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Driver of AI/ML Adoption in the Supply Chain is Productivity, not Cost Savings, Gartner Says

 

Survey also Sees Focus on Getting more from their Intangible Assets


March 5, 2024
SCDigest Editorial Staff
   
There is, of course, huge interest in artificial intelligence and machine learning in the supply chain, but how companies may be thinking about its adoption is in some ways surprising.


Supply Chain Digest Says...

Chadwick added that “Top performing supply chain organizations make investment decisions with a different lens than their lower performing peers.”.

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That according to a new report from the analysts at Gartner, based on a survey of supply chain managers in 2023.

For example, Gartner’s survey found that 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 for AI/ML investments.

OK, that begs this question: what is the difference between productivity and efficiency/cost savings, as the terms in other contexts are used virtually interchangeably?

Not stated in a Gartner press release accompanying the report, SCDIgest assumes that as opposed to the other two terms, productivity has a more positive connotation, implying empowering users to get more done.

The term also turns the focus on potential job losses from AI a bit, with the sense that by enhancing productivity we’re not dealing with replacing humans with AI , but rather extending the potential of the human.

We’ll see if it really turns out that way.

We also note that the applications that were the top in terms of value creation from AI/ML were as follows (high performing vs. low performing organizations):

• Partner with IT to establish unbreachable data security mechanisms (74% vs. 61%)

• Create ethical and binding data privacy frameworks for use of customer data (68% vs. 50%)

• Write cybersecurity measures into supplier and staff contracts (66% vs. 57%)

• Capture supply chain specific customer satisfaction data (58% vs. 40%)

• Analyze and leverage supply chain specific customer use and satisfaction data (57% vs. 35%)

This list surprised us, given that the top 5 did not include any core operational applications.


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CATEGORY SPONSOR: SOFTEON

 
 

 

This, Gartner says, is part of a focus on getting better leverage from intangible assets, such as data and people.

“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,” said Ken Chadwick, VP Analyst in Gartner’s Supply Chain Practice.

Chadwick added that “Top performing supply chain organizations make investment decisions with a different lens than their lower performing peers.”

The headline news from most media covering this research was that “Gartner Says Top Supply Chain Organizations are Using AI to Optimize Processes at More Than Twice the Rate of Low Performing Peers.”

Nothing too insightful from that, SCDigest found.


Any reaction to Gartner's AI research? Let us know your thoughts at the Feedback section below.

 

 
 
 
 
 

 

 

 

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