Vision, Value, Risk and Adoption

As it gears up for its Supply Chain Symposium May 6-8 in Orlando, the analysts at Gartner have identified what they say are the four pillars of success for use of artificial intelligence (AI) in the supply chain.

Before we get to that, Gartner said in a press release this week that the first key to AI success is getting the strategy right before developing the technical solutions.

Supply Chain Digest Says... Companies should prioritize their supply chain AI initiatives based on their value and their feasibility, Gartner says, as agreed to by both supply chain leaders and other stakeholders in the business. What do you say? Click here to send us your comments   Click here to see reader feedback

AI is seeing rapid uptake. Gartner research finds that by next year, 95% of data-driven decisions are expected to be at least partially automated through use of AI and its cousin machine learning.

However, Gartner says, only 10% of CEOs say that their business uses artificial intelligence strategically.

Gartner adds that in a recent survey, it found companies that rate themselves as mature in artificial intelligence were more likely to define performance metrics early at the ideation phase of every AI use case.

Companies, Gartner says, need to “craft a comprehensive AI strategy that not only mitigates risk but also leverages artificial intelligence disruptively to gain a competitive edge.

The Four Pillars of AI Strategy

The four pillars of supply chain AI success are as follows, Gartner says:

AI Vision: Companies must first establish their vision for supply chain AI. That includes clearly stating how supply chain AI advances the overall supply chain strategy.

“This is key to encouraging and enabling organization-wide fluency and adoption of AI and is helpful for funding the right AI use cases - ones that will deliver clear return on investment and lead to further innovation,” Gartner adds.

Gartner says that the 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

AI Value: Companies also need to Identify the organizational barriers that could hinder supply chain AI from succeeding, and leverage change management approaches to remove those hurdles. Those moves include rightsizing the supply chain AI portfolio and/or pilot supply chain AI projects; establishing accountability for AI strategy development and execution; collaborating with IT and data and analytics leaders.

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AI Risks: Gartner says companies also should identify the regulatory, reputational, competency, technology and other supply chain risks that should be mitigated.

Mitigation strategies include:

• Establishing AI governance
• Strengthening cybersecurity
• Developing data literacy among the supply chain workforce

AI Adoption: Companies should prioritize their supply chain AI initiatives based on their value and their feasibility, Gartner says, 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 observes.

It adds that a use case with a seemingly outstanding value contribution and strong feasibility is either a breakthrough or the market is missing a great opportunity.

SCDigest simply says we are very early in the supply chain AI game.

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