Gartner: Is AI a Help or Hindrance to Supply Chain Security?

A report from Gartner examining supply chain strategy trends has highlighted how business leaders are approaching AI.

As supply chains experience a rise in disruption from a growing number of cybersecurity threats, leaders are debating whether AI is a help or a hindrance to operational safety.

With various types of AI entering the market, some are being favoured more than others.

Gartner, a research and advisory firm, provides insights and tools to organisations globally.

Its Hype Cycle offers a visual representation of the maturity and adoption rates of new technologies and applications.

The methodology considers the evolution of a technology to provide insights into how it can help companies meet their business goals.

The Gartner Hype Cycle for Supply Chain Strategy, 2025, is aimed at helping Chief Supply Chain Officers (CSCOs) make more informed business decisions and investments by examining relevant technologies.

It has five stages: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment and Plateau of Productivity.

Supply chain cybersecurity expectations

Gartner reports that supply chain cybersecurity is at the Peak of Inflated Expectations, while Gen AI is in the Trough of Disillusionment which could present a threat to supply chain security. This follows a series of cyberattacks on retailers and manufacturers that have made cybersecurity a key concern for businesses. In response, more supply chain leaders have been adopting AI processes to add further risk mitigation solutions. The rapid growth of this technology over the past few years has also caused some concern.

“The large number of multitier partners in an organisation’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.

Mark explains: “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.”

Generative AI adoption challenges

While there is a clear need for cybersecurity protection, many companies find it difficult to implement.

A lack of clarity, an undefined scope of the IT systems requiring help and poor visibility of third-party risk can mean leaders find themselves lacking the resources to apply these solutions.

As generative AI technologies can create new strategies at low risk and cost, some organisations remain concerned about data security.

“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.

Noha says, “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.”

Machine learning and risk mitigation

Machine learning (ML) is a field of AI that constantly identifies patterns to make decisions and improvements automatically without human involvement.

According to Gartner, ML is nearing the Slope of Enlightenment, propelled by interest in agentic and generative AI.

It is helping businesses with their decision-making at a much more rapid pace, a factor leaders have found necessary in today's turbulent climate.

With ongoing threats to cybersecurity, more leaders have been keen to implement AI software to protect the supply chain from ransomware threats and malware attacks. These attacks can cause operational outages, halting business activities.

As a result, more leaders are noticing the need to safeguard supply chain operations with high expectations for cybersecurity software.

Despite some concerns around data security with generative AI, ML-based AI is being implemented at a large scale across planning, sourcing, manufacturing, logistics and inventory management.

Due to its success, CSCOs are scaling it across their supply chains to increase efficiency and resilience.