With the difficulties we're confronting today, there has never been a more important time to take full advantage of AI. The answers that AI holds for manufacturing are well-suited to helping them adapt to the pressures and topsy-turvy conditions the pandemic created.
In a recent global survey about AI, responses from more than 300 executives across industries pointed to growing evidence that we are on the verge of a momentum shift. Conducted by Forbes Insights and sponsored by SAS, Intel and Accenture Applied Intelligence, the survey reveals that AI deployment has gone beyond discrete use cases or experiments and into enterprise-wide adoption. Even with gaps in capabilities and strategy revealed through the responses, respondents indicate widespread AI adoption just around the corner – in manufacturing and every other industry.
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IT TRENDS IN THE MANUFACTURING SECTOR: TECHNOLOGIES LAGGING DESPITE TOUGH COMPETITION
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“The truth is that large global manufacturers still rely heavily on older, disconnected machinery”, says Marcia Walker, principal industry consultant, SAS global manufacturing industry practice. “So, while many manufacturers are using AI, in this survey we see that they’re using it in unexpected ways – in customer-facing operations, for example, or in the areas of warranty claims and recalls. There’s still so much that manufacturers will be able to do with AI as their businesses evolve and they continue to invest in modernizing other parts of their businesses. The good news is that we’re finally seeing AI move into aspects of manufacturing that have remained analog for years, such as on the factory floor. In production, for example, my own experience shows that image recognition is being more widely adopted.”
So, what do manufacturing executives report about their experiences with AI? Where are they focusing today? What’s working? What are their plans for the future?
Twenty-six percent of manufacturing respondents report that AI-based technology has been deployed, and 50% say it’s under development. These figures are roughly in line with other industries such as consumer packaged goods and retail. This suggests that the manufacturing industry has embraced AI. But, looking more closely, squaring these positive responses with actual experiences in the industry proves difficult.
According to Walker, new manufacturing customers often describe themselves as using AI. But an examination of which AI technologies they’re using (and how), usually shows they’re using AI as a catch-all term for everything from simple dashboards to analytics, basic statistics and rudimentary software-aided automation.
Think of AI as the science of training systems to emulate human tasks through learning and automation. Many manufacturers simply haven’t progressed to that level yet. But when they do, the results can be remarkable.
Given how manufacturers have embraced IoT, the opportunities from applying AI to IoT data seem exponential. “Machine learning and artificial intelligence are areas we’re emphasizing heavily now,” according to Conal Deedy, director of connected vehicle services for Volvo Trucks North America. “We’re uncovering hidden insights in our data and merging that with the truck knowledge from our engineering group. Together, we are in a much better situation to understand exactly what the data is telling us and integrating it into the remote diagnostics service. We are already seeing the benefits and the future is extremely exciting. We can now process millions of records in real time, expanding Volvo’s remote diagnostics capabilities, which on average helps reduce diagnostic time by seventy percent and repair time twenty-five percent.”
When asked to identify the central challenges to successfully implementing and applying AI in their organizations, manufacturers rightly pointed to a range of issues, from ensuring AI-based outputs are objective and neutral (24%) to a lack of development/deployment expertise (26%) and more. And, two related responses stood out – organizational culture (22%) and resistance from employees due to concerns about job security (16%).
Over the years, one of the biggest recurring themes is related to AI is convincing manufacturing employees that the AI systems are as reliable as their gut instincts. A big part of the opportunity for manufacturers in relation to AI will involve creating the right conditions for the cultural changes that will help AI adoption take root.
As is often the case, success breeds more success. One way to foster a culture change is to start with a win.
For example, a European-based manufacturer was pursuing an aggressive plan for new AI initiatives. At first it seemed to make sense to pick an initial AI project that had the greatest potential benefit. But instead, a lower ROI initiative was suggested for one important reason – it was the project most likely to win over skeptical engineers and provide proof that AI works. That early-win AI project convinced the engineers that AI could deliver reliable, trustworthy results, paving the way to implement higher-ROI projects based on the confidence that AI can work in their organization.
It should come as no surprise to any leader working in manufacturing that alignment between business objectives and IT presents a huge challenge to AI implementation. Twenty-two percent of survey respondents pointed to this as one of the most pressing problems they face.
Over the years, operational technology (OT) has become more specialized and sophisticated. And despite important advances toward standardization, these teams still have difficulty communicating with one another, much less agreeing about IT infrastructures. After all, the engineers who designed OT and IT capabilities tend to come from different engineering fields whose systems were designed to solve different problems – software engineering (IT) or mechanical engineering (OT).
Why is this OT-IT collaboration important for AI projects? Because in a manufacturing environment, AI should be able to operate at the intersection of OT and IT. That will not only require enhanced systems learning to communicate with one another, but also deep collaboration between different types of IT and OT leaders as well.
Today, many manufacturers simply aren’t there yet. But there are hopeful signs pointing the way to greater IT-OT collaboration in the service of AI in manufacturing. For example, the continuing advance of cloud capabilities has already forced manufacturers to standardize in ways that open the door to similar approaches for AI.
It seems clear that AI will continue to expand among manufacturers, as leaders become more adept at deploying these capabilities, and as the capabilities themselves become even more powerful. While what’s next will vary from facility to facility based on their operating environment, it’s a safe bet that the factors that set successful AI adopters apart will figure prominently among manufacturers. These factors include:
It’s clear that AI deployment is accelerating and only just getting started. If AI were a five-stage rocket, we may be firing the third stage now. And as AI continues its ascent, many of the issues examined in the survey will grow in importance, entering more boardroom-level conversations, landing on more implementation-level meeting agendas and appearing more frequently in media accounts.
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