Cognite Releases Definitive Guide to Generative AI for Industry
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From Cognite
Cognite, a globally recognized leader in industrial software, released "The Definitive Guide To Generative AI for Industry," a manual for companies to accelerate AI innovation and reduce time to value.
Generative AI is the most transformative technology to hit the market in ten years, and industrial organizations across the energy and manufacturing sectors are scrambling to keep pace with the accelerating, data-driven shifts that modern organizations must now navigate.
"The Definitive Guide to Generative AI for Industry" explains and defines the technological requirements necessary to make AI work for industry. The guide offers practical advice on successful AI adoption and scaling—including specific use case examples—and provides tools for digital leaders to scope and plan their digital journey and define and measure success in terms the enterprise will understand.
"Safe, secure, hallucination-free generative AI is critical to paving the road to sustainable and profitable global energy supply and manufacturing excellence. However, industrial organizations can only leverage generative AI successfully by solving the industrial data problem first," said Girish Rishi, CEO at Cognite. "This is the moment Cognite was built for, and The Definitive Guide to Generative AI for Industry provides a comprehensive how-to for transformation leaders looking to drive actual bottom-line impact.”
4 Takeaways About Generative AI for Industry
The Cognite guide highlighted four things to know about industrial generative AI:
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.
The guide goes on to cover what generative AI is, best practices for using it in industrial settings, business value of generative AI, use case examples, and more.
Read the full Cognite guide here.
Want to check out the latest tech, use cases and best practices for deploying AI with industrial/energy assets? Join us at energAIze: Energy AI Forum @ Industrial Immersive Week this March 5-7 in Houston, TX.
