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Gartner Predicts 30% of Generative AI Initiatives Will Fail by 2025
Research suggests generative AI projects will fail due to high costs and data quality issues, despite initial hype
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New research from analyst firm Gartner suggests that at least 30% of generative AI businesses currently testing will be abandoned after proof of concept by the end of 2025.
The statistics were announced during Gartner’s Data & Analytics Summit in Sydney, Australia.
Gartner found that early generative AI adopters are struggling with escalating costs and said that deployments can range in cost from $5 million to $20 million.
For example, designing a custom generative AI model, like fine-tuning a Llama model on industry-specific data, would cost a business $5 million to $6 million upfront and up to $11,000 in recurring costs.
Creating a model from scratch would cost as much as $20 million. Even setting up a simple document search feature through retrieval augmented generation (RAG) costs upwards of $750,000.
Credit: Gartner
Gartner suggested businesses are struggling to justify investments in generative AI due to the substantial finances needed.
The analyst firm said that regardless of ambition, generative AI “requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI).”
“After last year's hype, executives are impatient to see returns on generative AI investments, yet organizations are struggling to prove and realize value,” said Rita Sallam, Gartner’s distinguished vice president analyst. “As the scope of initiatives widens, the financial burden of developing and deploying generative AI models is increasingly felt.”
Gartner's research suggests early generative AI adopters are reporting varied business improvements.
A recent Gartner survey of 822 business leaders found that just 15.8% reported revenue increases, 15.2% said they had saved costs and 22.6% said they’d noticed productivity improvements in the wake of generative AI deployments.
“This data serves as a valuable reference point for assessing the business value derived from generative AI business model innovation,” said Sallam. “But it’s important to acknowledge the challenges in estimating that value, as benefits are very company, use case, role and workforce specific. Often, the impact may not be immediately evident and may materialize over time. However, this delay doesn’t diminish the potential benefits.”
Gartner suggests that businesses considering deploying generative AI technologies should analyze the business value and the total costs to uncover both direct ROI and future value impact, helping to make more informed investment decisions.
“If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling generative AI innovation and usage across a broader user base, or implementing it in additional business divisions,” said Sallam.
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