A gap is widening between the initial excitement for generative artificial intelligence and the realities of its implementation, according to a new report from Gartner. The research and consulting firm’s 2025 Hype Cycle for Supply Chain Strategy indicates that generative AI has entered the “trough of disillusionment,” a phase where the challenges of practical application begin to overshadow early enthusiasm. This shift occurs as organizations struggle to move pilot projects into full production, facing hurdles with system integration and data security.
While generative AI is facing a period of skepticism, the report places supply chain cybersecurity at the “peak of inflated expectations,” suggesting significant industry interest in leveraging AI to combat rising security threats. This dual positioning highlights a critical moment for enterprise technology, as leaders weigh the immediate necessity of security against the longer-term, but currently troubled, potential of generative AI. The findings come as cyberattacks continue to disrupt major retailers and manufacturers, underscoring the urgency of effective security solutions.
Understanding the Hype Cycle
Gartner’s Hype Cycle methodology provides a framework for tracking the maturity and adoption of emerging technologies. It consists of five distinct phases: the innovation trigger, the peak of inflated expectations, the trough of disillusionment, the slope of enlightenment, and the plateau of productivity. This model helps organizations determine which technologies are ready for investment and which may require more time to prove their value. The 2025 report’s placement of generative AI and supply chain cybersecurity illustrates two critical points in this technological lifecycle.
Phases of Technological Maturity
- Innovation Trigger: A technology breakthrough kicks off the cycle, generating initial media interest.
- Peak of Inflated Expectations: Early success stories emerge, accompanied by a number of failures. It is at this stage that many companies decide on adoption.
- Trough of Disillusionment: Interest wanes as failures and implementation challenges mount. Providers must refine their products to survive.
- Slope of Enlightenment: More concrete use cases and success stories begin to appear, with second- and third-generation products emerging.
- Plateau of Productivity: Mainstream adoption starts, with well-defined criteria for assessing the technology’s value.
Cybersecurity at its Peak
Supply chain cybersecurity has reached the peak of inflated expectations due to a growing number of success stories that have captured the attention of chief supply chain officers. However, this phase is also marked by an increasing awareness of failures, forcing companies to carefully consider their adoption strategies. The rapid expansion of digital supply chains has created a daunting landscape of third-party cyber risks, making it difficult for security teams to keep pace with evolving threats. According to Mark Atwood, Managing Vice President of Research at Gartner, the proliferation of generative AI among trading partners further elevates the risk of data breaches and intellectual property theft.
Organizations are increasingly adopting AI-powered cybersecurity tools to defend against ransomware and malware. Yet, they report significant difficulties in deploying these solutions. The challenges often stem from unclear requirements, the vast scope of IT systems needing protection, and limited visibility into the risks posed by third-party vendors. Despite these obstacles, the intense focus on cybersecurity reflects a broader industry recognition of its critical importance in maintaining operational integrity.
Generative AI’s Implementation Hurdles
Generative AI’s descent into the trough of disillusionment is characterized by the growing pains of moving from small-scale pilots to enterprise-wide production systems. While the technology’s potential remains a topic of considerable interest, the practicalities of integration with legacy infrastructure have proven to be a significant technical barrier. Concerns over data security have also led some companies to restrict deployment, further slowing adoption. Noha Tohamy, a Vice President Analyst in Gartner’s supply chain practice, notes that as more organizations encounter these scaling challenges, generative AI will appear less like a “silver bullet” solution.
Despite these setbacks, Gartner’s analysis suggests that the current struggles with generative AI may ultimately benefit the broader field of artificial intelligence. The continued enthusiasm for its potential, combined with the rise of agentic AI—systems capable of pursuing goals with a degree of autonomy—is accelerating progress in machine learning. Once viewed as an emerging technology, machine learning is now evolving into a key enabler of supply chain transformation, with successful implementations becoming more common.
The Broader AI Landscape
The 2025 Gartner Hype Cycle for Artificial Intelligence provides additional context for these trends, highlighting AI-ready data and AI agents as two of the fastest-moving technologies, both currently at the peak of inflated expectations. This indicates that while generative AI may be facing a period of recalibration, the foundational elements of the AI ecosystem are maturing. To successfully scale AI initiatives, organizations must focus on developing AI-ready data, which refers to data that is fit for use in specific AI applications. However, a significant number of organizations report that their data is not yet prepared for AI integration.
AI agents, which are autonomous or semi-autonomous software entities, are also gaining traction, with organizations deploying them to manage increasingly complex tasks. The development of these technologies underscores a broader shift in the AI landscape, from a singular focus on generative models to a more holistic approach that encompasses data readiness, security, and autonomous systems. This evolving perspective suggests that the path to widespread AI adoption will require a multifaceted strategy that addresses both the opportunities and the challenges of this transformative technology.
