Analyst Insight: Transforming operations with artificial intelligence is front and center for supply chain leaders. But when AI solutions are implemented in isolation, those leaders struggle to realize value for their investments. AI's real promise lies not in chatbots or individual solutions, but in reimagining supply chains and their currently siloed processes to redefine how they work.

The supply chain includes everything and everyone involved in getting a product from raw materials to the customer. But its individual functions often operate in silos, exacerbated by decades of technological limitations. Demand planning, supply planning, warehouse management, and logistics all have their own systems. Material movements, finances, and sustainability efforts get recorded separately. Decisions are therefore made at various stages of planning and execution, based on wildly differing assumptions. 

Now AI has the potential to make the existing infrastructure of data systems, planning and execution systems, spreadsheets, and data science models work as one. It can be used to create a connected semantic layer that acts as a single, understandable source of truth across the supply chain. 

To get there, we need first to create a semantic representation of the business. AI can help enterprises make the most of their existing IT infrastructure, pulling both structured and unstructured data from these systems, contextualizing it, and building a single, connected, semantic layer that replicates the physical value chain. 

If a retailer, for example, moves a product from a warehouse to a store, and then delivers to a customer, that product will be represented in its logistics, warehouse management system, and customer relationship management system. Pulling data into an AI-powered semantic layer means the product can be traced through the entire flow from warehouse to transportation to store, and finally to delivery, using a common framework.

Once built, the semantic layer can be turned into a dynamic foundation for intelligent, data-driven operations. Relevant rules, constraints, parameters, and policies can be codified and incorporated to guide expected behaviors across systems. And technologies like process mining can be used to enrich the representation of the supply chain with knowledge and insights from real business operations. What’s more, business-proven models can be linked to the semantic layer, enabling it to support predictions, simulations and informed decision-making.

Once businesses have a semantic layer that represents the physical supply chain, embedded with intelligence, they can establish a self-serve capability to drive analytics, manage workflows, and execute models on top of this enterprise intelligence. Decision-making frameworks can be used to orchestrate collaboration and drive action, both digital and physical. And the resulting impacts of those actions can be analyzed to enable continuous improvement. 

Once supply chains can operate with this underlying intelligence and connectivity, they can automatically adapt and optimize. By identifying and resolving issues before they become problems they can minimize the impact of the disruptions that have become a day-to-day inevitability. But more importantly, these reimagined, AI-powered supply chains enable enterprises to take advantage of any opportunity to improve customer experience, trigger growth, increase efficiency, cut costs or optimize working capital, to deliver the best possible performance aligned to the enterprise objectives.

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Outlook: AI has the capacity to be truly transformational for supply chains, but only if it’s infused into the very core of operations, to connect siloes and reimagine how end-to-end supply chains work.