Company NewsApril 25, 2024

The opportunity advantage of early AI adoption 

Failure to adopt AI quickly and effectively can present a significant challenge, particularly in consumer-facing industries like manufacturing.
Avatar Shoshana Kranish

Artificial intelligence is quickly becoming ubiquitous. Everywhere you look, it’s in the news for some reason or another. It’s feared, not yet understood. It might take over humanity as we know it, or it might elevate society. It might replace blue and white collar workers alike, or it might enable enhanced productivity and create new job opportunities. Whether you align with the apocalyptic views or the optimistic ones, AI is here to stay. But it won’t replace us; rather, it will enhance our abilities and take us to unprecedented heights.

However, unequal or uneven AI adoption stands to threaten the progress that can be gleaned from it. This is especially true for industries where customer experience is paramount. Implementing intelligent solutions like AI, particularly in manufacturing industries – which stands to benefit greatly from it – will produce net positive results for key players and consumers alike. 

Business meets tech: How quickly is AI being adopted?

According to a CITE Research study conducted in tandem with Dassault Systèmes, “84% of businesses allow the use of AI” in various forms. When most of us think about AI, our minds often jump first to user-facing AI chatbots and image generators, although these represent only a portion of tools available on the market. There’s a plethora of AI and machine learning programs and applications that businesses can – and already do – leverage. Most AI tools, in fact, are behind the scenes. What we as consumers interact with is just the tip of the iceberg. 

In terms of industry adoption, manufacturing is actually one of the few industries that has been open to adopting AI, according to a study from the National Bureau of Economic Research. The researchers found that 14% of manufacturing companies surveyed reported some use of the technology, compared to only 4% in construction, 5% in management and administration and just under 6% in agriculture and mining. The wide applications for AI in manufacturing make it a highly valuable tool. Robots can be deployed to do jobs that are difficult or dangerous for humans, opening up the possibilities for newly defined, skilled jobs that humans can excel at. AI can be integrated into factory machinery, giving operators insights into performance in real-time, and designers can leverage smart product design on the back-end to make the best possible goods for consumers at the best cost and in the best timeframe. 

However common AI is, though, it’s still a relatively nascent technology. That means there’s a shortage of talent for employers to hire. The same CITE/Dassault Systèmes study found there’s a significant skills gap for people who are able to achieve what companies are looking for when it comes to AI, whether that’s leveraging user-friendly tools or developing highly valuable, company-specific ones. And when those individuals are identified, they’re expensive. It’s basic economics: high demand and low supply will drive up costs. Recent findings from the Wall Street Journal indicate that AI and ML engineers are in such high demand, companies are willing to shell out yearly compensation packages of $1 million or more. While the jury may still be out on how the general public feels about AI, in the world of business, it appears to be a welcome addition. 

Could unequal AI adoption have consequences?

Given how much it can cost a company to hire skilled AI engineers to develop, adopt and enhance this type of tech, it’s no mystery why AI adoption is uneven. While smaller, newer businesses might be risk-takers when it comes to implementing emerging technologies into their tech stack, it’s possible they lack the funds to acquire them or the internal knowledge to effectively use them. On the other hand, larger or more-established companies might have the budget to try out new tools, but might also have a lot of red tape or internal policies that slow down the adoption process. 

Regardless of a company’s size or age, it’s likely that they can benefit from AI, even in the most rudimentary sense, like deploying it for internet security or data protection. But some industries stand to benefit – or potentially lose – a lot more than others when it comes to willingness to acquire AI tools. Recognizing the nexus of humanity and technology is key to incorporating AI that works for everyone. 

When it comes to consumer-facing industries like manufacturing, the effects of using AI or not will be keenly felt. As consumers, we’re often shielded from what goes on behind the scenes in the factories where the goods we buy are produced. Yet we’re affected significantly when changes occur in those places. Our wallets empty quicker, for example, when supply chains are disrupted, when shipping routes are forced to change, or when any other number of variables in the production process gets shifted. Over the last several decades, as manufacturing has become a more tech-led industry, productivity has increased to the level we now expect it to be, but as consumers, our demands are only ever-increasing. To keep up, manufacturers will need to turn to AI

The best tool in a manufacturer’s kit 

One of the most significant ways AI can upend manufacturing for the better is by enabling predictive maintenance, which monitors and detects changes in equipment efficiency and compares them instantaneously to massive data sets. It can then diagnose problems before they present production difficulties, thereby reducing lag times and enabling companies to meet deadlines. Having a virtual twin of the machine and the production processes it’s used for can provide a valuable tool for operators to be able to remain agile and make adjustments easily and quickly. Given the sheer number of machinery and parts required in the manufacturing process for any particular good, having technology tracking even the tiniest of changes can be the determining factor in a company’s success and, by extension, a consumer’s happiness. 

And that fact is amplified when processes are scaled. Robots or robotic machinery can be a tool for efficiency around-the-clock, helping companies redistribute workloads from their human employees, enabling them to train those robots or do what they can’t. When implemented with virtual twins, AI can be leveraged for intelligent product design, streamlined modeling and simulating processes, finessing operational optimization and beyond. 

Without a viable tech stack behind the scenes, consumers feel the impact. When machinery isn’t running efficiently and effectively, quality can’t be controlled as easily. When supply chains and product designs aren’t mapped virtually, the lack of agility means bottlenecks, increased lag times, even the inability to cope with material shortages or changes. The result for consumers can be goods that are inconsistent in quality, availability and price. Given the benefits that AI can afford companies, and despite the challenges that can come along with adopting it, why isn’t there a greater effort to democratize access to this type of tech? 

Technology for the future 

It’s not that adopting AI can solve all the potential problems that companies in the manufacturing industry might face. It can’t – after all, it isn’t magic. But it, along with other types of technology, can prevent significant fallout. What helps companies run better will, in turn, be better for consumers. In supporting more efficient processes, AI and tandem technologies like virtual twins can reduce the production of waste, increase the quality of goods produced, lessen overhead costs for preventable machinery repairs and more. 

For businesses, the question shouldn’t be if AI should be adopted, but how much. In manufacturing, there’s plenty to be gained by incorporating technological tools and programs that ensure excellence not only behind the scenes, but for end customers, too. Technology is now just as much of the production process as raw materials. And to optimize those processes at scale to continue to provide for customer demand, technology will continue to be a necessary foundation. 

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