A third of AI projects will be abandoned by businesses by the end of next year

The analyst company Gartner has predicted that 30% of generative AI projects will be abandoned by businesses following their proof of concept stages by the end of next year. Some of the reasons for the predicted abandonment include poor data quality, inadequate risk controls, escalating costs, or unclear business value.

Gartner gave some examples of how gen AI is being used, some applications include coding assistants, personalized sales content creation, document search with Retrieval-Augmented Generation (RAG), virtual assistants, and medical, insurance, or financial services LLMs.

Coding assistants are the most cost-effective applications with upfront costs ranging from $100K to $200K and recurring costs per user per year ranging from $280 to $550. Costs balloon when you start building LLMs from scratch to offer medical, insurance, or financial services LLMs with upfront costs ranging from $8 million to $20 million and recurring costs ranging from $11K to $21K.

Commenting on the prediction, Rita Sallam, a Distinguished VP Analyst at Gartner, said:

"After last year's hype, executives are impatient to see returns on GenAI investments, yet organizations are struggling to prove and realize value. As the scope of initiatives widen, the financial burden of developing and deploying GenAI models is increasingly felt.

 

Unfortunately, there is no one size fits all with GenAI, and costs aren’t as predictable as other technologies. What you spend, the use cases you invest in and the deployment approaches you take, all determine the costs. Whether you’re a market disruptor and want to infuse AI everywhere, or you have a more conservative focus on productivity gains or extending existing processes, each has different levels of cost, risk, variability and strategic impact."

It is not too surprising to hear that some businesses will not carry on with their use of some generative AI applications, that is what a proof of concept is for after all. As language models become more capable, businesses could have another look at finding use cases for them too.

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