Gartner: top-performing supply chains lead the charge in AI investments
Best organizations will focus on productivity, rather than efficiency or cost savings, over next three years.
Top performing supply chain organizations are investing in artificial intelligence and machine learning (AI/ML) to optimize their processes at more than twice the rate of low performing peers, according to a survey by Gartner, Inc.
Gartner surveyed 818 supply chain practitioners across geography and industry from August through October 2023 to understand how the supply chain is adapting to changes in economic values, fostering sustainable growth, harnessing digital assets’ potential to enhance productivity, and revitalizing the workforce and network of people.
In another finding, the survey also revealed that the best supply chain organizations are using productivity, rather than efficiency or cost savings, as their key focus to sustain business momentum over the next three years.
“Top performing supply chain organizations make investment decisions with a different lens than their lower performing peers,” Ken Chadwick, VP Analyst in Gartner’s Supply Chain Practice, said in a release. “Enhancing productivity is the key factor that will drive future success and the key to unlocking that productivity lies in leveraging intangible assets. We see this divide especially in the digital domain where the best organizations are far ahead in optimizing their supply chain data with AI/ML applications to unlock value.”
The data showed that high-performing organizations are far ahead in automating and/or optimizing processes that utilize supply chain data using AI/ML. Gartner defined “high performers” as those companies reporting performance that exceeded expectations over the last 12 months across the five measurements in supply chain outcomes.
According to Gartner, the divide between high and low performers in optimizing processes with AI/ML hints at a deeper rift in strategy among organizations. Top performers increasingly prioritize extracting value from their digital assets to drive productivity, rather than making digital investments to achieve efficiencies such as cost savings.
“Capturing, protecting and then leveraging an organization’s data through the use of AI/ML is an example of how organizations are increasingly turning towards intangible assets to extract new sources of value,” Chadwick said. “High performing organizations are moving beyond the initial implementation stage to full adoption, resulting in better decision making that unlocks new sources of value.”