Humans are natural worriers. Our worry, however, can get the better of us. When it comes to GenAI in the manufacturing industry, there has been a significant uptick in anxiety around its implementation:
- What if we aren't adopting AI fast enough?
- What if AI gives us phantom data?
- What if it exposes us to legal risk?
What if, what if, what if...
Instead of asking, “what if everything goes bad,” we should ask, “what if everything turns out great?” This reframing allows us to imagine a future where our GenAI implementation has taken off, and it worked. It puts our imagination and creativity in the driver’s seat, as opposed to our worry.
For the manufacturing sector, such a GenAI-augmented future looks seamless: supply chains flow like a smooth stream, development is innovative and unorthodox, QC is spotless, and automation underpins nearly every aspect of our workflows. This “what if” is possible; these technologies already exist. Here’s how B2B leaders and manufacturers can take advantage of them.
GenAI Through the Entire Supply Chain
WBR insights research showed that 81 percent of B2B leaders are already using GenAI in some capacity – with the three largest use cases being automating tasks, analyzing data, and enhancing decision-making. Over 90 percent of respondents said that those cases were a somewhat or very important investment.
But simply saying “enhancing decision-making" is nebulous. What does this look like in practice?
For manufacturers, GenAI can be used through the entire supply chain process. GenAI tools can map out ideal delivery routes and react to unexpected snarls by creating new routes and schedules. Predictive maintenance can be streamlined through GAN AI (general adversarial network) which requires less initial data to create effective predictions.
These maintenance programs go beyond simply predicting when maintenance needs to occur; self-monitoring GenAI can not only alert operators to when machinery needs replacement, it can generate a maintenance plan replete with vendor recommendations.
In each of these cases, GenAI is examining data, then generating actionable and interactable content that operators can use to make decisions. Instead of a vague blinking error light, an operator receives an AI-generated alert, tailored to the unique situation. The operator can prompt and receive a generated maintenance plan, and swiftly implement the most efficient course of action.
Taken together, these upgrades create a smoother manufacturing process. Errors are fixed more quickly, deliveries are completed on time, and forecasts are more accurate.
GenAI in Development and Design
Generative AI exploded onto the scene with two major programs: ChatGPT and Dall-E, which created text and images on demand, respectively.
These two tools, particularly image generation, can be used in industrial design to speed up the design process. R&D teams can prompt properly calibrated image generators to create design concepts and blueprints based on requested specifications. Or, R&D teams can supply original concepts, then ask for design variations.
Consulting firm McKinsey found that implementing GenAI in manufacturing design can reduce product development lifecycles by up to 70 percent. Products can be created more quickly and more responsively, allowing companies to deliver trend-setting designs and stay ahead of the competition.
B2B leaders should engage their design and engineer teams, challenging them to discover how these tools can supplement their workflows. It’s not simply a question of “how much more can you generate;” it’s a question of, “what can you build that you couldn’t before?”
Incremental Additions
At this point, you’d be forgiven for saying to yourself, “isn’t this a little imaginative?” It’s stepping into Willy Wonka’s chocolate factory to discover that everything is edible and, yes, the snozzberries taste like snozzberries. But imagination and optimism need to be tempered with practicality. How can manufacturing companies get to that imaginative vision of the future?
Start by implementing a manageable GenAI tool that you can adopt quickly and relatively inexpensively – one your employees can take to readily. Once that tool becomes a key part of your workflow, and your employees see how it improves their productivity, then you add a new tool.
Start with a product flaw detector, then move on to supply chain mapping, then implement GAN-powered predictive maintenance, then augment with generative troubleshooting and solutions.
At each of these steps, invite your employees to see how these tools augment their workflows. Investing in upskilling and reskilling talent will be required to ensure your company takes full advantage of the benefits GenAI has to offer. This investment is no less critical than investing in AI itself; these tools work best when they supplement and support human ingenuity.
The data is clear: a supermajority of B2B manufacturers are already using GenAI, with a particular focus on automation, decision-making, and data analysis.
Anxiety about this adoption is natural; there are legitimate questions about legal pitfalls, overzealous adoption, AI hallucinations. These are valid, but manufacturers shouldn’t let these qualms inhibit them from moving into the future.
Instead, be imaginative. Imagine what if everything went great? From your idealized AI-driven future, work backwards until you arrive at the practical, incremental steps you can implement now. We may not be able to build Willy Wonka’s fabled chocolate factory, but we can ultimately create more efficient, profitable, and responsive companies – and we can do it a lot sooner than most of us imagine.
