We are predictive people. We desperately want to know what’s next, and we’ll use whatever resources we have to understand what’s ahead. 

At one time, weather forecasting was based on our observation of animal behavior and cloud shapes. Baseball scouts, tasked with identifying the next phenom from a morass of marginal players, relied on intuition and the 80-20 rule to predict the best players. And consumer patterns — who will buy what, when, and for which price — were determined by focus groups and surveys.

In the past, supply chain planning was based on a look back at what happened to better plan for the future, with the belief that history would repeat itself. 

Today, with the emergence of artificial intelligence, our predictive capabilities are transforming before our eyes, generating forward-thinking insights as never before. As a result, what made supply chain strategies successful in the past won’t make them successful moving forward. 

To be sure, despite massive investments in people, processes and technology, supply chain outcomes remain inconsistent and uncertain. The causes are multifaceted, including SKU proliferation, new consumer channels, digitization, (de)globalization, regulation, legacy systems, disconnected processes, complex networks and misaligned key performance indicators.

Supply chain and logistics entities are awash in data that could help them address these challenges. But they often struggle to leverage the information to become truly analytics-informed and data-driven.

The Harvard Business Review reports that less than a quarter of organizations say they are successfully data-driven. At the same time, more than 90% of executives say that soaring data volume is making their organizations less effective decision-makers. 

AI-powered technologies offer a better way to address perennial supply chain challenges.

A February, 2024 Gartner analysis found that the top supply chain organizations are using AI to optimize their processes more than twice as often as their lower-performing counterparts. Simply put, the more proactive an organization is in its pursuit of AI implementation, the more prepared it is to take advantage of AI's capabilities.
 Organizations that act now have an opportunity to establish a competitive foothold by seeing and capitalizing on what others miss. AI is achieving this quickly, making information accessible to everyone in minutes rather than days, months, or years.

The latest AI technologies have enormous potential to predict supply chain outcomes. Yet brand differentiation isn’t to be found in the mere adoption of AI technologies. The key lies in how they’re deployed.

Following are three best practices for deploying AI in supply chain management to drive efficiency, innovation. and competitive advantage.

Start smart. Supply chain management and logistics entities don’t need a perfect road map to make AI matter. They also don’t need to reimagine their entire operational framework all at once. Instead, deploy AI steadily and strategically, making incremental, iterative changes to improve overall performance. 

To get started, identify a need and investigate how AI can help solve that problem by providing a faster workflow, enhanced data analytics or better decision-making efficiency. Begin with the most pressing problems, then expand your AI integration as your comfort and capabilities expand.

Prepare people to participate. The safe and effective integration of AI into supply chain and logistics workflows requires more than a plug-and-play approach. It demands intentional training and preparation. 

Employees know this. One Ernst and Young survey found that 80% of employees want more training and upskilling to feel more comfortable integrating AI at work, and 73% are worried that their companies aren’t doing enough to prepare them for AI adoption. 

Involve employees in the AI journey. Teach them to use the technology through comprehensive training programs and responsive skills-transference initiatives. At the same time, create channels for feedback, reflecting a holistic growth mindset ready to adopt the technology safely and effectively.

Learn, collaborate, and act. AI is an assistive technology. That’s why many of the most prominent AI products are branded as co-pilots. 

For logistics and supply chain entities, this means that simple, natural language prompts can lead to valuable insights, streamline complex processes and enhance decision-making capabilities.

IBM’s analysis of AI in the supply chain sector found that the technology excels at offering “assistance in forecasting, such as demand planning or being able to predict production and warehouse capacity based on customer demand.” AI makes information accessible by allowing everyone, from executives and high-level decision-makers to laymen and novice users, to search expansive data sets in conversational dialogue.

Ultimately, the goal is to elevate human decision-making, so prioritize usable insights over flash and novelty. Focus on implementing AI tools that provide real value and enhance the capabilities of your team, rather than those that simply offer impressive but superficial features.

Changing the way things have always been done won’t be easy. In this case, it means doing more than adapting how we work; it means redefining who we are. The supply chain experts of the past will give way to digitalists who rely on AI-powered technology to elevate decision-making.

It’s worth the work.

Allan Dow is president of Logility.