Supply chain cybersecurity has reached the peak of inflated expectations, while generative AI (GenAI) is in the Trough of Disillusionment and presents an added threat to secure supply chains, according to Gartner Inc.
“The large number of multitier partners in an organization’s supply chain has made managing third-party cyber risk a daunting task,” says Mark Atwood, Managing VP, research with the Gartner Supply Chain practice. “The rapid expansion of threats continually challenges cybersecurity and supply chain teams to keep pace, while the growing use of GenAI among trading partners increases the risk of data breaches and intellectual property leakage.”
Key takeaways:
· The Gartner Hype Cycle for Supply Chain Strategy 2025 identifies emerging, hyped and proven supply chain technologies, competencies and business models. It highlights the most important capabilities, detailing their maturity, business impact and potential challenges, and provides actionable guidance for effective adoption.
· Gartner Hype Cycles provide a graphic representation of the maturity and adoption of technologies and applications, and how they are potentially relevant to solving real business problems and exploiting new opportunities. Gartner Hype Cycle methodology gives a view of how a technology or application will evolve over time, providing a sound source of insight to manage its deployment within the context of specific business goals.
Gartner
· The Gartner Hype Cycle for Supply Chain Strategy showed that machine learning (ML)-based AI is nearing the Slope of Enlightenment, as explosive interest in agentic and GenAI solutions is accelerating the adoption of machine learning and logic-based reasoning to augment decision making at an unprecedented pace.
· Many supply chain organizations face significant obstacles, including the complexity of integrating GenAI with legacy systems, concerns over data security and intellectual property leakage, and the lack of clear governance frameworks to manage risks such as hallucinations or ethical issues.
· ML-based AI use cases now span planning, sourcing, manufacturing, logistics, and inventory management.
