Google Gemini, the flagship generative AI from Alphabet, is is being tapped by Honeywell to search for insights across the industrial giant’s massive data set that can lead to reduced maintenance costs, increased productivity and opportunities to upskill employees.
“The path to autonomy requires assets working harder, people working smarter and processes working more efficiently,” said Vimal Kapur, Honeywell CEO, in a statement announcing the collaboration, which will begin offering gen AI insights to industrial clients in 2025.
Kapur recently told CNBC that the biggest problems AI can solve in an industrial context start with a generational labor shortage, with declining birth rates in the industrialized world resulting in less available workers to do jobs that were popular 25 years ago. “Everyone has that problem in industrials,” he said at the recent CNBC Evolve AI Opportunity event. Kapur told CNBC that AI will allow 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.
AI-powered agents offered through Google will help automate tasks for engineers and help technicians resolve maintenance issues. Kapur had told CNBC at the recent event that Honeywell will soon be embedding connectivity within jet engines to enable predictive maintenance and reduce time needed for work in shops.
Honeywell stressed that while gen AI is already being used within the industrials sector, this partnership will take the opportunity to a higher level, going “beyond simple chat and predictions.”
“We’re moving from automation to autonomy,” said Suresh Venkatarayalu, Honeywell’s CTO and president of Honeywell Connected Enterprise, in a Google blog post about the deal. “Our goal is to equip companies with AI agents that assist workers in real time — on factory floors and in the field.”
Honeywell Forge, an Internet of Things platform that includes information from industrial designs, manuals, and real-world performance of Honeywell products, will leverage Google Cloud’s Vertex AI and Google’s large language models to build AI agents trained on this data.
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?” according to the companies.
The Google AI will offer images, videos, text and sensor readings to engineers.
“Industrial companies play a crucial role in our daily lives, whether it’s the airplanes we fly, the medical devices we use or the sensors that manage the air conditioning in our offices,” said Carrie Tharp, vice president of strategic industries at Google Cloud, in the blog post. “With an entire generation of workers retiring and — in many cases — no one coming behind them, industrial companies are under tremendous pressure.”
Honeywell said it is also exploring use of Gemini Nano, an on-device version of the AI, for operations in data centers, hospitals, refineries and warehouses, among other locations, and in particular in rural locations where internet connectivity can be an issue. Gemini Nano can provide AI directly on scanners, sensors and controllers for autonomous operations.
For the AI giants like Google, getting industries across the economy to adopt gen AI is crucial to turning a capital-intensive technology into a profitable opportunity. 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.
Companies across the economy are also hoping that their internal data becomes as valuable as the large language models like Gemini powering the gen AI boom. Clément Delangue, co-founder and CEO of Hugging Face, one of the most highly valued gen AI startups in the world, with backing from Amazon, Nvidia, and Google, said at the CNBC Evolve AI Opportunity event that “data and data sets are the next frontier for AI.” He noted that on Hugging Face’s platform, which uses an open-source approach to develop AI models, there are over 200,000 public data sets that have been shared, and the growth rate of data sets being added to the platform is faster than the growth rate of new large language models.
“The world is going to evolve to where it’s every single company, every single industry, even every single use case having their own specific customized models,” Delangue said.
Siemens and Microsoft announced a gen AI deal for the industrial sector late last year, which included an AI copilot for use across industries.
Kapur views gen AI as a growth opportunity for the labor-challenged industrial sector which will open up new revenue opportunities rather than as a productivity tool first and foremost, and he is bullish on the adoption curve steepening quickly. “Awareness is high, adoption is low, but there will be an inflection point,” he said at the recent CNBC AI event. “I do believe 2025-2026 will be a big year for adoption of AI in the context of industrials.”