September 2026
Supply chains are undergoing a profound transformation as artificial intelligence moves from promise to practical application. The challenge for today’s Chief Supply Chain Officers (CSCOs) is not simply adopting new technology, but making strategic choices that deliver real impact and lay the groundwork for a future-ready supply chain.
Recognizing the urgency and complexity of this shift, the newly launched Gartner CSCO Communities in Chicago, New York, and London recently convened for their inaugural Inner Circles. These private, peer-led events brought supply chain executives together for candid roundtable discussions, networking over dinner, and strategy validation – all centered on the timely topic of architecting AI-driven supply chains.
During these Inner Circles, CSCOs explored critical questions, including:
- How to fully integrate AI beyond isolated use cases
- What data-related obstacles must be overcome
- What leadership qualities will they need to embody to succeed in an AI-driven future
- How to deliver better ROI and forge new value streams across the supply chain ecosystem
Below, we share four key takeaways from the discussions at the Gartner New York CSCO Community and the Gartner Chicago CSCO Community Inner Circles:
Data Readiness: The Foundation and Bottleneck for AI
CSCOs across both communities emphasized that data remains both the foundation and the bottleneck for AI-driven supply chain transformation. They cited that persistent challenges around data quality, accessibility, integration, and governance continue to impede progress toward realizing AI’s full value. As one executive noted, “We’re focused on quality management and making sure [AI] has the right context. Hallucinations are due to bad data, incorrect context, irrelevant data, and, worst case, it’s making something up. One of our prompts even says ‘do not make up data.’”
Discussions consistently returned to the importance of data readiness. CSCOs stressed the need to clearly define business problems before assessing whether the right data exists to support AI solutions. Incomplete or poor-quality data can drive misguided decisions, highlighting the necessity for robust data governance and close collaboration between IT and business stakeholders. However, many CSCOs acknowledged an ongoing tension: while IT typically controls access and infrastructure, business functions own the use cases and data requirements. This dynamic remains a core challenge as they seek to balance speed, compliance, and impact.
AI Strategy: Overcoming Barriers to Execution and ROI
CSCOs in both the New York and Chicago communities acknowledged that their organizations are still shaping their AI strategies, with most still in the early stages of their AI journeys. While the excitement and sense of urgency around AI were undeniable, so too was healthy skepticism – especially regarding cost, ROI, and the challenge of scaling beyond isolated pilots.
For those seeing early wins, the impact is tangible: “We are low tech, and we built internally. Scenario planning used to take weeks, and now we’re turning it out immediately. We’re starting out small and going across functions.”
Execution challenges persist, particularly around breaking down silos and aligning teams. As one CSCO put it, “We struggle with an enterprise way of looking at things.” Another added, “What we're finding is as we build it out, people don’t know what prompts to use to get the right output.” This raised a critical question for supply chain leaders: “Are we truly freeing people up for more strategic work or just shifting tasks?”
Cost justification remains a central concern, as many CSCOs discussed mounting pressure to demonstrate clear, measurable savings. One executive observed, “In some cases, the cost to run and deploy AI is higher than the benefits companies are getting back.” Another cautioned, “AI-savvy people will be expensive in the future, as well. So, this likely won't be a cost saver anytime soon.”
As a result, many organizations are prioritizing operational efficiencies and incremental wins over large-scale, unproven investments.
Sometimes brakes make the car go faster. That’s the playbook we're going after.
Build vs. Buy: Making Strategic AI Investment Decisions
Another recurring theme across both Inner Circle discussions was the ongoing debate between building internal AI capabilities or leveraging external vendors. While cost-effective, off-the-shelf tools can offer a rapid entry point, many CSCOs are weighing these options against long-term strategic fit, scalability, and the ability to tailor solutions to their unique supply chain needs. Several leaders described mixed approaches, combining external partnerships for speed with internal development for control and customization.
This debate is closely intertwined with the evolving role of IT. Some CSCOs view IT as indispensable for scaling and governance, ensuring robust data management and compliance. Others, however, see IT as slowing down innovation and deployment. As one executive reflected, “We figured out quickly it didn’t work well across our organization, and it required us to find a vendor to give us the answers and help us.”
Ultimately, the consensus is that there is no one-size-fits-all answer. The right balance between building and buying, and the optimal role for IT, will depend on each organization’s strategic priorities, risk tolerance, and maturity on the AI journey.
Leading Change Management: Workforce and Organizational Transformation
CSCOs agreed that AI is fundamentally reshaping supply chain organizations – transforming roles, skill requirements, and even the culture of teams. As one executive noted, “AI will displace activity, but [we’re] training people to problem solve and think with AI.”
Adoption remains uneven across organizations. Some employees are eager to embrace new tools and ways of working, while others are more hesitant, underscoring the need for targeted change management. One CSCO observed, “Less tenured colleagues will challenge their ways of working. Longer tenured people will look at it differently and produce different results. What we found with the greatest traction is starting with the problem we are trying to solve.”
This shift is also evident in their hiring and talent development strategies. Many CSCOs emphasized a growing demand for data scientists: “All we are hiring is data scientists. That’s the integrity of AI. That's all I want to hire. We have repetitive data and need them to clean things up.” Others are prioritizing “AI curiosity” as a key hiring attribute: “All hiring should include being AI curious. The processes and mindset are what we're looking to hire.”
Supply chain executives are also focused on cultivating adoption among those who can bridge business and technology. “We call them ‘AI heroes’ internally as a way to elevate them,” shared one CSCO. They also discussed empowering talent by funding super users, rotating employees into new roles, and encouraging creative control. As one CSCO cautioned, “If you just keep people at analyst-level, you'll lose them.”
As supply chain leaders navigate the evolving AI landscape, they are focused on striking the right balance between strategy, technology, and talent. By learning from peers and sharing real-world experiences, CSCOs can accelerate their progress from AI hype to high-impact results.
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