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Companies are optimistic about the potential of generative AI, and plan to invest more resources into initiatives and embark on enterprise-wide changes around its adoption.

Less than two years after the release of ChatGPT from OpenAIGenerative AI is already the most widespread use of artificial intelligence, according to a Gartner study.

“We really need to help our teams, our business colleagues, our leaders, who are sort of swept up in the hype, understand where this is actually helpful and where other things might be better,” said Rita Sallam, Distinguished VP Analyst and Gartner Fellow on the Data and Analytics teamduring the firm’s IT Symposium/XPo last week. “We really need to keep the organization on its feet as to when it makes sense. »

CIOs need to clearly communicate and explain when technology is an effective solution and when it would be better to try other options, such as knowledge graphs or reinforcement learning. After all, organizations rely on the expertise of technology leaders to avoid costly missteps.

Failure of technology projects can damage an organization’s reputation, customer relationships, and consequences. the essential. Organizations that deployed AI in 2023 spent between $300,000 and $2.9 million is in the proof-of-concept phase, and many generative AI experiments never move beyond the nascent stage, according to a Gartner study.

Sallam said the generative system is generally not the best tool for businesses to:

• Plan and optimize
• Predict and predict
• Make critical decisions
• Run standalone systems

Businesses are full of potential use cases, but it’s critical to choose those that will provide the most value and the least risk.

Weaknesses in generative AI — including a lack of reliability, a tendency to hallucinate and limited reasoning — can derail many use case ideas, Sallam said. Technology leaders can look to other forms of artificial intelligence, including predictive machine learning, rules-based systems, and other optimization techniques, to achieve better results.

Large language models struggle to perform exact calculations, making it difficult to use generative AI for use cases like marketing allocation or route optimization, according to Sallam. Instead, CIOs can use knowledge graphs and composite AI, defined as a combination of AI techniques. The safeguards needed to ensure responsible and secure use of technology can hinder experiences like automated trading and agents. Reinforcement learning would be a better solution, Sallam said.

Wrong place, wrong task

Generative AI thrives in content generation, knowledge discovery, and conversational user interfaces. This has given rise to countless solutions targeting text and coding, question-and-answer systems, knowledge management, and virtual assistants.

Businesses were won over. Just 6% organizations have Delayed Generative AI investments, according to Capgemini investigation published in July.

“Don’t get me wrong, I think the potential is huge. » Sallam said. But the hype has caused executives to focus too much, and potentially overinvest, in generative AI to the detriment of the business, according to Sallam.

“This hype is dangerous,” Sallam said. “Organizations that focus solely on generative AI may risk failure of their AI projects and miss many important opportunities. So we want to make sure that the hype around generative AI doesn’t cut off the oxygen in the room.

Vendors have recently focused on AI-based agents with autonomous capabilities, for example. Soft And SAP has announced agent capabilities in existing solutions in recent weeks. Salesforce has moved its Agentforce platform in general availability this week. Microsoft plans to add agents to Copilot Studio next month.

“We barely hear them talk about co-pilots now,” Sallam said. “They’ve moved on to agents, and that’s certainly promising…but the reality is it’s still a work in progress.” You still have to be careful.

CIOs need to think about how autonomous capabilities fit into governance and risk management frameworksespecially as companies emphasize the importance of control and human intervention. Sallam said techniques such as reinforcement learning offer an alternative for powering autonomous systems.