CSCOs: Lead the transformation to build an AI-ready supply chain that delivers resilience, agility and performance.
CSCOs: Lead the transformation to build an AI-ready supply chain that delivers resilience, agility and performance.
By Julia Heyman | March 26, 2026
Supply chain leaders recognize AI as essential to future competitiveness, but most supply chain organizations lack a clear foundation. CSCOs must drive change by prioritizing data readiness, talent development and governance. Building a supply chain AI foundation is not about deploying tools, it’s about shaping the operating model, upskilling teams and governing data for scale and trust.
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Supply chain complexity, talent shortages and volatility demand new approaches. AI is not plug-and-play; it requires intentional investment in master data management, workforce strategy and ethical governance. To drive higher adoption, better outcomes and sustainable advantage, CSCOs must embed supply chain AI into business strategy.
The Gartner perspective? To establish a supply chain AI foundation, CSCOs should focus on four priorities: Expand their AI expertise, learn from industry leaders, assess organizational AI readiness and advance their role in enterprise AI strategy.
Grow CSCO knowledge base of AI basics. Invest time in learning core AI concepts, terminology and use cases. Build foundational understanding to lead confidently and make informed decisions.
Understand how others are using AI today. Study peer organizations and industry leaders to see how supply chain AI is being applied. Identify proven approaches, lessons learned and potential pitfalls.
Prerequisites: Prepare the organization for AI. Assess readiness by addressing gaps in data, talent and governance. Establish frameworks, clarify roles and ensure your supply chain is equipped for AI adoption.
Leaders who excel at supply chain AI:
Drive higher performance and reliability through faster response times, accurate diagnostics and reduced downtime.
Enhance customer satisfaction and workforce agility with improved order fulfillment, streamlined processes and reduced training needs.
Achieve substantial cost savings and operational efficiency across the supply chain.*
*Gartner clients have exclusive access to use cases demonstrating these outcomes with CSCO Playbook: Building Supply Chain’s Foundation for AI.
Assess your AI maturity using frameworks like Gartner’s AI Maturity Model.
Secure master data ownership and treat data as a P&L-level asset.
Implement hybrid governance, centralized policy-setting with decentralized management.
Upskill leaders and teams on AI foundations, value, engineering and governance.
Build multidisciplinary teams, centers of excellence and communities of practice.
Institutionalize ethical governance and measure adoption and outcomes.
By virtue of its rich data assets and highly repeatable processes, the supply chain is inevitably moving toward an AI-driven future. Leading CSCOs will pursue a two-pronged approach by delivering AI-enabled value today through narrow, practical use cases while also building the blueprints that establish the foundations for tomorrow’s AI-driven supply chain. This is critical to delivering on the mission-critical priority of architecting the AI-driven supply chain.
The steps in that journey include:
Becoming part of the enterprise‑level AI conversation by overcoming peers’ underestimation of supply chain’s strategic role through increased AI literacy, stronger cross‑functional competencies and partnerships that drive joint‑win deployments.
Building a supply chain AI roadmap that balances long‑term transformation and near‑term ROI by assessing AI maturity, identifying and prioritizing use cases, anticipating risks and aligning investments to measurable outcomes.
Building an AI‑ready workforce by preparing leaders and frontline teams for the uneven impact of AI across functions, redesigning workflows, roles and organizational structures, and linking upskilling, hiring and partnering to supply chain business needs.
Developing an AI‑enabled trading partner ecosystem through shared data frameworks, integrated workflows and transparent governance that supports trust, collaboration and joint value creation across the end‑to‑end network.
Enabling AI‑driven workflow and process change by understanding how AI transforms business processes, decision making and operating models through decision augmentation and automation.
Establishing a robust data foundation for AI by applying AI‑powered tools to improve data quality, adopting modern architectures such as lakehouses and data fabrics, and using natural language interfaces to accelerate insight generation and decision support.
Reconciling the desired AI future state with the existing supply chain technology stack by defining integration strategies, evaluating vendor capabilities and making informed build‑versus‑buy decisions across plan, source, make and deliver.
Start by assessing AI maturity, preparing data, upskilling talent and aligning supply chain AI strategy with enterprise goals.
Secure master data ownership, implement hybrid governance, focus on data quality and accessibility and ensure compliance with ethical and regulatory standards.
Incentivize employees to grow with AI, preserve talent, foster human-AI symbiosis and build multidisciplinary teams for governance and execution.
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