AI is Reshaping Supply Chains and the Transformation is About Skills

Artificial intelligence in supply chain management is already being deployed as a tool to forecast demand, anticipate disruption, optimise inventories and model and manage risks particularly geopolitical risks currently with war in the Middle East.  Most organisations that adopt AI tools are struggling to turn them into a positive return on investment.  The opportunity is now for supply chain leaders to improve AI driven performance expectations and results.

Looking at recent research and industry reporting, organisations are aiming to transition from isolated AI pilots to company wide implementation with the aim to achieve measurable commercial impact, including a reduction in costs, service improvement and built in resilience.   The dominant pattern that has been emerging for some time is in that companies are starting to extract value from AI by investing in better tools across the supply chain, which is improving capability.    This pattern is apparent across all areas, with the early adopters of AI tools seeing the greatest measurable impact in their end-to-end processes.   

From Reactive to Predictive Results

There is a gap in the skills required to scale AI into a measurable return on investment with strong data, improved processes and with execution and management discipline.

Across all sectors a dominant use of AI is to help produce more data in the form of predictive analytics for forecasting tools.  The opportunity now for supply chain professionals is in the expertise to interpret the data and make decisions and consider scenario planning with greater accuracy than before .  

Gartner has predicted that 70% of large organisations will adopt AI-based forecasting by 2030 and that the leaders who are gaining the advantage have already started.

The Skills Gap Emerging in Supply Chain

AI tools, while helping supply chain professionals it does impact them and change what is required of them day to day.

There are a few notable capability gaps emerging which require consideration:

Data literacy
Understanding how AI models generate outputs, what assumptions sit beneath them and how to question any anomalies that show up in the data.

Cross-functional decision-making
AI insights often cut across procurement, logistics, finance and operations. All of these profess must collaborate more strategically and not operate in isolated silos.

Change and systems confidence
Technology adoption frequently stalls as a result of low user confidence and a lack of clear governance models.

The organisations that are making the most of AI capability and investing in their supply chain teams learning and development, are the most successful and are maximising capability.

Technology Investment Without Learning is a Risk

Investment in AI and other technologies to improve supply chain processes alone, does not create the value required for positive transformation. The organisations that are investing in their people and processes outperform those that lead with AI and other tools without the change management and people skills to manage it.

AI tools promise efficiency and resilience and with targeted learning and development, organisations will be closer to achieving their efficiency goals and a stronger ROI on their AI and tech investments.  

Why L&D is at the Core of Supply Chain Strategy

We are working with organisations who are aligning their AI investment with structured learning pathways and are looking at the skills gaps around areas such as:

• Data and digital

• Leadership development

• Applied AI and automation

• Project management capabilities

This is particularly important in sectors with high supply chain volatility, margin pressure and regulations all of which require human judgement and governance with strategic overview and experience.

Embedding AI in L and D Strategy 

There is a widening gap between organisations that treat AI as more of a procurement exercise than a strategy to upskill their teams for successful implementation.

Deploying AI in workforce development does build capability and builds on the learning and expertise already in the supply chain.    Investment in learning and development is a core element of success and reducing skills gaps is the foundation to optimal AI execution in supply chain processes.  

In effect, supply chain transformation at any time is both a systems upgrade and a capability upgrade for the teams involved.