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Navigating the Hype: AI and the Electronics Industry Supply Chain

Oct. 11, 2023
The future of AI is promising, but the path of integration demands careful decision making and a drive toward creating value within the supply chain.

Having worked in the software industry in many different roles and then co-founding a company providing sales, procurement and data automation solutions in the electronics industry, I frequently encounter questions about artificial intelligence (AI) and its impact on the industry’s supply chain. While writing predictive articles is always a little unnerving, I’d like to offer practical advice for navigating this era of accelerated technological advancement with a clear and straightforward perspective. My goal is to offer clarity and assistance to enhance understanding, facilitating sound decision-making.

The Current Landscape

AI, with its capabilities for data processing, prediction and automation, is generating undeniable excitement. Industry leaders are captivated by its potential to revolutionize sales and procurement processes by optimizing demand forecasting, automating routine tasks, enhancing customer service and providing deep insights for more informed decision-making, to name a few applications.

A Healthy Skepticism

Despite the enthusiasm for AI, my position remains one of skepticism, especially regarding its short-term impact on our industry.

You have probably noticed that AI is excellent at language processing and natural language processing (language two humans might use to interact, for example). Language processing is the foundation of modern computing: computers are, in fact, built on languages from the hardware up. While these languages aren’t spoken (though there are some great jokes in those languages that I’ll spare you), they still have all of the core characteristics of a spoken language: parts of speech, verbs, nouns, sentence structure, etc. These elements are called grammar, and all computation is built on top of grammar.

It is, then, not a surprise that combining data processing with natural language processing could produce a solid AI-like linguistic experience. But make no mistake: this is still very much a finite computer program. It is limited to only what it can be programmed to do, which is to assemble data and understand or create language. 

Because so much of human interaction is founded in language, the combination of these capabilities makes AI feel very real and very alive. But this AI lives in a very controlled environment. While it has access to datasets that allow it to accumulate information and assemble linguistic responses very quickly, outside of those pre-defined functions, it has the same problems as any other computational system: integrations. Ask it to unlock your front door, start your car or even the simplest of digital tasks like sending an email on your behalf, and you’ll begin to notice very quickly how isolated and limited AI really is, even with robust and excellent integration interfaces in place.

Challenges and Considerations

Having spent nearly three decades working on system integrations, I expect that AI (as a whole) will remain rooted at this spot, with nominal advances for years to come, with data and systems integrations as the major impediment to growth. For background, think about the myriad systems you interact with on a daily basis: alarms, games and messaging on your phone; banking, bill-pay, travel arrangements and shopping on your phone or computer; even entertainment on your computer, console or TV.

At work, you interact with a quote system, a website, an ERP/MRP, a time-tracking system, a ticketing system and hundreds of other systems that help your company run.  For AI to be able to interact with these things on your behalf, it must be properly integrated in the form of an Application Programming Interface (API)—a digital connection between systems—and that means a software or data integration initiative. Does that sound familiar? I bet it does—it’s the root of all business computing problems, siloed systems and the flow of data between them. Unfortunately, the data stores used by those systems (i.e., databases, EDI, XML, etc.) are not so neatly defined or nearly as finite as natural language, and integrating them will be exponentially more difficult, more specific and more expensive than the resources expended to build AI to this point.

 Beyond the Hype: Practical Recommendations

All of that said, AI is a tool like any other. It’s not a solution to all problems, but it’s a fantastic resource that allows companies to do more with less. Given all of these factors, my advice to our customers, partners and industry decision-makers in general is grounded in pragmatism.

Take a Deep Look

Don’t take my word for it and don’t take anyone else’s word for it. Go try to use it for something real and tangible. Exercise it beyond an exchange of words and see what it can do. My guess is that you’ll be impressed in some areas and disappointed in others, but ultimately you will get a feel for its capabilities and how it might be applied in your company.

Focus on Tangible Benefits

Keep the emphasis on concrete, measurable outcomes. If you are able to find a spot where generative AI can be of value (i.e., assembling marketing language or providing an excellent human-language interface to specific customer-facing functions), then approach these problems specifically. Currently, a company-wide AI approach is not necessary (or even affordable). Find small, discrete use cases and regularly evaluate impact and ROI to ensure they are delivering value.

Consider Intellectual Property and Trade Secrets

It may seem like the usage of public AI is free, but it surely is not. You are paying for your usage by means of the data you provide to the AI. Information shared via an AI prompt should be considered by your team to be fully exposed to the public, and information considered confidential by your organization should not be joined or provided (unless that AI has been properly secured and privately deployed with those terms of service in place, and if that sounds like a big project, that’s because it is).

The lure of AI and generative AI is compelling, and its long-term impact on the electronics industry supply chain may well be substantial. However, as we navigate this journey, let’s proceed with thoughtful consideration, realistic expectations and a commitment to strategic, informed integration. The future is promising, but the path demands careful, conscious decision making and a drive toward creating value within the supply chain.

About the Author

Tony Powell | Co-Foudner, Chief Technology Officer

Tony Powell co-founded Orbweaver Sourcing, LLC in 2012 and is the firm’s chief technology officer, where he is responsible for driving business strategy and all technological aspects of the company. Prior to founding Orbweaver, Powell worked for nearly two decades in various software engineering positions. In 2013, he worked with four other engineers to pass the patent, “protocol agnostic dynamic messaging platform and a system and a method thereof.” Tony is a graduate of Muhlenberg College and received his MBA from Lehigh University. He can be reached at [email protected].

Based in Bethlehem, Pa., Orbweaver is the first and only provider of complete, real-time supply chain integration solutions for the electronics manufacturing industry. 

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