Interest in AI in supply chain (and in almost every areas of a company) remains high, but just how to well leverage the potentially game changing capabilities remains elusive for many companies.
Analytics is certainly one area of major opportunity. Earlier this year, for example, the researchers at Gartner predicted that by 2028, 25% of supply chain KPI reporting will be powered by GenAI models.
What is needed is a plan, and to have a good plan companies need to align that with the business strategy – how will AI help a company’s strategic initiatives and goals.
In other words, don’t build an AI technical plan until you’ve defined how AI enables strategy
However, Gartner research finds that only 10% of CEOs say that their business uses artificial intelligence strategically. Even fewer technology leaders (9%) say their business has a clearly defined AI vision statement.
Other Gartner research found that companies in successful in artificial intelligence were more likely to define specific performance metrics at the early phases of every AI initiative.
In a recent blog post, Gartner further stated that it sees four key pillars of supply chain AI strategy, which we summarize below:
AI vision: Gartner says that companies should state clearly how AI advances supply chain strategy. This is key to encouraging and enabling organization-wide fluency and adoption of AI, and is helpful for funding
Gartner adds that business goals that supply chain AI enables include:
• Reduced supply chain costs and higher productivity via process automation
• Improved customer satisfaction from increased proximity to the customer
• Improved forecast accuracy from predictive analytics
Our take here: We’re not sure a short list of generic potential benefits from AI adds much value.
AI value: Identify the organizational barriers that could hinder supply chain AI from succeeding, Gartner says, and leverage change management approaches to remove those hurdles. Actions might include sizing the supply chain AI portfolio and/or piloting supply chain AI projects; establishing accountability for AI strategy development and execution; and collaborating with IT and data and analytics leaders.
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 AI risks: Another key success factor, Gartner says, is Identifying the regulatory, reputational, competency, technology and other supply chain risks companies may need to mitigate.
AI adoption: Gartner also says companies must prioritize their supply chain AI initiatives based on their value and their feasibility, as agreed to by both supply chain leaders and other stakeholders in the business.
“It’s typical for businesses to pursue initiatives where value is high (and risk also tends to be high, i.e., feasibility is low) but avoid projects where feasibility is so low that it makes the project impossible,” Gartner adds.
We would say so.
Any comments on these 4 AI Pillars? Let us know your thoughts at the Feedback section below.
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