Artificial Intelligence has been enjoying another media resurgence since ChatGPT hit the scene earlier this year. ChatGPT is seemingly part of everyone’s vocabulary these days, and its popularity isn’t confined to our consumer lives. Countless companies have been introducing ChatGPT into their products and product lifecycles. Microsoft uses the same large language models (LLMs) that power the ChatGPT chatbot to power its Bing search engine and UK-based energy supplier Octopus Energy has built ChatGPT into its customer service channels, with it now responsible for handling 44 percent of customer inquiries.

Generative AI engines, such as GPT-4 that powers ChatGPT, hold enormous potential for insight and innovation around how products are designed and used by customers. But it also raises a few risks and legal questions, venturing into grey areas that have yet to be addressed.

Opportunities for Manufacturers

ChatGPT is an advanced language model that uses artificial intelligence (AI) and natural language processing (NLP) to provide conversational responses to users. It’s trained on massive amounts of text data, enabling it to understand and generate human-like language.

For manufacturers, this can be a golden opportunity. Manufacturing companies generate their own specific data from customer usage and equipment performance, and it’s all unique to them. Until now, there have been limits on how companies can monetize and apply this data for development and innovation. ChatGPT opens the door to accelerate and extend the application of this data. It has the potential to enable design engineers to gain much deeper insights into how the products they design are being used, how they perform over time and what trends arise, ultimately enabling them to design better products going forward.

ChatGPT can expose manufacturers to additional data so they can uncover, analyze, and harness opportunities they wouldn’t have otherwise spotted. Say for example, customers are routinely researching what eBike will fit into the back of a Toyota 4Runner. Toyota could capitalize on this and factor it into the future design spec of its next model. Think of ChatGPT as an incredibly efficient assistant. It shouldn’t have the final decision, but it can gather all sorts of information so that you and your designers can make a more informed one.

ChatGPT could also reshape service operations as it matures and moves deeper into day-to-day service and maintenance interactions. Service technicians can use it to ask niche questions they may not know the answer to without having to refer to a manufacturer’s product manual for a complex piece of equipment operating in a specific context. Likewise, language comprehension in ChatGPT means service technicians can also provide basic information and use ChatGPT to generate a report, saving time and minimizing the drudge work. Such content generation could eventually be used for customer-facing content, as well as educational, upselling, and personalized cross-selling content.

It also has the potential to triage service maintenance calls and provide more self-service opportunities, helping with costs, unnecessary truck rolls, and sustainability. Furthermore,  as older more experienced technicians retire, ChatGPT can fill the knowledge void, becoming a subject matter expert, and providing knowledge guidance either onsite or remotely. With a lack of incoming technicians and a growing skills gap, ChatGPT will be able to help alleviate some of the challenges manufacturers face since it can take years to not only understand how to operate the machines but to also deal with the technicalities of complex modern equipment.

Risks to the Business

Of course, no tool is perfect. ChatGPT is limited by the quality of the data it’s trained on. While the model is trained on a vast amount of text data, this data is not always representative of real-world language use. The training data may be biased towards certain topics or demographics, leading to limitations in the model’s ability to understand other contexts. Additionally, the data may contain errors or inconsistencies that can impact the model’s performance.

ChatGPT is also limited by the complexity of language. While the model can generate responses to a wide range of questions and prompts, it’s not always able to understand the nuances of language use. This can lead to misunderstandings or incomplete responses. The software may also struggle to understand idiomatic expressions or sarcasm, leading to responses that are not appropriate or relevant.

The newness also carries an element of risk. While ChatGPT version 4 is better than version 3.5, it can still confidently give wrong answers. That’s no different from humans. We do this all the time. But unlike humans, its ability and speed to consume and learn is compounding at a rate faster and deeper than anything a human brain can do. In this sense, risks around ignorance will dramatically decline.

The other potential grey area is that there’s very little legal precedent for the content it is generating. Since ChatGPT pulls content from lots of disparate places and sources, do you 100% own that content? If you use the content and monetize it in some way, who else has a legal claim to it? In time, as generative AI becomes more pervasive and ChatGPT moves into the enterprise, we’ll start to see legal challenges, precedents and clarity around protection on how ChatGPT creates code, content and ideas.

What began as a slow creep of AI automation to make things faster is now rapidly accelerating into a mass tipping point of generative intelligence. It’s just a matter of time before AI technology such as ChatGPT is woven into daily business tools, processes, operations and field support. While the bones of your business may not change, the intelligence, design efficiency, product outcomes, and innovation will almost certainly improve.

Read more about ChatGPT: 5 Ways ChatGPT AI Can Transform Field Service Operations

ABOUT Joseph June

Avatar photoAs the SVP of Product Management, Joseph is responsible for the strategy and roadmap of the mobile product portfolio at ServiceMax. Joseph is an experienced product leader with more than 19 years of extensive experience in enterprise mobile applications, mobile cloud services, field service, BPM (business process management) and CRM platforms. Prior to ServiceMax, Joseph was the Senior Director of Product Management at Pegasystems where he was responsible for leading and launching the Pega Application Mobility Platform and Pega Field Service. Joseph also held product leadership and management roles at Antenna Software, BEA Systems, Oracle and IBM. Joseph holds a Bachelor of Science degree from The University of Chicago.