Gartner predicts AI agents will transform work, but disillusionment is growing

Very quickly, the topic of AI agents has moved from ambiguous concepts to reality. Enterprises will soon be able to deploy fleets of AI workers to automate and supplement — and yes, in some cases supplant — human talent. 

“Autonomous agents are one of the hottest topics and perhaps one of the most hyped topics in gen AI today,” Gartner distinguished VP analyst Arun Chandrasekaran said at the Gartner Symposium/Xpo this past week. 

However, while autonomous agents are trending on the consulting firm’s new generative AI hype cycle, he emphasized that “we’re in the super super early stage of agents. It’s one of the key research goals of AI companies and research labs in the long run.” 

Top trends in Gartner’s AI Hype Cycle for gen AI

Based on Gartner’s 2024 Hype Cycle for Generative AI, four key trends are emerging around gen AI — autonomous agents chief among them. Today’s conversational agents are advanced and versatile, but are “very passive systems” that need constant prompting and human intervention, Chandrasekaran noted. Agentic AI, by contrast, will only need high-level instruction that they can break out into a series of execution steps. 

“For autonomous agents to flourish, models have to significantly evolve,” said Chandrasekaran. They need reasoning, memory and “the ability to remember and contextualize things.”

Another key trend is multimodality, said Chandrasekaran. Many models began with text, and have since expanded into code, images (as both input and output) and video. A challenge in this is that “by the very aspect of getting multimodal, they’re also getting larger,” said Chandrasekaran. 

Open-source AI is also on the rise. Chandrasekaran pointed out that the market has so far been dominated by closed-source models, but open source provides customization and deployment flexibility — models can run in the cloud, on-prem, at the edge or on mobile devices. 

Finally, edge AI is coming to the fore. Much smaller models — between 1B to 10B parameters — will be used for resource-constrained environments. These can run on PCs or mobile devices, providing for “acceptable and reasonable accuracy,” said Chandrasekaran. 

Models are “slimming down and extending from the cloud into other environments,” he said. 

Heading for the trough

At the same time, some enterprise leaders say AI hasn’t lived up to the hype. Gen AI is beginning to slide into the trough of disillusionment (when technology fails to meet expectations), said Chandrasekaran. But this is “inevitable in the near term.”

There are a few fundamental reasons for this, he explained. First, VCs have funded “an enormous amount of startups” — but they have still grossly underestimated the amount of money startups need to be successful. Also, many startups have “very flimsy competitive moats,” essentially serving as a wrapper on top of a model that doesn’t offer much differentiation.

Also, “the fight for talent is real” — consider the acqui-hiring models — and enterprises underestimate the amount of change management. Buyers are also increasingly raising questions about business value (and how to track it).

There are also concerns about hallucination and explainability, and there’s more to be done to make models more reliable and predictable. “We are not living in a technology bubble today,” said Chandrasekaran. “The technologies are sufficiently advancing. But they’re not advancing fast enough to keep up with the lofty expectations enterprise leaders have today.”

Not surprisingly, the cost of building and using AI is another significant hurdle. In a survey by Gartner, more than 90% of CIOS said that managing cost limits their ability to get value from AI. For instance, data preparation and inferencing costs are often greatly underestimated, explained Hung LeHong, a distinguished VP analyst at Gartner.

Also, software vendors are raising their prices by up to 30% because AI is increasingly embedded into their product pipelines. “It’s not just the cost of AI, it’s the cost of applications they’re already running in their business,” said LeHong.

Core AI use cases

Still, enterprise leaders understand how instrumental AI will be going forward. Three-quarters of CEOs surveyed by Gartner say AI is the technology that will be most impactful to their industry, a significant leap from 21% just in 2023, LeHong pointed out. 

That percentage has been “going up and up and up every year,” he said. 

Right now, the focus is on internal customer service functions where humans are “still in the driver’s seat,” Chandrasekaran pointed out. “We’re not seeing a lot of customer-facing use cases yet with gen AI.” 

LeHong pointed out that a significant amount of enterprise-gen AI initiatives are focused on augmenting employees to increase productivity. “They want to use gen AI at individual employee level.” 

Chandrasekaran pointed to three business functions that stand out in adoption: IT, security and marketing. In IT, some uses for AI include code generation, analysis and documentation. In security, the technology can be used to augment SOCs when it comes to areas such as forecasting, incident and threat management and root cause analysis. 

In marketing, meanwhile, AI can be used to provide sentiment analysis based on social media posts and to create more personalized content. “I think marketing and gen AI are made for each other,” said Chandrasekaran. “These models are quite creative.” 

He pointed to some common use cases across these business functions: content creation and augmentation; data summarization and insights; process and workflow automation; forecasting and scenario planning; customer assistance; and software coding and co-pilots.  

Also, enterprises want the ability to query and retrieve from their own data sources. “Enterprise search is an area where AI is going to have a significant impact,” said Chandrasekaran. “Everyone wants their own ChatGPT.” 

AI is moving fast

Additionally, Gartner forecasts that: 

• By 2025, 30% of enterprises will have implemented an AI-augmented and testing strategy, up from 5% in 2021. 
• By 2026, more than 100 million humans will engage with robo or synthetic virtual colleagues and nearly 80% of prompting will be semi-automated. “Models are going to get increasingly better at parsing context,” said Chandrasekaran. 
• By 2027, more than 50% of enterprises will have implemented a responsible AI governance program, and the number of companies using open-source AI will increase tenfold. 

With AI now “coming from everywhere,” enterprises are also looking to put specific leaders in charge of it, LeHong explained: Right now, 60% of CIOs are tasked with leading AI strategies. Whereas before gen AI, data scientists were “the masters of that domain,” said LeHong. 

Ultimately, “most of our clients are still throwing things to see if they stick to the wall,” he said. “Now they know which wall to throw it at. Before they had four walls and maybe a ceiling to throw it at, now they have a marketing wall, an IT wall, a security wall.”