AI in Supply Chain: Avoiding 'Shiny Object Syndrome'

Artificial intelligence (AI) is a powerful tool driving innovation and operational efficiency across industries, including supply chain management. Fear of missing out (FOMO) on the rapid pace of AI development can lead to what is often referred to as “shiny object syndrome,” where businesses invest in the latest technologies without a clear plan for how they will create value.

As AI continues to evolve, supply chain leaders face a unique challenge: determining how to approach AI strategically, without getting caught up in the hype. Generative AI (GenAI), for example, represents exciting advancements, but it’s crucial to focus on the business value AI delivers, rather than simply chasing after the latest trend for the sake of innovation.

The buzz around GenAI is hard to ignore, and adoption rates are rapidly increasing. According to IDC, AI spending has surged due to heightened investments in GenAI solutions. Many organizations are experimenting with GenAI technologies, integrating them into their digital transformation strategies in hopes of enhancing efficiency and uncovering new opportunities. Early results from these efforts have been promising, with companies reporting improvements in customer experience, increased employee productivity, and higher revenues. However, there is a clear distinction between organizations deploying AI with a strategic mindset and those doing so without a concrete business case. The allure of GenAI is so strong that many tech leaders are making significant investments driven by FOMO, often without evaluating how these technologies align with their core business needs.

It’s important to keep a clear focus on the outcomes. AI adoption should be about understanding how and where AI can truly deliver value for the business.

How can you ensure AI investments deliver tangible results for your supply chain? Here are a few key steps to consider.

1. Evaluate Use Cases Based on Strategic Fit

A fundamental step in adopting AI in supply chain is identifying where the technology can add the most value to your operations. This means prioritizing use cases that align with broader business goals—whether that’s improving operational efficiency, reducing costs, managing risk, or enhancing customer service.

When evaluating potential AI applications, it’s crucial to ask: What business problem am I solving, and how does this technology help? For example, while GenAI excels at transforming raw data to create text-based outputs, it’s not necessarily the best tool for tasks such as optimizing inventory or forecasting demand patterns.

Choosing AI solutions based on their feasibility and impact will ensure that your organization reaps the rewards without wasting resources.

2. Understand the Landscape of Supply Chain and AI Technologies

GenAI, powered by large language models (LLMs), is just one component of the broader AI ecosystem. While GenAI has captured much of the public’s attention due to its ability to create text, images, and more, other AI techniques—including machine learning, predictive analytics, and optimization algorithms—have already proven to be highly valuable in supply chain planning.

For example, machine learning models can analyze historical data and take advantage of many internal and external data sources simultaneously to predict future demand, while optimization algorithms help streamline logistics and transportation planning. These tools provide real value by addressing specific pain points in the supply chain, such as reducing lead times or minimizing inventory costs.

Familiarizing yourself with the various AI models and approaches will help you choose the best tool(s) for specific business needs.

3. Combine AI Techniques for Optimal Results

Another key insight for AI adoption is that AI techniques are not mutually exclusive. In many cases, combining different AI models and approaches can yield stronger, more accurate results. This might mean combining GenAI’s language capabilities with machine learning algorithms for demand planning or optimization techniques for route planning.

Build Your AI Roadmap with a Focus on Value

The potential for AI to revolutionize supply chains is immense, especially when approached with a clear focus on business value. Metrics for success should be tied to measurable business performance metrics such as reductions in lead times, improved inventory management, or cost savings—not the number of AI-powered systems deployed within the organization.

Resisting the hype and taking a strategic approach to carefully evaluate AI solutions based on their strategic fit, feasibility, and ability to solve specific business problems, will help you achieve measurable, long-term success.

The key takeaway is simple: AI should be a means to an end, not an end in itself. By keeping this in mind, supply chain leaders can avoid the pitfalls of “shiny object syndrome” and unlock the true potential of AI.

We recently shared expert insights and tips on how to create an AI roadmap that drives real business results. Watch the webinar recording for a deeper understanding of how AI is being successfully implemented in supply chain, and learn from real-world examples of organizations that have seen success through a value-focused approach.

We help supply chain teams explore the AI landscape, including GenAI and other exciting innovations to advance their supply chain processes. Let’s have a chat about how we can empower you to develop a phased AI implementation plan that maximizes ROI and harnesses the full power of AI in supply chain planning software.