Gartner has forecasted that by the end of 2025, at least 30% of generative AI (GenAI) projects will be abandoned due to the financial burden associated with their development and deployment. This prediction highlights the growing challenges organisations face in proving and realising the value of GenAI initiatives amidst mounting costs and executive impatience.
Rita Sallam, Vice President and Analyst at Gartner, discussed these challenges during the Gartner Data and Analytics Summit. She emphasised that as organisations expand the scope of their GenAI initiatives, the financial strain becomes increasingly apparent. “As the scope of initiatives widen, the financial burden of developing and deploying GenAI models is increasingly felt,” Sallam stated.
Sallam pointed out that justifying the substantial investments in GenAI, particularly for productivity enhancements, remains a significant challenge. These investments often fail to directly translate into immediate financial benefits, leading to scepticism among stakeholders.
Adding to the complexity is the rising cost of GenAI projects. For example, initiatives involving GenAI-powered virtual assistants could cost between $5 million and $6.5 million. “Unfortunately, there is no one size fits all with GenAI, and costs aren’t as predictable as other technologies,” Sallam said. “What you spend, the use cases you invest in, and the deployment approaches you take, all determine the costs.”
Analysing the Benefits of GenAI
Despite these financial challenges, Gartner suggested that organisations can still determine the direct return on investment (ROI) and future value of GenAI by carefully analysing the business value and total costs of their GenAI initiatives. A recent Gartner survey of early adopters revealed promising outcomes, including an average increase in revenue of 15.8%, cost savings of 15.2%, and a productivity improvement of 22.6%.
Sallam noted that if business outcomes meet or exceed expectations, there is potential for organisations to expand their investments in GenAI by scaling innovation and usage across additional business divisions. “However, if they fall short, it may be necessary to explore alternative innovation scenarios,” Sallam advised. “These insights help organisations strategically allocate resources and determine the most effective path forward.”
She also highlighted the importance of recognising the challenges in estimating the benefits of GenAI, as they can vary significantly depending on the company, usage case, and role. “Often, the impact may not be immediately evident and may materialise over time. However, this delay doesn’t diminish the potential benefits,” Sallam concluded.
As the landscape of GenAI continues to evolve, organisations will need to carefully weigh the costs and benefits, making informed decisions on whether to continue, scale, or abandon their GenAI projects
