Don’t Make These Mistakes When Adopting AI in Your Supply Chain

AI’s potential to benefit procurement and supply chain management is immense. It can streamline processes, enhance decision-making and uncover efficiencies you didn’t even know were possible. 

But success isn’t automatic. Many enterprises stumble into common pitfalls along their AI journey that they could have avoided with better planning. 

Let’s unpack three of these challenges and the strategies to sidestep them, so you can make your AI investment pay off. 

The First Hurdle: Misunderstanding Use Cases 

Picture this: your team’s all-in on AI, but when it comes to practical applications, the excitement fizzles out. That’s because figuring out how to apply AI—and where it can have the most impact—is easier said than done. 

In fact, 54% of enterprises say understanding use cases is a critical hurdle to clear when implementing AI, according to a recent Foundry survey of senior IT decision makers. 

Let’s make it real. Imagine leveraging a large language model (LLM) to summarize global news in real time. This could help your supply chain react faster to disruptions like natural disasters or political upheavals. Or think about automating contract creation in procurement, saving countless hours. These aren’t just buzzwords; they’re achievable outcomes. But without clear, measurable goals, AI risks becoming a shiny object with little practical value. 

How to Avoid This Pitfall: 

Start small and specific. Collaborate with AI specialists to identify high-impact opportunities that align with your business needs. Set measurable outcomes, track results and expand only after proving value in targeted areas. 

The Hidden Elephant: Data Challenges

 AI runs on data, but not all data is created equal. Between fragmented silos, messy, unstructured data or poor governance, many companies find themselves bogged down before their AI projects even get off the ground. The Foundry survey revealed a lack of internal expertise (45%) and resistance to sharing data across teams (43%) as among the biggest obstacles. 

Even organizations with data lakes and warehouses struggle with cleansing, categorizing and governing their data. These issues slow progress and undermine confidence in AI’s potential. And yet, 56% of enterprises have already adopted regular data audits and quality assessments, while others plan to catch up soon. That’s good news—but it’s only the beginning. 

How to Avoid This Pitfall: 

Prioritize data readiness before diving into AI. Invest in tools for data cleansing, governance and quality assessment. Train your teams to appreciate the value of clean, shared data. A solid foundation will give your AI initiatives the runway they need to succeed. 

The Talent Shortage: AI’s Achilles’ Heel 

The demand for skilled AI talent is sky-high. Unsurprisingly, 59% of enterprises rank it as their top challenge when implementing AI. Hiring externally is an option, but external talent can only take you so far. Not every vendor or consultant is equipped to meet your specific needs, and over-reliance on outsiders can leave your team playing catch-up. 

Internal talent, on the other hand, offers a sustainable path forward. However, only 34% of enterprises have invested in training programs to build their internal capabilities. That means most businesses are leaving potential untapped, and getting out in front here can improve your competitive advantage. 

How to Avoid This Pitfall: 

Partner with proven AI vendors for their expertise, but don’t neglect internal development. Establish training programs, create AI centers of excellence and foster a knowledge-sharing culture to build in-house capabilities that last. 

Strategies for Long-Term Success 

Avoiding these AI pitfalls isn’t just about dodging mistakes; it’s about setting yourself up for long-term success. Here’s how: 

Build Cross-Functional Teams: 

Break down silos by encouraging collaboration between IT, procurement and supply chain teams. 

Invest in Governance: 

Focus on data quality, not just volume. Strengthen governance frameworks to ensure your data is clean, accessible and actionable. 

Stay Agile: 

Regularly reassess your tools, processes, and goals to stay ahead of the curve in this rapidly changing arena. 

The Bottom Line 

We all know the hype by now: AI will streamline procurement and supply chain management and unlock new value. However, enterprises must first navigate AI challenges wisely. By understanding use cases, prioritizing data readiness and investing in talent, you can turn AI from a buzzword into a business advantage. 

For more insights on how to ensure your AI implementation delivers on its promise, download our white paper with CIO -- Top 3 AI Adoption Pitfalls to Avoid in Procurement & Supply Chain Management.