Gartner’s head of AI research, Erick Brethenoux, was in a prime position to witness the explosion in generative AI interest from enterprises worldwide given that the launch of ChatGPT in 2022. In fact, he said now, for the first time, even his 83-year-old mother finally understands what he does for a living.
“She’s been really imaginative, actually, in the way that she’s been using [generative AI],” he stated.
Enterprises, though, do not constantly begin with a complete understanding of generative AI. Speaking to TechRepublic at the Gartner IT Symposium/Xpo in Australia in September, Brethenoux said there is confusion in the market about the technology– partially due to the language utilized by suppliers.
Common misunderstandings include what wider AI really is, in comparison with generative AI, and how AI agents differ from generative AI designs. This is causing some organisations to make mistakes in the method they seek to apply the innovation for usage cases in their service.
Erick Brethenoux, chief of AI
research study, Gartner Confusion about different types of AI
The sudden rise of interest and media attention around generative AI has actually led to a lot of confusion, where individuals are equating AI as a whole with generative AI capabilities. Brethenoux emphasised that AI is a much wider discipline, with many other crucial applications beyond generative AI.
“AI and generative AI are not the same thing,” he discussed. “They are not interchangeable.”
As Brethenoux discussed, generative AI is a practice under the umbrella of AI, whereas AI is a large discipline that has many techniques and practices, including decision intelligence, information science, and generative AI.
SEE: Why Teradata believes generative AI tasks run the risk of failure without comprehending
One example of confusing market terms is the prevalent usage of the AI/ML acronym in the field.
“I hate that acronym because it means AI equates to ML. That’s not true,” Brethenoux said. “AI strategies are rule-based systems, optimisation techniques, graph technologies, search systems, ambient innovation; there’s all sort of AI strategies that have been there permanently, for the last 5 decades.”
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Generative AI used in just 5% of production use cases
Brethenoux said that, at present, generative AI represent only a little proportion of AI in production.
“It’s 90 percent of the airwaves and 5 per cent of the use cases,” he described.
“That’s generally what I see today in production. Of course, if you count the variety of copilots that are out there, and you state that’s generative AI, then now the number is much bigger. But till I see a roi on that kind of application, for me, that’s not really an usage case. That’s simply a function.”
On the other hand, Brethenoux noted that other AI technologies continue to be utilized in a range of use cases.
“The rest of AI? Well, that’s why planes get here on time, since you use optimisation methods to orchestrate all these teams and travelers and planes and airports and gates and everything. And all the best doing that without AI. All these systems work because AI is the background today.”
AI agents are being puzzled with static AI designs
Gartner highlighted agentic AI as a key tactical technology trend to view in 2025. Nevertheless, Brethenoux said consumers should avoid confusion over what an AI representative really is, especially when “suppliers are great at puzzling our customers” by stating that AI designs and AI agents are the same.
“They are far from the very same thing,” he said. “It’s really damaging, in fact, to put them in the same sentence.”
Brethenoux included that:
- An AI agent is an active software application entity that carries out tasks on behalf of someone or something and frequently acts individually.
- An AI design is a passive entity developed by an algorithm and a set of information. While an agent can utilize models to perform their job, they are not the exact same thing.
SEE: 9 ingenious usage cases of AI in Australian companies in 2024
“I believe the confusion originates from that mix of building a dynamic system that carries out something, and building a set and a library of static possessions that can be exploited, but are refraining from doing anything in particular,” he described. “They are just sitting there up until you utilize them. Representatives can use them, however they are not the exact same thing.”
AI confusion causing costly errors for organisations
Brethenoux stated he had actually seen organisations “making big, pricey errors” as an outcome of misunderstanding AI. Some organisations strike problem when they use a fixed AI model without having the right infrastructure in location to make it dynamic, causing pricey hold-ups and other issues in production.
Brethenoux stated some confusion appeared at the Gartner Seminar, “I simply had a conversation with a gentleman, who was informing me, ‘We wish to use generative AI for this.’ And I said, ‘Well, what you’re attempting to do can be solved by a chart method in a lot easier method, a more affordable method, and a lot quicker.”
AI ‘recess’ over with focus now on operationalising AI
The AI field dove headlong into a duration of exploring generative AI models after the launch of ChatGPT. This marked a switch from a previous concentrate on operationalising AI and managing the technical debt associated with releasing AI systems at scale, which Brethenoux called AI engineering.
Since January 2024, Brethenoux stated organisations had returned from this “recess” and were making AI engineering a top concern once again as they try to efficiently implement new generative AI abilities.
“Starting in January 2024, it was sudden for us from a query viewpoint; recess was over, and it was back into the school room,” he explained. “It was, ‘How do we make those damn things work?’, ‘Just how much cash do they cost?’, ‘Are they actually beneficial?’, and ‘Where do we utilize them?’ AI engineering is back.”
