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To turn supply chain challenges into opportunities and accelerate transformation, build the right AI foundation to walk before you run 

 

TAKEAWAYS:
● Building a solid AI foundation is crucial for successful supply chain transformation.
● Identifying strategic goals ensures AI applications deliver tangible value to your business.
● Proper support across people, processes, and technology is key to maximizing AI’s potential in supply chains.  

 

 

Introduction

Artificial intelligence and machine learning have enormous potential to help organizations manage ongoing uncertainty and disruption in the supply chain by improving operational responsiveness. That potential is already showing impact. For example, AI features in Scoutbee, a supplier discovery solution, can identify suppliers impacted by tariffs or weather events and suggest alternatives.

We have seen a push to use AI to improve supply chain operations. In the Manufacturing Leadership Council’s 2025 supply chain study, 36% of manufacturers said they already use AI/ML for supply chain digitization. Another 50% expect to integrate it within two years throughout their operations. However, many executives still question how best to proceed and build momentum.

When approaching the AI journey, it is helpful to think in terms of “crawl, walk, run.” Crawling means getting the proper foundation for integrating AI into supply chain operations. West Monroe’s paper, “Laying the Groundwork for AI in Supply Chain Management,” discusses elements of that foundation, including:

• Clear leadership and accountability
• Integrated business planning across functions
• Proper data governance
• Optimized core supply chain systems

Without this work, it is hard to walk—that is, start to develop and scale practical use cases and build AI maturity. Here again, how you proceed will make a difference between spinning wheels and investing in a way that maximizes return. Below, is an overview of the four key steps that prepare your supply chain organization to accelerate the pace of your AI journey.

1)    Start with your strategic goals and objectives

Ultimately, AI is another capability for helping your business where it aims to be—that is, a means to an end, not the end in and of itself. Before developing new use cases, you must consider the purpose for using AI in your organization and why it makes sense. That means defining your business and operations objectives, apart from AI. Approaching this from the bottom up will ensure AI applications provide tangible value and prevent you from falling victim to the AI “flavor of the week.”

Supply chain strategy typically targets objectives around cost reduction, revenue capture, revenue growth, customer experience, and market differentiation.

2)    Assess the challenges or pain points that prevent you from achieving your objective

AI use cases should be purposeful in their application, either alleviating challenges and pain points or providing pathways to achieving your strategy. Taking an issue-based, value-driven approach to implementing AI offers more significant benefits than simply implementing it to say you are using the latest and greatest technology.

Some of supply chain executives’ most common challenges in actualizing their strategies mirror the goals outlined above:

Data is a common denominator across these challenges—including quality, availability, and access. In the MLC’s supply chain survey, 60% of executives said they lack common data platforms across the supply network.

Technology is another limiting factor across operations. One common issue is fragmented and legacy technology applications where best of breed solutions are deployed on a per function basis. Additionally, many organizations have under-adopted the technology in which they have invested. For instance, they have a transportation management system but still use manual outreach to connect with carriers. They have a planning system, but planners still pull information into Excel or custom tools for analysis and then load the output back into the system afterward. In the MLC’s Future of Manufacturing Project Survey, 68% of manufacturers said they still use spreadsheets for data analysis.

3)    Conduct a cost-benefit analysis for AI use cases that could solve your business problem

Once you have defined specific supply chain strategies and identified challenges to delivering on those strategies, you can begin evaluating AI use cases that solve the business problem—determining which use cases provide the best potential return on investment.

When identifying possible AI use cases, don’t assume AI is the correct answer for addressing a challenge. Instead, look at solutions across people, processes, and technology to determine which solution or combination of solutions—including AI—is best suited to resolving the problem. This ensures you are applying AI in a way that can solve a problem better than other alternatives.

The following are several simplified examples of common operational challenges across the SCOR supply chain model, and the traditional and AI-powered solutions that may address them.

4)    Support the selected use cases adequately

You will need the right support capabilities to build AI use cases for maximum value. To do so, look at each use case through three lenses. These are some key considerations for each area:

People

• End-user skills that align with and can support the identified AI use case
• Defined individual and functional responsibilities for supporting a use case
• Ongoing training and informational resources
• Change management and upskilling as needed
• Sufficient internal IT support capabilities
• Job requisitions that integrate AI to support the acquisition of the right talent

Processes

• Supply chain processes redesigned to integrate and embed AI and maximize its utility, rather than just tacking it on to existing processes—for example, redesigning an existing S&OP process to incorporate AI-driven demand planning
• Increased automation, where possible, to produce data for AI analysis and maximize the benefits of AI output

Data and technology

• Assessment of data in its current form to determine whether it can support the identified use case and what, if any, remediation is required to improve data quality and access
• A master data governance program/policy that ensures data maintains conformance with AI use case requirements
• Networks and/or SaaS instances that can support the demand of deployed AI use cases

Finally, it is essential to consider whether current systems can provide sufficient AI/ML capabilities or whether you will need a new solution. Many Tier 1 supply chain solution providers, such as SAP and Oracle, continuously embed new AI and other advanced analytical features into their products. These AI-driven insights can help optimize supply chain management and manufacturing processes—for example, streamlining product development, detecting equipment anomalies, improving field response, predicting ship lead times, sensing demand, facilitating dynamic discounting, and more.

When you can walk, you’re that much closer to running

Approaching these activities strategically and sequentially not only takes you from baby steps to a full stride. It prepares you to accelerate your pace to “run”—with AI integrated into your supply chain processes, skill requirements embedded in training and hiring to build an AI-fluent organization, and AI-native technologies supporting your operations. That’s when you will really begin to see the value from the data and technology in your supply chain ecosystem.  M

 

About the authors:

 

Brian Pacula is a Partner at West Monroe.

 

 

 

Sri Sripada is a Managing Director at West Monroe.

 

 

 

Ian Nugent is a Principal at West Monroe.