Supply Chain Leaders Deliver the Promise of AI

Gartner recently gathered supply chain and operations executives from six industries for a Leaders in Action event focused on exploring the next frontier of AI in supply chain. An enormous “thank you” to Jammi Tu and Guan Wei, Lenovo’s group operations officer and head of global supply chain, for hosting our community at their U.S. headquarters in North Carolina.

Group discussions centered on various aspects of AI deployment — the latest use cases, key enablers and several organizational and talent implications. It was clear that even the most advanced players in the room are still working on what native AI organizations will look like in the future.

Lenovo, a $69 billion revenue global technology leader, is focused on rapid growth enabled by “Hybrid AI” that integrates three forms of intelligence: Public AI (broad intelligence from the cloud), Enterprise AI (secure shared insights within the business) and Personal AI (devices deeply knowing the user, while respecting privacy).

During our time on campus, the Lenovo team highlighted the many ways they leverage their own technology to progress the future of work in both office and frontline environments. For the latter, there is the Daystar GS, a six-legged robotic dog being used in both factory and field applications.

Internally, Lenovo considers itself "customer zero," and is using AI to manage complexity across its factories and to reduce time to decisions, more broadly. The team has deployed traditional AI and machine learning in planning and quality, as well as more advanced supply chain AI solutions that include:

• The iChain super-agent: A simulation solution that enables predictive insights for smarter supply chain management. It connects dots across functional streams to optimize supply and manage risk.
• An AI-enabled solution that guides end-to-end manufacturing operations, knowledge and performance management and continuous improvement.
• An ESG AI platform, that prescribes decarbonization actions across the value chain, including Scope 3.

Paul Gallagher, global CSCO at General Mills, shared a transformative journey supported by people, processes, data and technology/AI.

Paul’s team took a “go slow to go fast” approach, prioritizing foundational elements first. They delayed an ERP implementation to consolidate and clean their data, recognizing that a strong, self-healing data foundation is a non-negotiable prerequisite for AI success.

General Mills' primary objective is end-to-end decision automation through business process management. The organization shifted from being primarily relationship-driven to being highly process-driven, with a focus on measuring process adherence in addition to KPIs.

They are building a dynamic digital twin to automate high-volume, high-frequency decisions, with a goal of automating 50 million decisions per year. This success has enabled General Mills to achieve productivity gains, including significantly reduced product waste and lower transportation costs.

We had an active roundtable discussion during this event. The following topics generated the most energy and provocation across the group:

• Orchestration and Governance of Autonomous Agents: The community explored how to manage complexity when thousands of AI agents are working together. A key concern was preventing the creation of unmanageable technical debt from many small tools developed by decentralized teams. Leaders debated centralizing governance versus deputizing thousands of "citizen developers" to create local tools. Accountability for agent outcomes and the necessity of robust knowledge capture were also highlighted as critical control mechanisms.
• The Evolution of Organizational Structure and Roles: The shift in workforce structure due to automation was a central theme, characterized by the move away from a traditional "talent pyramid.” This implies automating lower-level work and increasing the number of higher-level roles focused on orchestration, integration and algorithmic thinking. This transformation presents a tricky and dynamic puzzle that introduces cultural challenges, including potential fear among employees concerned about job elimination.
• Defining and Proving AI ROI: Attendees noted that some executive teams are pushing a "gold rush" approach to AI adoption without a clearly defined ROI. Tech companies often receive a grace period for trials, but generally, there is mounting pressure to lock down promised benefits in the business plan. Even for companies with strong revenue and profit growth, AI is most often financed by a self-funding flywheel, where savings generated are reinvested into digital capabilities.

We’re grateful for the inspiring two days spent together in North Carolina and look forward to continuing these discussions with the COO/CSCO community at our future events.

Stan Aronow
VP Distinguished Advisor
Gartner Supply Chain
Stan.Aronow@gartner.com