3 strategies to turn supply chain uncertainty into advantage in 2026

Heading into 2026, company growth ambitions are rising, but volatility hasn’t loosened its grip. Networks tuned for efficiency are cracking under pressure, and cost cuts often erode the capabilities needed for resilience.

Tariff volatility, economic swings, and shifting customer expectations are the norm. Adaptability means sensing change early, responding quickly, and shifting resources where they’ll have the greatest impact. The next wave of supply chain leadership will come from organizations that make adaptability their default.

This can be achieved by prioritizing three strategies: Embedding AI into workflows, engineering profit through cost discipline, and stress-testing networks before the next disruption hits. In the year ahead, adaptability isn’t optional. It’s the price of admission. Let’s take a look at the three strategies CSCOs need to focus on.

Start to make AI the backbone of supply chain workflows

Gartner research shows that 65% of CEOs in supply chain-centric enterprises expect AI to shape the next era of business, but most admit their current models aren’t ready. The real value of AI isn’t just in automation or analytics; it’s in how it enables organizations to adapt faster and smarter. When AI is embedded across workflows, supply chains can sense market shifts, adjust inventory, and reroute logistics in real time.

Too often, AI pilots generate excitement, but stall before delivering real impact. Without consideration of how new AI capabilities will impact workflows, limited enterprise-grade governance, and poor data quality will undermine trust and adaptability. By contrast, companies that centralize AI governance and standardize platforms with embedded machine learning operations see tangible results.

For example, one global retailer cut inventory days-on-hand by nearly a quarter simply by feeding machine-learning forecasts directly into replenishment workflows. This freed up working capital to fund a new same-day delivery service that won market share.

For AI to fuel broader transformation, it must live in the core applications that power inventory planning, order orchestration, transportation routing, and dynamic pricing. Executives should hold model performance to the same rigor as financial results, tracking not only accuracy but cost savings, service levels, and fulfilled order velocity.

Engineer profit through cost discipline

In recent years, many CEOs and CFOs have put in place challenging cost reduction mandates for supply chain organizations leading to headcount reductions, the suspension of discretionary projects, and negotiating every dollar of savings from carriers. Those moves protected margins, but at the cost of tomorrow’s differentiation. In 2026, cutting costs within the supply chain alone won’t be enough.

CSCOs need to engineer profit by managing costs across the business, not just inside their own walls. This starts with reframing cost management as an enterprise-wide mandate. CSCOs must clearly articulate to the C-suite how today’s volatile environment is reshaping cost and service structures, and why material change is necessary.

From there, the goal is to enable teams to own cost trade-offs in their decision-making, embedding cost visibility into processes so choices reflect both financial and operational realities. Finally, systemic changes must make good cost decisions the default, hardwiring discipline into governance and decision infrastructure.

Leading organizations are already moving in this direction. They treat cost management as a source of strategic funding. One manufacturer replaced its annual cost-cutting checklist with zero-based budgeting for its warehousing and fulfillment centers. Instead of trimming a percentage here or there, cross-functional teams ran rapid-fire “opportunity sprints,” mapping total cost-to-serve by customer segment and testing quick wins.

The lesson is clear: cost discipline isn’t about cuts. It’s about creating capacity for growth and engineering profit.

Build resilience before the next shock arrives

Today’s supply networks are efficiency machines, optimized to squeeze out every basis point. But lean designs offer little buffer when natural disasters, geopolitical clashes or sudden tariff changes disrupt flows. Gartner research finds that organizations lacking scenario-based stress testing may endure recovery times up to 30% longer after a major shock.

Leading companies are turning to digital twins and quarterly “resilience rehearsals” to close that gap. One electronics retailer built a live digital replica of its end-to-end network, ingesting real-time data on inventory, lead times, carrier rates, and macroeconomic indicators. When tariff changes hit key components, the organization ran alternative routing models in minutes, rerouted freight mid-transit, and preserved a consistent on-time delivery rate.

The most effective exercises don’t rely on static spreadsheets or hypothetical scenarios. They simulate high-impact events: economic slowdowns, raw material shortages, exchange-rate swings, on top of actual operating data, then codify response playbooks. Incentives across procurement, planning, and commercial teams must reward not only cost efficiency but also buffer capacity, supplier diversification, and rapid pivot capability.

From uncertainty to advantage

The next era of supply chain leadership won’t be defined by who reacts fastest, but by who builds adaptability into their DNA. Volatility isn’t going away, but neither is opportunity. Companies that hardwire these strategies into their organizations won’t just survive, they’ll set the pace for the industry.

The question isn’t whether disruption will happen. It’s whether you’ll be ready to turn it into your advantage.

About the author

Thomas O’Connor, is the chief of research for Gartner's supply chain business and technology insights practice. He is responsible for leading a global team that supports chief supply chain officers and leaders in supply chain, planning, sourcing & procurement, manufacturing, logistics, quality, sustainability and R&D.

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