A Third Of Gen AI Projects May Be Abandoned After Proof-Of-Concept By 2025: Gartner

At least 30% of generative artificial intelligence projects will be abandoned after proof-of-concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value, according to research and consulting firm Gartner, Inc. 

After the initial hype, executives want to see returns on their gen AI investments, but organisations are struggling to prove and realise value, Gartner said in a note. As the scope of initiatives widens, the financial burden of developing and deploying gen AI models is increasingly felt.

A major challenge for organisations arises in justifying the substantial investment in gen AI for productivity enhancement, which can be difficult to directly translate into financial benefit, according to Gartner. Many organisations are leveraging gen AI to transform their business models and create new business opportunities. However, these deployment approaches come with significant costs, ranging from $5 million to $20 million, including upfront and recurring costs.

“Unfortunately, there is no one size fits all with gen AI, and costs aren’t as predictable as other technologies,” said Rita Sallam, vice president-analyst at Gartner. “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,” Sallam said.

Regardless of AI ambition, Gartner research indicated that gen AI requires a higher tolerance for indirect, future financial investment criteria as compared to immediate return on investment. Many executives are reluctant to invest today for indirect value in the future, which can tilt investment allocation towards tactical versus strategic outcomes.

Early adopters of gen AI across industries and business processes are reporting various business improvements that vary by use case, job type, and skill level of the worker. According to a recent Gartner survey, respondents reported an average 15.8% revenue increase, 15.2% cost savings, and 22.6% productivity improvement.

However, it’s important to acknowledge the challenges in estimating business value, as benefits can be specific to the company, use case, role, and workforce, and the impact may not be immediately evident and may show over time.

By analysing the business value and the total costs of gen AI business model innovation, organisations can establish the direct ROI and future value impact, according to Gartner. This can serve as a tool for making informed investment decisions about gen AI business model innovation. 

“If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling gen AI innovation and usage across a broader user base or implementing it in additional business divisions. However, if they fall short, it may be necessary to explore alternative innovation scenarios,” said Sallam.