Gartner: Why Gen AI is Hitting a Disillusionment Phase

The gap between AI hype and reality is becoming clearer as organisations struggle to move pilot projects into production, with early enthusiasm giving way to questions about practical implementation and return on investment.

Gartner has published its annual Hype Cycle for Supply Chain Strategy, placing Gen AI in the trough of disillusionment while positioning supply chain cybersecurity at the peak of inflated expectations. 

The findings suggest that while cybersecurity solutions are attracting significant interest from chief supply chain officers, Gen AI is entering a phase where implementation challenges are tempering earlier excitement.

But why? And what does this mean for enterprises?

Gartner’s methodology to help companies track what technology to invest in

Gartner’s Hype Cycle methodology tracks technologies through five phases: 

• Innovation trigger
• Peak of inflated expectations
• Trough of disillusionment
• Slope of enlightenment
• Plateau of productivity

The framework helps organisations assess which technologies warrant investment by examining maturity and real-world application.

The 2025 cycle indicates that supply chain leaders are weighing how AI technologies can address security threats while managing concerns about data protection and system integration. 

The research arrives as retailers and manufacturers face mounting pressure from cyberattacks that have caused operational outages at companies including Marks and Spencer and Jaguar Land Rover.

Why supply chain cybersecurity has reached peak expectations

Supply chain cybersecurity has hit the peak of inflated expectations, the phase where early success stories generate significant interest but failures also begin to emerge. 

Companies at this stage typically decide whether to adopt the technology based on initial results from early implementers.

Mark Atwood, Managing Vice President of Research at Gartner’s supply chain practice, says: “The large number of multitier partners in an organisation’s supply chain has made managing third-party cyber risk a daunting task.

“The rapid expansion of threats continually challenges cybersecurity and supply chain teams to keep pace, while the growing use of Gen AI among trading partners increases the risk of data breaches and intellectual property leakage.”

The positioning shows growing adoption of AI-powered cybersecurity tools designed to protect supply chains from ransomware and malware attacks. 

However, organisations report difficulties deploying these solutions due to unclear requirements, the scope of IT systems requiring protection – and limited visibility into third-party risk.

Machine learning (ML), a subset of AI that identifies patterns in data to make automated decisions without human intervention, is nearing the slope of enlightenment. 

This phase indicates that concrete examples of successful implementation are emerging. 

Gartner attributes MLs progress to interest in agentic AI, systems that can pursue goals autonomously. 

ML tools are being deployed across planning, sourcing, manufacturing, logistics and inventory management functions.

Why Gen AI faces implementation challenges

Gen AI has entered the trough of disillusionment, the phase where interest declines as implementation challenges and failures mount. 

The positioning reflects difficulties organisations face when attempting to move Gen AI from pilot projects to production systems. 

Integration with existing infrastructure presents technical obstacles, while concerns about data security cause some companies to limit deployment.

Noha Tohamy, Vice President Analyst in Gartner’s supply chain practice, says: “As more organisations grapple with the challenges of scaling Gen AI pilots and integrating the technology into legacy systems, it will appear as less of a ‘silver bullet’ solution.”

Despite current challenges, Noha notes that Gen AI’s trajectory may accelerate ML adoption more broadly. 

She says: “The ongoing enthusiasm for Gen AI’s potential, along with the emergence of agentic AI, has rapidly accelerated the progress we have seen with ML-based AI, which has evolved from an emerging technology to a key enabler of supply chain transformation.”