AI Versus Automation: What’s the Difference?

As part of Solutions Review’s Contributed Content Series—a collection of articles written by industry thought leaders in maturing software categories—Doug Ladden, CEO and co-founder of Deliveright, explains the difference between how AI and automation are used in the supply chain.

Regardless of industry, most people are aware of the broad conversation around artificial intelligence and its potential, and those of us in supply chain and logistics are no exception. Speculation around AI’s power to radically transform the supply chain and logistics industry has reached a fever pitch. Companies of all sizes struggle with shifting industry conditions and cost increases, forcing a need for change. And it goes beyond mere conversation—Gartner recently reported that half of supply chain organizations will have invested in AI and advanced analytics by 2024.  

But behind the speculation is a more important question: What role does AI actually play in supply chain and logistics technology today? Do investors, board members, and business leaders understand what constitutes AI, or has the buzz blurred the lines, leading to confusion between AI and automation? Both are responsible for significant strides in this industry, but they are different, and understanding these differences is essential, especially when selecting technology solutions to support business needs. 

AI and the Supply Chain 

AI enhances automation, leading to significant benefits in overall operations. However, the machines making this automation possible are initiated, operated, and trained by humans. They do not “think” independently. This distinction may seem pedantic, but staying grounded in reality is necessary for understanding and appreciating existing technology and its applications.  

That’s not to say AI doesn’t come with some genuinely nifty capabilities. Right now, there are plenty of tasks for which AI is ideally suited in this industry, such as pattern recognition and its impact on growth, efficiency, and resilience. But things are not always as they seem. Amazon, for instance, has been leveraging AI for years to speed up deliveries—but the public perception is closer to science fiction than reality: People believe Amazon’s use of cool robots represents its most significant AI use case. In fact, many of its robots simply automate the execution of repetitive tasks like lifting heavy packages and triggering the next steps in a process. Still, automation relieves warehouse workers of manual tasks, freeing them up to put their skills to greater use, such as applying AI to build “the world’s best personal assistant,” as Amazon CEO Andy Jassy recently told analysts is in the works.  

AI is vital for improving the health of the supply chain. Its ability to mitigate the rate of damaged goods, especially in delivering big and bulky items, has a material impact on any business. Damages prove a significant drain on both time and money for shippers—and, by extension, retailers. For example, the damage rate for most furniture carriers is anywhere from 12 percent to 30 percent, but there are many ways to address this problem. Implementing quality assurance processes that include various inspection points along delivery routes helps avoid disappointing customers by delivering big-ticket items (like a couch) in a broken condition.  

When it comes to inspection, humans’ pattern recognition skills can prove a double-edged sword. We are predisposed to notice patterns, which is valuable when making big-picture logical deductions. But this predisposition can also work against us: humans are wired with the ability to identify broad patterns. After a while, we become fatigued and can gloss over small details. Damaged shipments are overlooked, even after being inspected in the warehouse and before they go out on trucks. Once we get used to the pattern of un-damaged boxes, our human brains limit our ability to distinguish between them. 

Not so for AI. AI’s pattern recognition capabilities are literally inexhaustible. Where a person might get fatigued and miss details, AI will scan the nine-hundredth box with the same precision as the first. Today, AI plays into mechanisms with a broad and detailed understanding of the path each item takes while being shipped, enabling, for example, more accurate insurance pricing based on SKU, historical data per region, and retailer. In this case, AI enables more informed and precise damage prediction, again based on SKU data. AI also proves helpful for optimizing truck loads and creating ideal routing strategies based on public and proprietary data ranging from traffic to weather patterns.  

Automation and the Supply Chain   

The urge to label something “AI” when it’s actually good old-fashioned automation is understandable. Using the latest tech buzzwords can help convince decision-makers to invest in new technologies—especially crucial in the supply chain and logistics industry. But consider this: If a car radio uses AI to determine which song to play, is it accurate to call the whole car “AI-powered”? Even if a significant investment was made to create a truly AI-powered vehicle, would the ROI be substantial enough to make it worthwhile? This nuance is what’s lost in current conversations about the topic. 

The advancements in AI, automation, supply chain, and logistics technology are emerging as foundational, especially as customer expectations continue to evolve. As AI and its applications become more accessible, it will prove invaluable for tasks far more complex than re-routing a truck based on traffic patterns and real-time tracking of packages. Artificial intelligence and automation will inevitably continue to improve supply chain operations. Every enhancement, cost reduction, efficiency, and improvement will broaden the path to success and increase revenue—one innovative step at a time.