Agent AI Washing Risks in Supply Chain Planning
Consulting firm Gartner is warning about Agentic AI hype and “agent washing”, explaining that this conduct is creating real risks for organizations under pressure to deliver results.
“SCP leaders should prepare for an agentic AI future, but they need to separate meaningful capability from market noise,” said Jan Snoeckx, senior director analyst in Gartner's supply chain practice, in a statement. “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.”
Opportunities and Risks with Agentic AI
Agentic AI now dominates vendor messaging and executive discussions in SCP. Snoeckx told attendees 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 vendors claiming end-to-end autonomous supply chain planning before 2027 are overstating what is possible in the near term.
Snoeckx said agent washing further obscures those differences by relabeling conventional automation as agentic, increasing the risk of misaligned investments and long-term lock-in.
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.
Snoeckx warned SCP leaders to avoid common missteps in agentic AI adoption:
- Do not mistake vendor positioning for true autonomy: Organizations should rigorously scrutinize 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.
According to Gartner research, supply chain planning leaders should take the following actions when evaluating agentic AI solutions:
- Prioritize “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.