even as volatile trade and cost conditions tighten scrutiny on capital allocation. A new 2026 MHI Annual Industry Report finds adoption accelerating alongside investment, with companies tying AI deployments directly to cost control, resilience, and decision speed.
AI Shifts From Concept To Operating Fabric
The latest MHI report, produced with Deloitte and based on responses from 500 supply chain professionals, places AI at the center of near-term operating design. Seventy percent of respondents see AI as a technology with the potential to disrupt how networks are planned, run, and governed, a level of confidence that now rivals the enthusiasm once reserved for automation and robotics.
Adoption is catching up with intent. Forty-one percent of surveyed organizations report current use of AI in supply chain activities, up from 30 percent a year earlier. That ten-point gain in twelve months points to projects that are moving out of labs and into day-to-day workflows, especially where business cases rely on hard metrics rather than abstract productivity claims.
Reported applications concentrate where decisions depend on large, noisy data sets. AI is being deployed to refine demand and inventory decisions, where incremental gains in forecast quality can unlock working capital and protect service levels. It is shaping predictive maintenance programs that anticipate equipment failures before they reduce throughput. Teams are also using AI to support operational decision making and optimize transportation and logistics routes in the face of constantly changing capacity, constraints, and costs.
MHI chief executive John Paxton described a rapid evolution in how the field talks about these capabilities. Two years ago, many discussions focused on defining AI itself. Last year, attention shifted to generative models that create content, code, and plans. This year, the conversation has advanced to agentic AI, where software agents execute tasks, orchestrate workflows, and remove manual steps from operations.
That evolution has direct consequences for operating models. Agentic AI moves systems from recommendation engines toward actors that initiate changes, such as shifting inventory between locations or adjusting production schedules within predefined limits. For organizations facing chronic labor constraints and rising complexity, this creates a path to scale decision throughput without matching it with additional headcount. It also raises requirements for governance, process clarity, and performance monitoring so that agents operate inside clear boundaries.
The report also shows that AI is part of a broader technology and automation agenda. Fifty-six percent of respondents expect to increase spending on supply chain technology and automation this year. More than half plan to allocate over $1 million, and 17 percent expect budgets above $10 million, even as the emergency pace of pandemic-era automation has eased.
Recent benchmark studies from industry associations point in the same direction: investment is gravitating toward integrated platforms that connect planning, execution, and risk management. In that architecture, AI functions as a coordinating layer across existing systems rather than as a standalone tool, which intensifies the need for cleaner data structures, clearer ownership, and cross-functional integration.
Capital, Risk, and AI Adoption Converge
The same MHI report situates these technology moves within an environment of elevated external pressure. Respondents rank economic and geopolitical instability as the most significant trend shaping supply chain decisions this year. Trade disputes, sanctions, inflation, and intermittent border constraints are keeping volatility high across both demand signals and physical flows.
The report notes that such conditions tend to push organizations toward more cautious investment and renewed scrutiny of supplier structures. Many are reassessing sourcing footprints, concentration risks, and exposure to currency and regulatory shifts. They also expect higher costs across raw materials, energy, services, and labor, which narrows tolerance for misaligned inventory, excess capacity, or inefficient transport.
In this context, AI is being positioned as a way to tighten control over volatility rather than a discretionary technology play. Demand and inventory models that learn from short-cycle data can reduce blunt safety-stock buffers while preserving resilience in priority segments. Routing and load-building tools that weigh fuel, carrier availability, and service requirements can steady transport cost per unit moved. Predictive maintenance models can protect critical assets without defaulting to conservative, time-based maintenance policies.
Industry analyses show that organizations combining AI with digital twins and control towers are starting to connect disruption signals directly to financial outcomes. Teams can model the effect of a port closure, energy price jump, or supplier outage on revenue, margin, and working capital before events fully work through the network. The MHI findings indicate that this kind of capability is now embedded in active roadmaps, even if full maturity remains uneven.
The report also hints at practical constraints. Many organizations still operate with fragmented data, overlapping systems, and gaps in advanced analytics capability. Agentic AI, in particular, rests on disciplined process mapping and clear rules of engagement. Without that groundwork, automated agents may generate additional noise or lock in suboptimal responses rather than elevating performance.
At the same time, regulatory expectations around AI governance, data protection, and ESG reporting continue to rise, especially in Europe. Trade bodies and policy makers are pressing for greater transparency into how models are trained, how decisions are audited, and how supply chain data is shared with partners. These pressures add a further dimension to AI planning, as teams balance speed of deployment with demonstrable control and accountability.
Where AI Rewiring Changes Operating Discipline Next
The MHI report describes a moment where AI strategy is starting to shape core disciplines such as network design, capital planning, and risk governance. As AI tools become embedded in orchestration and decision flows, the dividing line will sit less in access to technology and more in the quality of operating assumptions, data standards, and governance frameworks that determine how those tools behave under stress.
