Honeywell partners with Google to introduce Gemini AI to the industrial industry.

• Honeywell has partnered with Google to combine its Gemini AI technology with Google's extensive data set.
• The industrial sector is expected to benefit from a deal that commences in 2025, with the aim of decreasing maintenance times and enhancing productivity, thereby resolving the labor crisis.

Google Gemini, a generative AI from , is being utilized by to discover insights from the industrial giant's extensive data set, which can result in decreased maintenance expenses, enhanced productivity, and opportunities for employee upskilling.

Vimal Kapur, Honeywell CEO, stated that the path to autonomy necessitates assets working harder, people working smarter, and processes operating more efficiently, as announced in a deal that will provide gen AI insights to industrial clients starting in 2025.

Honeywell is facing a generational labor shortage due to declining birth rates in the industrialized world, resulting in fewer available workers to do jobs that were popular 25 years ago. AI can solve this problem by allowing an employee with five years of experience to operate at the same level as an employee with 15 years of experience through the help of AI co-pilots, as Kapur told CNBC at the recent Evolve AI Opportunity event.

Google's AI-powered agents will assist engineers in automating tasks and help technicians resolve maintenance issues. Honeywell will soon integrate connectivity with jet engines to facilitate predictive maintenance, as stated by Kapur to CNBC.

"Suresh Venkatarayalu, Honeywell's CTO and president of Honeywell Connected Enterprise, stated in a Google blog post that the company is shifting from automation to autonomy. The objective is to provide AI agents that can assist workers in real-time, both on factory floors and in the field."

The deal with Google AI will extend beyond basic chat and forecasts, providing engineers with images, videos, text, and sensor readings.

Honeywell Forge, a digital platform that contains information from industrial designs, manuals, and real-world performance of Honeywell products, will use Google Cloud's Vertex AI and Google's large language models to create AI agents trained on this data.

According to the companies, workers will be able to ask the AI questions like "How did this unit perform last night?" or "Why is my system making this sound?"

"Industrial companies are essential to our daily lives, from the airplanes we fly to the medical devices we use and the sensors that control the air conditioning in our offices, according to Carrie Tharp, VP Strategic Industries at Google Cloud, in a blog post. However, with an entire generation of workers retiring and no one coming behind them, these companies are facing immense pressure."

Honeywell is also considering the use of Gemini Nano, an AI technology that can be used in data centers, hospitals, refineries, and warehouses, among other locations. This on-device version of the AI can provide autonomous operations by directly applying AI to scanners, sensors, and controllers.

The success of AI giants like Google in turning a capital-intensive technology into a profitable opportunity depends on getting industries across the economy to adopt gen AI. According to Honeywell data, 82% of companies in the industrial sector that consider themselves AI leaders are behind on adoption, with only 17% having fully launched initial AI plans.

The gen AI boom is fueled by large language models, and companies across the economy are hoping that their internal data becomes as valuable as these models. Clément Delangue, CEO of Hugging Face, a highly valued gen AI startup, stated at the CNBC Evolve AI Opportunity event that "data and data sets are the next frontier for AI." Hugging Face's platform, which uses an open-source approach to develop AI models, has over 200,000 public data sets shared, and the growth rate of data sets being added to the platform is faster than the growth rate of new large language models.

Delangue stated that the world will progress to a point where every company, industry, and use case will have their own unique, customized models.

Kapur believes that gen AI presents a growth opportunity for the labor-challenged industrial sector, opening up new revenue opportunities rather than being viewed primarily as a productivity tool. He is optimistic that the adoption curve will steepen quickly, with awareness high and adoption low, but an inflection point on the horizon. Kapur predicts that 2025-2026 will be a big year for the adoption of AI in the context of industrials.