NEW YORK — OpenAI’s release on Thursday of GPT 5.2, the latest version of its flagship generative AI model, just a month after GPT 5.1, appears to back up the contention of many in the tech world that AI technology is advancing at a faster pace than its adoption.
Over the last three years, since the popularization of generative AI following OpenAI’s release of ChatGPT, the market has shifted from hype about AI models to hype about agentic AI. At times it seems that enterprises are barely taking in one technology before vendors such as Microsoft, AWS, Google, and OpenAI release their next product.
Intensity in the AI Business
Sometimes, the multitude of AI models and agentic AI tools can feel overwhelming.
“There’s a lot of hype and noise, and I don’t think people like [OpenAI CEO Sam Altman] are making it easier,” said Peter Guagenti, CEO of agentic AI vendor EverWorker, during an interview with AI Business at Informa’s AI Summit conference.
EverWorker’s focus is on helping businesses create, customize and deploy agentic AI workers without using code.
“They are talking in platitudes and all these other things,” Guagenti continued. “It’s really scary and overwhelming.”
Due to the noise and promotional energy surrounding new AI releases, enterprises may feel like they are behind, while to others, the technology may seem advanced and tantalizing.
It certainly felt compelling for Naomi Taddesse, an assistant architect at General Motors.
“There’s a lot of AI everywhere,” Taddesse said in an interview, referring to the dozens of AI vendors showcasing their tools on the AI Summit floor. “There is a lot of AI integrated within manufacturing, within automative … things that will help or aid you do things a lot faster. It’s very intriguing.”
“The whole taking away jobs, all that stuff … I don’t necessarily love that,” she continued. “But I can see that everyone is going toward AI, and everyone is trying to move ahead.”
Adoption Starts With the Use Case
However, in moving toward AI, enterprises need to be intentional, instead of moving full speed ahead, Guagenti, of EverWorker, said.
“What I advise is to turn the volume down,” he said. “Start small, stay focused. Where in your business do you have pain today? Where in your business can’t you hire today? Where in your business do you have excess expenses that you know are not adding value?”
“Start with these simple, straightforward use cases where you have a high reward and a low risk,” he continued.
Data is the Driver
In addition to starting with simple applications, enterprises should focus on their data.
“Without data observability, I do not believe there is ever trust between AI applications,” said David Swan, a sales engineer at Retool, developer of a low-code software and AI development platform, during a panel at the conference on Dec. 10.
Data observability is also critical because it helps ensure that the use cases enterprises are applying AI technology to are grounded correctly.
“Context really matters for AI,” said Anuradha Maradapu, manager of data engineering and analytics, data governance at American Airlines, in an interview. “Pointing the data to the right use case really matters.”
She added that not only should enterprises focus on data, but they should also ensure that their data is ready. One way to determine if the data is ready is to verify that it fits the application. Indeed, a certain lack of consideration for ensuring that data is the right data for the job and has been properly prepared and governed is reflected in the sometimes hurried pace with which many businesses are adopting AI technology.
While many say that adoption is slow, for Maradapu, it’s somewhat the opposite.
“We are moving so fast without considering all the underlying governing processes and foundations put in place,” she said. “So that it’s actually becoming chaotic more than slow.”
Making it Accessible
One way to make things less disorderly is for enterprises to give everyone within their organization access to AI tools, Guagenti said.
“There are all these bespoke agents that are departmental or use case focused,” he said.
One organization that says it’s working on making the tools less confusing for workers is NBC Universal.
In a fireside chat at the conference on Dec. 10, Chris Crayner, EVP, chief digital and technology officer at NBC Universal Destinations and Experience, discussed how enterprises can use generative and agentic AI to address challenging points within the organization.
“It doesn’t matter if you’re a front-line attraction lead for us, or you’re an AI data scientist at the back,” Crayner said. “It doesn’t really matter. There’s friction in everybody’s job.”
“We’re trying to demystify it and also be transparent that this is a tool that we’re going to use, but here’s the lens that we’re trying to use it through,” he continued.
