Vimal Kapur, CEO of Honeywell, one of the world’s largest industrial conglomerates, sees AI differently than most. AI is not about threatened office workers. “Every five years there’s a trend that makes your skills obsolete,” Kapur said at the recent CNBC Evolution: The AI ​​Opportunity Summit in New York. “The loss of white-collar workers is an ongoing evolution.” AI, he said, is not about the cool features it can offer consumers, who “get excited about writing resumes or restaurant recommendations.” The biggest problem AI can solve for Honeywell is first and foremost the labor shortage it and its client companies face. From pilots to technicians, falling birth rates in industrialized countries have led to fewer and fewer jobs that were popular 25 years ago. “Everyone in industry has this problem,” he said. Honeywell’s AI opportunity is about creating a new pool of labor that can learn and work alongside AI and accumulate and deploy institutional knowledge faster. Traditionally, he said, humans needed 15 years of experience to handle complex roles, but someone with five years of experience working with two AI co-pilots can reach the same level.Labor isn’t the only issue with AI deployment. Kapur noted that Honeywell plans to roll out connectivity within jet engines in the coming months that will allow the company to proactively monitor engine performance to spot maintenance issues before they return to the shop. The same is true for smoke detectors, another staple in Honeywell’s product line, which will be identified as needing repair or replacement earlier than before.

But labor issues remain a top concern for the Honeywell CEO, who added that it makes him see AI as a revenue opportunity rather than a way to increase productivity. “Skills shortage is at the heart of our problem,” Kapur said. “It’s the constraint to increasing revenue. The biggest constraint to revenue is the lack of skilled labor.”

Most companies are just beginning to find a return on their AI investments, with levels of return far beyond OpenAI’s underlying large language models and Nvidia’s chipmaking.

Raw data, collected directly from the source without being filtered through intermediaries, will be the key to AI success for many companies, said Jake Loosararian, CEO of Gecko Robotics. Gecko Robotics, a company that works in energy, manufacturing and defense to optimize maintenance, has AI-powered inspection robots analyzing equipment as large as aircraft carriers to identify structural defects.

“The future belongs to companies with ‘best-in-class’ datasets,” he told Jon Fortt, host of CNBC’s “Closing Bell Overtime,” at the Evolve: AI Opportunity event.

Several executives stressed the importance of moving beyond the current focus on large language models, including one at the forefront of the LLM: Clément Delangue, co-founder and CEO of Hugging Face, one of the world’s most highly valued AI startups, backed by Amazon, Nvidia and Google. He expressed similar sentiments to Loosararian at the CNBC event. Delangue

“Data and datasets are the next frontier in AI,” he said, noting that more than 200,000 public datasets have been shared on Hugging Face, a platform that takes an open-source approach to developing AI models, and that datasets added to the platform are growing faster than new large language models.

“The world will evolve to a world where every company, every industry and even every use case has its own specific custom model,” Delangue said. “Eventually, every company will build their own model, just like they have their own code base and build their own software product… Ultimately, this will help them differentiate themselves.”

If companies gain the most from AI customized to their use cases, a view is gaining momentum in AI regulatory discussions to shift the focus from large language models to industry-specific monitoring. As these use cases proliferate, executives need to make sure they are communicated to the board.

“Board members really need to understand what use cases the company might be using so they can get a report from the people who best understand the risks the company might face,” Katherine Forrest, a former federal judge, partner at Paul, Weiss, Rifkind, Wharton & Garrison and an expert in AI law, said at the CNBC AI Summit.

Now, she said, is the time to ask: “What are the risks? Do we have the right people to manage those risks? Have we had any incidents? They should know if those risks actually occurred.”

While there is debate about how quickly AI opportunities will be realized, Honeywell’s Kapur is optimistic about a rapid steepening of the adoption curve. “Awareness is high and adoption is low, but there will be an inflection point,” he said. “I do believe 2025-2026 will be a big year for AI adoption in the industrial sector.”
Related product recommendations:
UAC326AEV1
IC693CHS392
IC698CHS009
IC693CHS392J
531X300CCHBDM3
IS200ERGTH1AAA
IS2020ISUCG3A
IC698CRE030
FC-USI-0002 V1.0
PFRL101C 2.0KN 3BSE002964R002
3HAC039109-002
IS200TTURH1B
8104-AO-IP-03 
IC693CPU341R
UAC326AEV1  UA C326 AE V1 HIEE401481R0001
PC104-DPIO DRL-DPM-BKF
DS3800HIOD
more……