Why AI readiness isn’t enough for CSCOs

With AI now entering routine supply chain work, chief supply chain officers (CSCOs) are racing to make their organizations “AI ready.” Yet readiness alone is too limited an ambition.

If CSCOs focus only on adoption, they risk missing the bigger shift already underway: roles are changing, team structures are changing, and the way work gets done is changing with them. Gartner research shows that leaders who take this big picture approach by focusing on AI-human collaboration and its organizational implications outperform laggards who focus primarily on technology adoption.

The pressure to rethink how work gets done in supply chain is only growing, with 88% of supply chain leaders surveyed by Gartner believing it likely or very likely that advancements in agentic AI alone will require new processes for future talent pipelines. That urgency is coming from the top with CEOs looking to leaders across the business to help drive transformation with AI.

That makes AI more than a technology decision. It is a talent, process, and performance issue that sits squarely with supply chain leadership. To capture the full value, CSCOs need to rethink the expectation for their talent, how work is organized and how teams perform through the lens of AI-human collaboration.

To maximize success, CSCOs should prioritize the following three shifts.

1. From “do my job” to “co-evolve with AI”

The first shift is a change in mindset. Many supply chain teams treat AI as a tool that can help people efficiently do existing tasks. However, as AI agents start proposing options, explaining trade-offs, and taking guided action, they can play a larger role in how work gets done. This changes what employees are expected to contribute and how leaders should provide clarity on that expectation.

CSCOs should prepare teams to work with AI as a collaborator, not as a background application. This calls for stronger AI and data literacy and better judgment. Planners need to know when to trust a recommendation from AI and when to push back. Frontline supervisors need to know how to use time saved by automation in ways that raise the quality of decisions, coaching, and execution.

In practice, that could mean a planner spending less time assembling reports and more time resolving exceptions, or a frontline manager using AI-supported insights to coach team members more effectively. The goal is to raise the value of human work as AI takes on more of the routine load.

2. Redefine performance measurement for human–AI teams

As AI becomes part of execution, CSCOs need a broader definition of team performance. Beyond meeting supply chain goals and objectives in the realm of cost and revenue, CSCOs also need to factor in how people and AI work together under real operating conditions.

For example, if an AI agent flags a likely supply disruption earlier than the team would have spotted it on its own, the real measure of success is what happens next. Did the team act quickly, weigh the trade-offs, and make a stronger decision because the signal arrived sooner? That is the kind of intelligence CSCOs need to understand and measure.

3. Embrace agility in workflows and team formations

Many organizations still respond to friction by revisiting job boundaries, process maps or individual KPIs. Those tools have their place, but are not fast enough in supply chains where priorities are changing and decisions are made in real time in response to disruptions. When teams must wait for a formal process review to adjust roles or handoffs, confusion lingers longer than it should, and momentum is lost.

CSCOs need a more adaptive operating rhythm. Core accountability should remain clear, but teams should be able to adjust parts of the workflow as conditions change. Consider a team working from an outdated process design while AI is already predicting demand shifts and triggering earlier replenishment signals. If role clarity is revisited only during a quarterly review, the result is avoidable delay, duplicate effort, and missed value. However, if the team can rework handoffs and regroup around emerging needs, they will be better positioned to keep pace with the business and get more from AI.

Leading through AI-human collaboration

AI will create the most value in supply chains when leaders treat it as an operational issue, not just a technology rollout. That means preparing employees to work alongside AI, redefining what strong team performance looks like, and giving teams more flexibility in how work is organized. CSCOs who make those shifts will be in a stronger position to use AI in ways that improve execution, strengthen decisions, and keep the organization moving as the environment changes.

About the author

Mel Mohamednur is a Director Analyst with Gartner’s Supply Chain Practice. As part of the supply chain talent team, Mel works with CSCOs, head of strategy, and supply chain leaders to navigate talent strategy development. Mel and other Gartner analysts will provide additional insights on human and AI collaboration at the Gartner Supply Chain Symposium/Xpo, taking place May 4-6 in Orlando, FL. Follow news and updates from the conferences on X using #GartnerSC.

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