Don’t Let AI FOMO Leave Your Business SOL
When it comes to AI, many manufacturers have come down with a serious case of FOMO (fear of missing out). Peers are announcing new pilot programs; analysts are predicting sky-high ROI; and industry reports promise leaner operations, faster decisions, smarter everything. There’s a growing sense that the future belongs to those who adopt early and move fast. If you’re not doing AI, are you even trying?
This fear—of being left out, of falling behind—is driving a wave of accelerated adoption across the industry. But in the rush to modernize, many inadvertently gloss over a critical question: What are we sacrificing in the name of speed? Is it more important to simply have AI than it is to have well-functioning AI?
When organizations implement enterprise AI solutions, they’re choosing to put their data in someone else’s hands. But can they be sure that these solutions are safe and secure? If the worst should happen, if a breach or a failure should occur, manufacturers’ processes, efficiency and competitive edge are all on the line.
Thankfully, it’s not all doom and gloom. There’s a path forward that facilitates responsible AI adoption while allowing manufacturers to protect their business’s secret sauce.
What’s in the vault?
Enterprise AI solutions, like all software solutions, may have access to a variety of manufacturing data. Before moving forward, leaders should be aware of the information that may be shared with new technology.
• Production data including workflows, quality assurance/control processes, machinery being used and performance figures for that machinery
• Product data including inventory numbers, suppliers and partners, and product recipes
• Customer data including shipment details, order history and contact information
All this information may be shared with AI, depending on the chosen solution and its scope. In the unlikely event the solution goes haywire or is breached, the data could be compromised.
If that happens, manufacturers will need to deal with the effects on their customer relationships, trade secrets and business continuity. Any loss of data could also result in lost trust—among both internal teams and external partners. And once that trust is lost, it’s difficult to recover.
This is why manufacturers shouldn’t rush to implement AI just for the sake of it; the risks of poorly planned AI adoption outweigh even the strongest case of FOMO. But beyond the potential ramifications of a rushed or half-hearted implementation is a long-term opportunity cost. If leaders plunge into AI without thinking it through all the way, they likely won’t realize its many benefits. It’s worth taking the time to get it right.
Alleviating FOMO responsibly.
AI has the potential to transform businesses through efficiency gains, predictive analytics and overall agility—if implemented thoughtfully. But like everything else in this world, it’s not perfect; nobody can entirely avoid its inevitable bugs.
One of the best things businesses can do to minimize negative experiences is to choose their partners wisely. This means taking the time to research potential solutions and assess whether they can meet your predetermined business needs. Sign up for demo calls to see the solution in action, and come prepared with a list of questions. Any reputable AI vendor will be eagerto discuss their security and safety protocols; if you sense any reluctance to do so, that’s a red flag. The right partner will highlight their solution’s benefits while validating your caution, supporting you as you move forward with incremental, controlled implementation.
Just as you’d never hand over your Social Security number to a brand-new acquaintance, manufacturers shouldn’t dive off the deep end with their first foray into AI. A more measured approach can help businesses get the ball rolling with demonstrable benefits while keeping both feet firmly on the ground.
This “yes, and” approach works well with relatively low-risk use cases such as statistical process control (SPC). Traditionally, SPC involves taking a sample of a facility’s products to evaluate the quality and build and, from there, extrapolating to calculate an average of the entire throughput. It’s a way to gauge overall product quality and process efficacy. With AI, though, it becomes much cheaper and easier to test individual items. And the more individual items you test, the more accurate your averages will be. AI has proven incredibly reliable when it comes to testing and pattern matching, so SPC is a great place to start for manufacturers just beginning their AI journey. Once these lower-stakes implementations have proven to work, manufacturers can begin integrating AI into more serious use cases.
Regardless of the specific applications, manufacturing teams should be looped into any AI implementation before it begins. They need to know whether their roles may change, how to use the new tools and how to recognize potential warning signs of AI errors or malfunctions. Not only does this information empower teams but it increases the likelihood of being able to nip any malfunctions in the bud. Every second counts when it comes to protecting manufacturing data.
There’s plenty of AI for everyone.
Simply put, some things can only be done with AI; there are thresholds of efficiency that can only be reached with advanced technology. But this doesn’t mean manufacturers should rush—AI is about the long game.
There are innumerable AI solutions on the market. Take the time to get it right the first time, and you’ll reap the benefits for years to come. Don’t let FOMO render your facility SOL. Proceed with caution and adopt AI ASAP (as safely as possible).
