Supply chain disruption readiness remains uneven heading into 2026, according to new findings from the 2026 State of Supply Chain Report. The study links confidence directly to visibility, data quality, and system maturity, while cost pressure and slow AI adoption continue to limit structural progress.
Visibility and System Maturity Define Disruption Readiness
The 2026 State of Supply Chain Report from AI-solutions provider Sage draws on a January 2026 survey of more than 200 operators across retail and wholesale, covering organizations with annual revenues from under $1 million to more than $500 million. Respondents described a year shaped by shifting tariff regimes, volatile input costs, transportation delays, and geopolitical tension that repeatedly stressed networks.
Only about half of consumer-focused brands reported strong confidence in their ability to handle disruption this year. The data links that confidence to the depth of planning and execution infrastructure rather than to basic awareness of risk. Teams that have integrated operational, financial, and supplier data into coherent views tend to move from signal to decision faster when tariffs change, lanes close, or a key supplier stumbles.
By contrast, operators that still rely on fragmented tools, spreadsheets, or lagging reports detect problems later and escalate more slowly. These environments turn manageable events into extended service issues, drive overtime and expedited freight, and erode margin. Recent resilience benchmarks from industry groups point to similar patterns: networks with end-to-end visibility maintain higher fill rates and steadier working capital through shocks.
Cost pressure runs through the findings. Even groups described as highly optimized continue to prioritize unit cost and near-term savings over broader modernization of planning, orchestration, and risk sensing. This stance keeps immediate expenses in check but delays investment in integrated risk platforms, digital twins, and scenario planning capabilities that convert disturbance into controlled adjustments rather than emergency reactions.
Nearshoring Drivers and Early AI Deployment
The report highlights a notable shift in sourcing posture. Nearly half of surveyed operators plan to relocate some supply closer to demand regions over the coming period. Quality performance and compliance oversight rank as primary reasons for this move, ahead of pure cost considerations, indicating a push to improve execution discipline and traceability alongside network redesign.
This emphasis reflects broader trade and regulatory trends. Regional standards on product safety, ESG reporting, and data handling have tightened, while customers expect predictable service and clearer proof of origin. Moving production or assembly nearer to end markets concentrates attention on supplier process capability, audit readiness, and data sharing, since shorter lead times do not remove the need for structured oversight.
AI adoption in supply chain workflows remains limited in scope. Only 10 percent of brands in the survey have AI actively embedded in operational processes such as planning, sourcing, or logistics management. Effective deployment appears closely tied to data readiness and visibility maturity: organizations with standardized data models, cleaner transaction history, and established control environments are more likely to have AI in live use.
Industry research across manufacturing and consumer sectors shows similar patterns. Predictive and generative AI gain traction first in demand forecasting, inventory optimization, and exception management where there is adequate historical data and clear decision rules. The Sage findings reinforce that AI tools amplify existing operating models; they accelerate decisions where processes are defined and information is reliable, but add little value where data remains siloed or inconsistent.
Cost discipline again shapes adoption choices. Many teams pursue AI for narrow, quick-return use cases such as automating routine reporting or supporting tactical planning, rather than for more extensive redesign of cross-functional orchestration. This approach yields local efficiencies but leaves larger structural benefits, such as unified disruption response or continuous scenario testing, largely unrealized.
A Deeper Benchmark: Converting Awareness Into Operating Practice
The report points to a practical benchmark that goes beyond classic resilience metrics: the speed and consistency with which organizations turn known risks into codified operating practice. External shocks will continue, but the gap between similar companies increasingly reflects differences in visibility architecture, supplier governance, and the discipline with which AI-ready data foundations are built. Readers who treat readiness as a measurable capability set rather than a general aspiration can use this survey as a prompt to re-examine how far their own networks have progressed from risk recognition to repeatable, system-supported response.
