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Across the supply chain, technology is becoming less of a back-office function and more of a core operating tool. Artificial intelligence (AI) is helping companies plan better transportation routes, software is generating more accurate forecasts, and robots are picking, packing and moving orders through warehouses.
These shifts are happening at a time when supply chain managers are being asked to do more with less room for error. Companies are still managing labor constraints, rising costs, changing customer expectations and more frequent disruption. The question now is, how much of the planning, sensing, decision-making and execution can be handled by systems that learn, adapt and act quickly and efficiently?
Gartner’s new Top Trends in Supply Chain Technology for 2026 report answers those questions, plus a few more. The research firm groups the trends into three different buckets: autonomy and agency, specialization and intelligence, and trust and governance. It says the themes reflect “a shift toward intelligent, self-directed and accountable systems that operate seamlessly across digital and physical environments.”
AI Takes the Lead
Naturally, agentic AI and physical AI (systems that perceive, reason and act within the real world, versus just processing information) sit at the top of Gartner’s list this year. It says advances in AI technologies help CSCOs drive business value, strengthen resilience and reimagine operating models.
“This year’s trends highlight the growing role of AI as the foundation for more autonomous, intelligent and adaptive supply chains,” says Christian Titze, VP analyst, in a press release. “As organizations move toward hyperconnected, AI-driven environments, leaders must focus not only on deploying advanced technologies, but also on ensuring they work together to deliver measurable value and long-term resilience.”
Here are the eight tech trends that Gartner says organizations should be paying attention to this year:
- Polyfunctional robots. Advances in AI, machine learning and robotics engineering are enabling robots to take on multiple tasks beyond their original design. Gartner says these flexible systems offer a new workforce model, particularly in environments facing labor shortages.
- Physical AI. Bringing AI into physical operations, this technology combines AI models with IoT sensors, robotics and automation systems to enable real-time sensing, analysis and execution across supply chain environments.
- Agentic AI. Gartner says a class of AI systems is emerging that introduces a virtual workforce of agents that move beyond insights to execution, and are capable of planning, acting and adapting to achieve goals in complex environments.
- Collaborative multi-agent systems (MAS). Extending the capabilities of individual AI agents, these systems enable multiple agents to work together across workflows and environments, each specializing in a specific task or domain.
- Intelligent simulation. Enhancing traditional modeling approaches, intelligent simulation integrates AI, machine learning and advanced analytics into simulation models to improve predictive capabilities and decision-making.
- Domain-specific language models. Designed for targeted business needs, these models are trained or fine-tuned for specialized supply chain use cases, delivering greater accuracy, reliability and compliance than general-purpose AI models.
- Product provenance. Gartner says growing demand for transparency and regulatory compliance is driving the need to trace and verify the origin and journey of products across the supply chain.
- Decision governance. Finally, as AI adoption scales, organizations are implementing frameworks and guardrails to govern AI-enabled decision-making, ensuring transparency, accountability and compliance.
Action Steps to Take Now
In the full report, Gartner also outlines actions organizations can take for each trend. Specific to physical AI, it says companies should start with use cases that can deliver measurable value, such as logistics planning, predictive maintenance and safety monitoring. It also recommends employing cross-functional teams, simulations, digital twins, stronger oversight and IoT expertise to manage implementation, integration and risk.
On the decision governance topic, Gartner recommends phasing in governed decision-making, updating existing processes and adding human oversight as AI takes on more complex supply chain decisions. “Overall,” it adds, “the primary implication of decision governance is to enable AI-powered decision making at the speed of trust.”
