Gartner: Technology Considerations for Procurement Leaders

In a post-pandemic world defined by geopolitical instability and economic uncertainty, organisations are navigating an environment rife with disruption and cyber threats.

In its latest Hype Cycle report, Gartner has highlighted a critical divergence in the maturity of technologies vital to procurement and supply chain leaders.

The consulting giant's analysis places supply chain cybersecurity at the ‘Peak of Inflated Expectations’. In contrast Generative AI (Gen AI) is situated in the ‘Trough of Disillusionment’. This presents a complex challenge for leaders weighing the procurement of new technologies against the escalating risks within their supply networks.

Gartner's Hype Cycle for Supply Chain Strategy 2025 provides a visual snapshot of technology maturity to help Chief Supply Chain Officers (CSCOs) and procurement teams make smarter investment decisions.

Understanding the hype cycle framework

Gartner’s methodology tracks a technology's evolution across five phases. 

The framework gives leaders a clear view of a technology's journey from conception to mainstream use, helping them decide precisely when to invest to meet their specific business goals.

Procurement and third-party cyber risk

For procurement leaders, managing third-party cyber risk across extensive supplier networks is a major and growing challenge.

The Gartner report emphasises this, noting that the high expectations for cybersecurity solutions are a direct response to the increasing frequency and sophistication of attacks such as ransomware and data breaches on supply chains.

The positioning of cybersecurity at the peak of inflated expectations suggests that while awareness is high the market is still maturing with many solutions yet to prove their long-term effectiveness.

“The large number of multitier partners in an organisation’s supply chain has made managing third-party cyber risk a daunting task,” explains Mark Atwood, Managing VP Research within the Gartner Supply Chain practice.

This complexity creates numerous potential entry points for malicious actors.

Mark adds: "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.”

This places a heavy burden on procurement to implement rigorous vetting processes and continuous monitoring of supplier security protocols moving beyond one-time assessments.

AI adoption and implementation hurdles

While Gen AI is in the trough of disillusionment, its more mature counterpart machine learning (ML) is nearing the ‘Slope of Enlightenment’.

According to Gartner, interest in agentic AI and Gen AI has accelerated the progress of ML, which is now being implemented across planning sourcing manufacturing and logistics to improve decision-making.

However, enthusiasm for AI is tempered by major concerns. The report highlights organisational worries about data security when using Gen AI, particularly regarding the exposure of confidential commercial data intellectual property and customer information to external models.

Furthermore, the path to adopting these technologies is often unclear. Many leaders lack visibility into their third-party risks, the scope of IT systems needing protection and the internal skills required to apply effective solutions as the talent gap for AI specialists remains a major barrier.

“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,” adds Noha Tohamy, Distinguished VP Analyst in Gartner’s Supply Chain practice.

“However, 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.” 

ML algorithms, for example, are already optimising inventory levels through predictive demand forecasting and automating routine procurement tasks.

For procurement teams, this means exercising caution with newer AI like Gen AI in the supply base. In contrast, the proven benefits of ML present a compelling case for strategic adoption to boost operational resilience.

Ultimately, a balanced approach is essential.