How AI Is Transforming Supply Chain Management

Come next year, 95% of data-driven decisions are expected to be at least partially automated. And yet, 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.

In a recent survey, we discovered that those companies mature in artificial intelligence were more likely to define performance metrics early at the ideation phase of every AI use case.

Craft a comprehensive AI in supply chain strategy that not only mitigates risk but also leverages artificial intelligence disruptively to gain a competitive edge. A supply chain AI strategy that is both defensive and competitive addresses the 4 key pillars of AI strategy: vision, value, risks and adoption.

4 pillars of AI strategy

AI vision: Establish your vision for supply chain AI. State clearly how supply chain AI advances supply chain strategy. This is key to encouraging and enabling organizationwide fluency and adoption of AI, and is helpful for funding the right AI in supply chain use cases — ones that will deliver clear return on investment and lead to further innovation. 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: Identify the organizational barriers that could hinder supply chain AI from succeeding, and leverage change management approaches to remove those hurdles — e.g., size the supply chain AI portfolio and/or pilot supply chain AI projects; establish accountability for AI strategy development and execution; collaborate with IT and data and analytics leaders.

Core to a successful supply chain AI strategy is a solid understanding of which processes would benefit from implementing AI. But gathering potential use cases for supply chain digital transformation can be overwhelming, evaluating them can be time-intensive, and prioritizing them can be complex. You must consider the practicability, impact and cost of each proposal while also accounting for potential risks and benefits.

Few chief supply chain officers (CSCOs) have a reliable framework to source, organize and assess different proposals from their teams for generative AI. But limited understanding of the precise applicability of GenAI isn’t stopping them from trying to capture first movers’ advantage; almost 70% of business leaders believe the benefits of generative AI adoption outweigh the risks.

To structure GenAI pilots for supply chain optimization, Gartner recommends that CSCOs map short-term GenAI use cases to four overarching supply chain objectives. The long-term impact of generative AI on supply chain planning will undoubtedly evolve, but mapping function-agnostic tasks to supply chain objectives allows CSCOs to quickly pilot generative AI and net potential short-term benefits for their organization.

Supply chain objectives

1. Defining and implementing a supply chain strategy: This includes designing the strategy and managing its financials and sustainability.

2. Managing technology: This includes managing data, designing a digital roadmap and leveraging technology investments.

3. Building an effective organization: This includes executing a talent strategy and establishing the right culture.

4. Managing supply chain performance: This includes defining and managing processes, developing analytics and governing performance.

Supply chain leaders see supply chain technology as a critical enabler of competitive advantage. According to a recent survey, 27% say that “gaining competitive advantage” or “addressing competitive disadvantage” is one of their top three goals for investing in emerging technology, and 9% say this is their No. 1 goal.

Behind these future supply chain trends, we identified four major areas of AI technology investment:

• Labor: Addressing rising labor costs and labor shortages by supplementing, enhancing or replacing humans with automation. Another capability has to do with motivating the workforce. By 2028, Gartner predicts that 40% of large warehouse operations will have deployed employee engagement tools.

• Intelligence: Leveraging technology to make better, faster and more enlightened decisions. As supply chains have become increasingly complex and volatile, decision-making speed and quality persist as a top internal challenge supply chain organizations face. This is motivating companies to invest in a variety of disruptive technologies such as advanced analytics, machine learning (ML) and — most recently — generative AI. 

• Edge: Blending the value of applications in the cloud with the responsiveness and performance of technologies sitting at the edge. In fact, by 2027, 80% of manufacturing operations management solutions are expected to be cloud-native and edge-driven.

• Security: Addressing cyber risks prevalent in supply chain organizations today. Our research shows that by 2026, 15% of supply chain software will actualize software bills of materials (SBOMs) to thwart cyberattacks.

Supply chain AI has the potential to redesign job roles, upend customer demands and transform workplace productivity. But not all supply chain AI use cases are equal. To maintain supply chain cost-efficiency, it’s important to distinguish AI initiatives by those within boundaries, pushing boundaries and breaking boundaries, as explained below.

• Everyday AI: This type of AI is typically focused on productivity. While it helps increase efficiency in ordinary tasks, it’s unlikely to create true market differentiation for supply chain organizations.