State of Manufacturing Report offers insights into attitudes, strategies and deployments of artificial intelligence in product development and production.
Fictiv has sponsored this post.
Following the development of artificial intelligence (AI) is a bit like riding a roller coaster:
It starts with a breakthrough that gets everyone excited, with proof-of-concepts that conjure images of an impending revolution. As the excitement builds, emphasis is placed on spectacular successes (e.g., DALL-E’s ability to create high-quality images from text alone), balanced by spectacular failures (e.g., the hundreds of crashes caused by advanced driver assistance systems [ADAS]). These peaks and troughs quickly lead to disillusionment as the failures continue to skew the successes and all that early optimism starts to look hopelessly naïve.
But that’s not the end of the ride, because the companies that persist end up finding better applications for the technology in their second- and third-generation products and, eventually, mainstream adoption starts to grow.
This process is generally known as the Gartner hype cycle, and it’s been used to describe technology trends ranging from railways to the World Wide Web. In the context of AI, the low point has its own metaphor: winter. Over the last half century, AI has gone through several hype cycles, each followed by frustrated expectations, cynicism and, eventually, renewed interest.
The idea of artificial intelligence has proved remarkably resilient, with large language models (LLMs) driving the latest set of roller coaster cars up yet another steep incline of anticipation. Will this ride be different or is another AI winter on its way?
One of the best ways to answer that question is to ask the people who understand the limits of the technology within their domain expertise. Consider product development and manufacturing.
AI’s Impact on Product Development and Manufacturing
Fictiv’s latest State of Manufacturing Report comes from a survey of more than 240 leaders in engineering, supply chain, manufacturing and product development. Participants were asked about everything from workforce issues to technology trends, including their perspective on AI.
According to Fictiv, the report was motivated by more than just academic interest. The company’s goal of simplifying sourcing of custom-manufactured parts is enabled, in part, by an AI-powered platform that analyzes 3D models and 2D drawings of mechanical parts to provide design for manufacturability (DFM) feedback and quotes in real time. Gathering these data points helps Fictiv understand its customers and how to help them make the most of its global manufacturing network.
Given how much hype there’s been around AI lately, it shouldn’t be surprising that 97 percent of respondents expect it to impact product development and manufacturing, with nearly as many (85 percent) already having adopted AI or intending to do so in the near future.
What’s more interesting is where the respondents appeared to disagree. The report divides survey participants into two categories based on seniority: directors or C- and VP-level. Asked about their attitudes toward the long-term impact of AI on their business, only 50 percent of directors expressed excitement about AI compared to 78 percent of C- and VP-level respondents. What’s more, directors were nearly four times more likely to be worried about AI than the other group (15 percent versus 4 percent).
What are we to make of these differences? Several possibilities come to mind.
In terms of the relative disparity in excitement, it may be that this has less to do with AI per se than with the introduction of any new technology in manufacturing or product development. Many seasoned directors will know the pain of trying to integrate the latest trending tech into their existing stack, just because their VP or someone in the C-suite watched a few webinars that made them keen to hop on the bandwagon. (Blockchain anyone?)
As such, the prospect of adding yet another shiny new tool to the toolbox may be less exciting to those who need to make it work on top of everything else they’re worried about.
Another potential explanation that’s a bit more charitable to the VPs and C-suites of the world is that the people working at those levels have a wider perspective and thus a better insight into their organization as a whole compared to their directors. Hence, the former group is more excited than the latter because they can see all the possibilities of where AI can impact the functional areas of the business.
Along similar lines, VP- and C-level leaders typically have more experience than directors, and so are better positioned to evaluate whether the latest technology trend is just a flash in the pan or something likely to make substantive change in the long-term. With less experience and greater demands on meeting short- and medium-term goals, directors may not be thinking about what AI could mean for their careers in the future.
Other possibilities abound, both more and less cynical than those suggested above.
AI Adoption Strategies in Product Development and Manufacturing
Trepidations notwithstanding, the 85-percent adoption rate indicates that AI isn’t going anywhere anytime soon. But what does AI adoption actually look like?
The results in Fictiv’s State of Manufacturing Report break down the answer to this question by company size, once again revealing some fascinating differences between groups of respondents. The most common answer to the question, “What approach is your company taking or planning to take to adopt AI technologies in product development and manufacturing?” turns out to be the same for small (38 percent), medium (66 percent) and large (58 percent) businesses: “Working with existing suppliers to implement AI into their solutions.”
It’s in the next most common answers where differences start to show, with large companies almost twice as likely as small ones to choose “Purchasing commercially available AI solutions” at 52 percent and 29 percent, respectively. Medium-sized companies, in contrast, had “Sourcing new suppliers to utilize AI solutions” as their second-most likely response.
Additionally, it’s worth noting that small companies were just as likely to respond that, “We haven’t developed any plans for AI tech yet” as they were to be working with existing suppliers.
Taken together, these data points paint a fairly standard picture of how small, medium and large businesses differ in their operations. Most businesses are conservative by nature, but the larger an organization is, the more inertia it has when it comes to new technologies. In other words, it makes sense that the most common approach to AI adoption is working with existing suppliers because rocking the boat is rarely a good idea. By the same token, small companies may not have the capacity to develop plans for AI adoption, even if they recognize how important it is, but if they do, they’re likely to stick with the suppliers they trust.
The fact that medium-sized companies are the most diverse in their AI adoption strategies also makes sense if you think of them as trying to find ways to gain an edge over their larger competitors while avoiding being outmaneuvered by their smaller, scrappier rivals. It would also explain why medium-sized companies were the least likely to have no plans for AI at all, at just 2 percent, compared to small companies (at 38 percent) and large companies (at 18 percent).
Implementing AI in Product Development and Manufacturing
When it comes to deploying AI in production, Fictiv’s State of Manufacturing Report divides survey respondents into two groups: engineers and everyone else. As you might expect, while both groups agree that AI will have a significant impact (only 3 percent of respondents in each group replied that it will not impact product development or manufacturing), there are stark differences between where they expect that impact will be most prominent.
Engineers were most likely to bet on quality control and inspections, areas that—as the report points out—have already been using AI for several years. The non-engineers, on the other hand, were most likely to place their bets on supply chain management and product design, both relatively novel applications for AI. Product design also had the biggest gap between engineers (36 percent) and non-engineers (51 percent).
Once again, there are several possible explanations for these differences. One could infer that since engineers understand the technology and its limitations better than non-engineers, they’re more likely to expect that its impact will be strongest where AI has already found purchase.
Alternatively, some engineers might see AI’s deployment to product design as encroaching on their professional territory and are understandably skeptical as a result. Ask any professional writer what they think about ChatGPT and you’ll likely get a similar level of skepticism.
Whatever the case, it’s clear that our perspectives on AI are very much influenced by who we are (engineers versus non-engineers), where we work (small versus medium versus large companies), and how far along we are in our careers (directors versus VPs/C-suite). Which perspectives prove more perspicacious is ultimately a matter of time but, right now, the next AI winter seems a long way away indeed.
To learn more, read Fictiv’s 2023 State of Manufacturing Report.
