Supply chain risk management is taking a larger share of corporate attention and spend as companies reset budgets and rethink network design. New KPMG research points to heavier investment, tight labor markets, and a faster turn toward automation and AI to protect growth.
Risk And Resilience Move To The Forefront
Risk mitigation sits at the top of supply chain transformation plans. In KPMG’s U.S. Supply Chain Survey of 462 senior executives, just over half identified managing and mitigating risks as their most important transformation objective, and nearly four in ten named it the leading investment priority in the near term. That emphasis aligns with a marked increase in spend: most respondents now direct roughly 11 to 15 percent of revenue to supply chain activity, compared with a 5 to 10 percent range reported for 2024.
This step-up in funding reflects a broader re-rating of network importance on earnings and cash flow. Organizations are directing capital toward resilience, agility, and governance rather than treating supply spend purely as overhead to compress. The finding that 73 percent of executives intend to transform their supply chain operating model within one to three years highlights the depth of the reset. Under continuous disruption, many are reassessing how sourcing, manufacturing, logistics, and inventory management connect, with risk metrics built into design choices rather than added post hoc.
The survey also pinpoints where money slips away. Logistics and transportation costs ranked as the most significant source of value leakage for 38 percent of respondents, ahead of other categories. That aligns with recent trade data showing that rate volatility, capacity swings, and service inconsistency can erode margin and stretch working capital if networks lack flexible capacity agreements and integrated cost visibility. As budgets increase, attention is shifting toward tools that link freight, supplier, and external risk signals to inventory, routing, and capacity decisions with minimal delay.
Digital capabilities sit at the center of that agenda. Half of the organizations surveyed are planning investments in automation, digitalization, and AI to reinforce supply chain performance. This wave goes beyond isolated tools. Control towers, simulation capabilities, and predictive analytics are being used to interpret weather alerts, geopolitical changes, and supplier distress indicators and route them into planning and procurement workflows. The aspiration is not simply more data on risk but faster conversion of that data into practical decisions.
Talent Shortages Reshape The Automation Agenda
While capital allocations increase, talent capacity is under pressure. KPMG found that 77 percent of executives see a significant talent shortage across procurement and supply chain roles. Respondents tied that gap to specific weak spots: 38 percent reported that it undermines supply chain visibility, while 36 percent pointed to demand planning performance as another casualty.
These constraints strike at the heart of digital ambition. AI-driven forecasts and automated workflows still require people who can judge model output, set guardrails, and manage exceptions that affect customers and financial results. When those skills are thin on the ground, even strong technology road maps stall. Many companies in the survey cited familiar barriers to implementation, including legacy system compatibility, data security concerns, and cultural resistance, all of which are harder to overcome without practitioners who understand both process and data.
The responses outline a dual response. Roughly 30 percent of organizations plan to upgrade or replace enterprise resource planning platforms to improve integration across planning, sourcing, manufacturing, and logistics. Around 28 percent are stepping up training and development for existing employees, and a similar share is prioritizing better data management capabilities. These moves aim to tighten data foundations, build confidence in analytics, and free teams from manual reconciliation so they can focus on higher-order coordination.
Industry reports reinforce the logic. Many AI programs deliver limited impact because models run on fragmented data and operate in isolation from core transaction systems. By linking ERP renewal, data quality work, and targeted skill building, companies are trying to create conditions where automation and AI can scale and support cross functional decision-making instead of adding yet another tool to an already cluttered landscape.
The Coming Test: Turning Spend Into Faster Decisions
The next battleground sits between rising investment and real-world decision speed. As networks add instrumentation and budgets tilt toward risk and resilience, performance will depend on how effectively organizations fuse supply, finance, and commercial inputs into shared playbooks. The KPMG numbers suggest that many have accepted the cost of inaction; the harder task is ensuring that new systems, data, and skills translate into quicker, cleaner choices on inventory, capacity, and supplier exposure when the next disruption hits.
