Advisor to Manufacturing Executives | Future of Industrial Work, EHS, and Knowledge Management
The rapid advancement of #AI, particularly generative AI models like ChatGPT, has sparked intense debate around its potential impact on Advanced Industrial Analytics #AIA and manufacturing operations. How should manufacturing leaders view AI? * Disclaimer: I used an #AI tool to provide a summary of our discussions over the last two weeks and a draft LinkedIn post for me 😅 The LNS Research team has had many spirited internal discussions on the topic of AI within manufacturing. 🔍 Some view AI as simply the next evolution of AIA 🔎 Others see it as a transformative force that will fundamentally change how we interact with and build analytics tools altogether 👉🏼 In reality, the truth likely lies somewhere in between Instead of getting caught up in definitional debates, or whatnot, manufacturing leaders should focus on how AI can be leveraged as a powerful tool for solving key business challenges. Here's what we recommend to manufacturing leaders considering entering, pausing, or exiting the AI movement: ✔ Accept that AI is not a silver bullet, but a complementary capability to AIA. ✔ Take a holistic view and establish clear governance around AI deployment from the start to accelerate decision making while implementing AI thoughtfully. ✔ Follow in the footsteps of IX Leaders and explore high-value use cases where AI can drive tangible impact, such as predictive maintenance, supply chain optimization, and quality management. ✔ Integrating AI within existing tools, such as Connected Frontline Workforce #CFW applications, to dramatically improve workforce decision-making and ROI. As the AI toolbox expands with new techniques and technology, it will be critical for Global Heads of Operations to be aware of the latest research insights into the use of AI across manufacturing. 🔔 Follow the LNS Research team as we identify the best practices driving real value as AI's role in industrial operations evolves. Members can access Niels Erik Andersen's latest Research Spotlight in the LNS Research library, or by clicking here 📖 https://lnkd.in/eaXDSrAg #IndustrialTransformation #GenerativeAI #FrontlineWorkforce #TheIXEvent
Rick Bullotta
4mo
I wish we could all stop conflating Gen AI and AI. There is sooooooo much more to AI and machine learning/teaching than this stuff.
Incubating and Scaling New Products with AI and IoT to Drive Business Growth
4moAgreed with Rick. We will all benefit from some "translation" of what industrial use cases each of AI techniques is good at and not good at. I summarized below a while ago, and specificity on LLMs, it become more clear to me that the biggest two value proposition of LLMs in industrial use cases are 1) natural language interface and 2) turning unstructured data into structured data, and even in the use cases where LLMs is good fit, many times they need to work alongside with other AI techniques, like knowledge discovery use cases needs graph, RCA use cases needs causal model.