A growing share of global supply chain teams now view disruption as a structural feature rather than an episodic shock. That shift is changing how organizations invest, organize, and deploy AI, particularly as digital systems increasingly influence planning, forecasting, and labor models. Accenture’s latest Pulse of Change research captures this pivot toward autonomy, data foundations, and workforce redesign.
Resilience Reoriented Around Data and Autonomy
Accenture reports that 76% of executives expect 2026 to bring the same or higher levels of disruption, reinforcing a trend seen across manufacturing and logistics firms that have expanded scenario modeling, multi-sourcing, and real-time planning tools over the last year. While leaders feel strongest about navigating technology- and talent-related shifts, readiness drops sharply for economic, geopolitical, and environmental risks, areas where volatility has intensified, according to recent trade and commodity market data.
One-third of respondents now place resilience at the center of their strategic agenda. Much of that activity is rooted in digital infrastructure: nearly seven in 10 are expanding AI and connected data platforms; close to six in 10 are reallocating resources to match shifting demand patterns; and more than half are sharpening forecasting and risk analytics. These moves align with broader industry adoption of autonomous planning systems, which have gained traction as companies seek faster cycle times and smaller planning teams.
AI Spend Rises, Integration and Data Quality Lag
AI budgets are set to rise again in 2026, with 85% of supply chain executives planning additional investment and more than one in five expecting increases above 20%. Companies are simultaneously experimenting with new forms of automation, 30% testing AI agents, 24% deploying them, and 21% building them into enterprise architectures. These patterns mirror what logistics providers, consumer goods manufacturers, and retailers have publicly reported: AI agents are emerging as workflow participants, handling exception alerts, data ingestion, and multi-variable scenario recommendations.
Yet the same momentum highlights persistent structural barriers. Executives point to poor data quality and weak links between AI initiatives and broader business strategy as core constraints. These challenges echo findings across multiple industry reports showing that fragmented ERP, WMS, and supply network data remains the biggest inhibitor to scaling AI in operations.
Talent remains another tension point. While both the C-suite and supply chain leaders overwhelmingly view AI as a driver of revenue growth rather than pure cost reduction, 16% of operations executives still struggle to source or develop the talent required to operationalize AI, slightly higher than C-suite sentiment. Many are responding by redesigning operating models to embed AI more deeply (56%), upskilling teams (53%), and strengthening responsible-AI practices and leadership literacy. According to trade reports, these organizational adjustments have accelerated as companies shift from isolated pilots to AI-supported control towers, automated root-cause detection, and digital twins for forecasting and replenishment.
Where Operational Momentum Is Quietly Shifting
One development worth watching is how AI is beginning to change the tempo of cross-functional decision-making. Companies that have adopted autonomous planning modules, real-time forecasting, or AI agents, such as those publicly described by large retailers, consumer goods manufacturers, and global logistics providers, consistently report a reduction in lag between sensing a shift and acting on it. That compression is more than a technical upgrade; it alters how organizations coordinate inventory, purchasing, and network design. As more firms modernize their data foundations, the advantage may accrue to those able to convert faster decision cycles into steadier execution, particularly in environments where volatility has become routine.
