Gartner’s head of AI analysis, Erick Brethenoux, was in a primary place to witness the explosion in generative AI curiosity from enterprises worldwide because the launch of ChatGPT in 2022. Actually, he stated now, for the primary time, even his 83-year-old mom lastly understands what he does for a dwelling.
“She’s been very creative, actually, in the way that she’s been using [generative AI],” he stated.
Enterprises, although, don’t all the time begin with a full understanding of generative AI. Talking with TechRepublic on the Gartner IT Symposium/Xpo in Australia in September, Brethenoux stated there’s confusion out there concerning the know-how — partially as a result of language utilized by distributors.
Widespread misunderstandings embrace what broader AI really is, as compared with generative AI, and the way AI brokers differ from generative AI fashions. That is inflicting some organisations to make errors in the best way they search to use the know-how to be used instances of their enterprise.
Confusion about several types of AI
The sudden surge of curiosity and media consideration round generative AI has led to lots of confusion, the place individuals are equating AI as a complete with generative AI capabilities. Brethenoux emphasised that AI is a much wider self-discipline, with many different essential purposes past generative AI.
“AI and generative AI are not the same thing,” he defined. “They are not interchangeable.”
As Brethenoux defined, generative AI is a follow beneath the umbrella of AI, whereas AI is a big self-discipline that has many strategies and practices, together with choice intelligence, knowledge science, and generative AI.
SEE: Why Teradata thinks generative AI initiatives danger failure with out understanding
One instance of complicated market terminology is the widespread use of the AI/ML acronym within the subject.
“I hate that acronym because it means AI equals ML. That’s not true,” Brethenoux stated. “AI techniques are rule-based systems, optimisation techniques, graph technologies, search mechanisms, ambient technology; there’s all kinds of AI techniques that have been there forever, for the last five decades.”
Generative AI utilized in solely 5% of manufacturing use instances
Brethenoux stated that, at current, generative AI accounts for under a small proportion of AI in manufacturing.
“It’s 90 per cent of the airwaves and 5 per cent of the use cases,” he defined.
“That’s basically what I see today in production. Of course, if you count the number of copilots that are out there, and you say that’s generative AI, then now the number is much larger. But until I see a return on investment on that kind of application, for me, that’s not really a use case. That’s just a feature.”
In the meantime, Brethenoux famous that different AI applied sciences proceed for use in quite a lot of use instances.
“The rest of AI? Well, that’s why airplanes arrive on time, because you use optimisation techniques to orchestrate all these crews and passengers and planes and airports and gates and everything. And good luck doing that without AI. All these systems work because AI is the background today.”
AI brokers are being confused with static AI fashions
Gartner highlighted agentic AI as a key strategic know-how pattern to look at in 2025. Nonetheless, Brethenoux stated clients should keep away from confusion over what an AI agent really is, particularly when “vendors are very good at confusing our clients” by saying that AI fashions and AI brokers are the identical.
“They are far from the same thing,” he stated. “It’s very damaging, actually, to put them in the same sentence.”
Brethenoux added that:
- An AI agent is an lively software program entity that performs duties on behalf of somebody or one thing and sometimes acts independently.
- An AI mannequin is a passive entity created by an algorithm and a set of knowledge. Whereas an agent can use fashions to carry out their activity, they aren’t the identical factor.
SEE: 9 modern use instances of AI in Australian companies in 2024
“I think the confusion comes from that mix of building a dynamic system that performs something, and building a set and a library of static assets that can be exploited, but are not doing anything in particular,” he defined. “They are just sitting there until you use them. Agents can use them, but they are not the same thing.”
AI confusion inflicting expensive errors for organisations
Brethenoux stated he had seen organisations “making big, costly mistakes” because of misunderstanding AI. Some organisations hit hassle after they apply a static AI mannequin with out having the right infrastructure in place to make it dynamic, inflicting costly delays and different points in manufacturing.
Brethenoux stated some confusion was evident on the Gartner Symposium, “I just had a discussion with a gentleman, who was telling me, ‘We want to use generative AI for this.’ And I said, ‘Well, what you’re trying to do can be solved by a graph technique in a much easier way, a much cheaper way, and a lot faster.”
AI ‘recess’ over with focus now on operationalising AI
The AI subject dove headlong right into a interval of exploring generative AI fashions after the launch of ChatGPT. This marked a swap from a earlier give attention to operationalising AI and managing the technical debt related to deploying AI methods at scale, which Brethenoux known as AI engineering.
As of January 2024, Brethenoux stated organisations had come again from this “recess” and had been making AI engineering a prime precedence once more as they attempt to successfully implement new generative AI capabilities.
“Starting in January 2024, it was sudden for us from an inquiry perspective; recess was over, and it was back into the school room,” he defined. “It was, ‘How do we make those damn things work?’, ‘How much money do they cost?’, ‘Are they really useful?’, and ‘Where do we use them?’ AI engineering is back.”
