AI adoption in supply chain is shifting from experimentation to embedded capability, with momentum strongest in analytics and planning. Eighty-three percent of respondents are piloting or implementing AI in supply chain intelligence/analytics and 79% in data visualization. Importantly, these establish the data backbone for more advanced use cases. Planning adoption is also strong, with 74% of enterprises reporting AI capabilities have been implemented or piloted in S&OP/IBP and 72% in advanced planning and scheduling. In practice, that means shorter data analysis cycle time for planning activities and enhanced service cost trade-off decision-making. Advanced inventory optimization has the deepest pipeline of any use case, with only 15% of organizations having implemented AI capabilities but 68% piloting. The clear pattern is that AI scales fastest where it can be delivered through existing platforms and governed, rich datasets.
The Hackett Group, Inc. recently released findings from its 2026 Supply Chain Key Issues Study, showing that supply chain leaders are accelerating artificial intelligence (AI) adoption as persistent cost pressure converges with the need to modernize. Momentum to deploy AI is concentrated in supply chain data visualization tools, intelligence and analytics, and advanced supply chain planning, where AI has become essential to faster, more informed decision-making and improved service performance in today’s volatile marketplace.
“Supply chain leaders understand where they need to go next – toward more autonomous, AI-enabled workflows that improve planning, responsiveness and execution across the network”
Cost-efficiency remains the top priority for supply chain leaders for the third consecutive year, reflecting ongoing geopolitical uncertainty, tariff exposure and inflationary pressures. At the same time, digital transformation has surged to the No. 2 priority from fifth place in 2025, underscoring the growing recognition that competitive advantage in the years ahead will be shaped not just by advanced technologies and AI, but also by the ability to deploy agentic workflows that sense, decide and act across the enterprise.
“Cost has been the top supply chain priority three years running – and that pressure is not easing,” said Kate Reilly, director, Strategy and Operations at The Hackett Group. “What has changed is how leaders intend to respond. More organizations now recognize that cost improvement cannot be sustained without modernizing the platforms, data and processes that enable the supply chain.”
AI adoption is advancing most rapidly in analytics-intensive, data-rich areas of the supply chain. The study found that 83% of organizations have deployed or are piloting AI in supply chain intelligence and analytics, with 79% reporting data visualization capabilities. Supply chain planning is also a leading area of adoption, with 74% of enterprises reporting AI capabilities in sales and operations planning (S&OP) or integrated business planning (IBP), and 72% in advanced planning and scheduling. This shift is enabling speed to decisioning, as well as the ability to more precisely analyze cost, service and performance.
To support these gains, organizations are accelerating transformation across the supply chain. Network design optimization (67%) and increased transactional automation (66%) lead initiatives reported for 2026, followed by inventory optimization (59%) and core platform upgrades (57%). Together, these initiatives are reshaping supply chain operations to deliver greater efficiency and resilience at scale.
However, scaling AI remains dependent on addressing foundational challenges. Data quality (50%) and data integration (47%) continue to be the most significant concerns reported by supply chain leaders, along with data privacy and regulatory concerns (46%). At the same time, 45% of leaders cite a lack of AI talent as a major constraint, highlighting the need for targeted investment in talent and data readiness. Looking ahead, supply chain organizations are moving toward integrated, AI-enabled operating models that embed intelligence across supply chain activities.
“Supply chain leaders understand where they need to go next – toward more autonomous, AI-enabled workflows that improve planning, responsiveness and execution across the network,” said Erin Blair, principal, Strategy and Operations at The Hackett Group®. “What remains challenging is how to scale from individual use cases to a cohesive operating model that can support it. That gap between ambition and execution is becoming a competitive risk.”
As organizations balance immediate operational demands with longer-term transformation priorities, those that scale AI and modernize core supply chain capabilities will be best positioned to outperform on cost, resilience and service.
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