Gartner: Is ‘Agent Washing’ a Risk to Supply Chain Planning?
As agentic AI continues to transform the supply chain planning landscape, there is a warning from leaders at Gartner that people must be aware of hype and “agent washing”, which are creating real risks for organisations under pressure to deliver results.
Supply chain planning (SCP) leaders who focus on proven use cases, strengthen data and governance foundations and follow a deliberate adoption sequence can improve near-term productivity while preparing for more advanced agentic capabilities over time, attendees learned at the Gartner Supply Chain Symposium in Barcelona.
“SCP leaders should prepare for an agentic AI future, but they need to separate meaningful capability from market noise,” says Jan Snoeckx, Senior Director Analyst in Gartner’s Supply Chain practice.
“The priority today is not full autonomy, but building the operational discipline, architectural flexibility and decision frameworks that allow agentic AI to scale as the technology matures.”
Separating market Noise From True Autonomy
Agentic AI is often found at the centre of messaging coming from vendors, as well as executive discussions in SCP. Jan told those in attendance that most of the current agentic capabilities improve user experience through query interpretation, recommendations and conversational support, rather than fundamentally changing decision quality or how decisions are made.
True autonomous planning would require the automatic generation of plans, automatic selection of the optimal plan and seamless execution without human intervention.
Most current solutions have not reached that level of end-to-end autonomy, and Gartner believes that vendors claiming end-to-end autonomous supply chain planning before 2027 are overstating what is possible in the near term.
Jan believes that agent washing further obscures those differences by relabelling conventional automation as agentic, increasing the risk of misaligned investments and long-term lock-in.
Mitigating risks and avoiding common missteps
Despite current constraints, there are immediate opportunities for efficiency gains.
Traditional automation remains well suited to repetitive, low-complexity tasks, while current AI agents can support high-volume, medium-complexity planning actions where risk is low. Gartner recommends a measured approach that captures value now while building the foundations for more advanced agentic planning in the future.
- Do not mistake vendor positioning for true autonomy: Organisations should rigorously scrutinise agentic claims, because many current offerings do not independently re-sequence objectives, negotiate trade-offs or adapt execution logic.
- Avoid monolithic transformations and legacy retrofits: Inflexible upgrades and retrofitted agents can limit future flexibility, cap ROI and increase long-term lock-in.
- Do not pursue high-risk autonomous use cases too early: Cross-enterprise negotiation, dynamic cost trade-offs and ethical judgment are poor candidates for agentic AI before 2027.
A roadmap for strategic AI integration
According to Gartner research, supply chain planning leaders should take the following actions when evaluating agentic AI solutions:
- Prioritise “sweet spot” use cases for immediate value: Focus early efforts on well-defined, high-volume planning activities where impact is measurable and the cost of error is low, such as touchless forecasting for stable SKUs or automated replenishment parameter changes.
- Build data, integration and governance foundations: Invest in unified, real-time data, robust integration across planning and execution systems and transparent governance with clear guardrails, human hand-off points and audit mechanisms.
- Apply frontier AI beyond assistants: Use AI not only for conversational support, but also to create and maintain digital twins, automate data management and integration and improve how users engage with planning decisions and trade-offs.
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Jan Snoeckx
Senior Director Analyst SC Planning Technology