There is a lot of information about Generative AI (GenAI) out there. It can be a lot to take in. To help, we’ve boiled down generative AI for Industry to four key points. We will dive into more details on each of these points in our book about GenAI , but if you only read one part of this book, let it be this:
1. LLMs + knowledge graph = Trusted, explainable generative AI for industry
This is the simple formula to apply generative AI for industry. Your asset performance management is made intelligent and efficient by combining large language models (LLMs) with a deterministic industrial knowledge graph containing your operations data.
2. Generative AI for industry needs to be safe, secure and hallucination-free
And with the previous formula, it is. You need a complete, trustworthy digital representation of your industrial reality (industrial knowledge graph) for LLMs to understand your operations and provide deterministic responses to even the most complex questions.
3. To apply generative AI in industrial environments, the ability to prompt LLMs with your operational context is everything
This means having a deterministic industrial knowledge graph of your operations, including real-time data. You need a solution that delivers contextualized data-as-a-service with data contextualization pipelines designed for fast, continuous knowledge graph population.
4. While generative AI itself is undeniably transformative, its business value is in its application to the real-world needs of field engineers
Generative AI can already be applied today across field productivity, maintenance planning and robotic automation, but only with a platform that delivers essential AI features that enable simple access to complex industrial data for engineers, subject matter experts, data scientists and more To learn more about making generative AI work for industry, download The Definitive Guide to Generative AI for Industry , a free resource authored by AI innovators at Cognite.