Will Generative AI Fail to Deliver on Its Promised Value?
By the end of 2025, Gartner predicts that at least 30 percent of generative AI (GenAI) projects will be abandoned after the proof-of-concept stage. This is mainly due to poor data quality, inadequate risk controls, escalating costs, or unclear business value.
Rita Sallam, Distinguished VP Analyst at Gartner, explains that after last year's hype around GenAI, executives are growing impatient to see returns on their investments. However, organisations are struggling to prove and realise value. As these initiatives expand, the financial burden of developing and deploying GenAI models becomes more pronounced.
A significant challenge is justifying the substantial investment in GenAI for productivity enhancement, which can be difficult to directly translate into financial benefits. Many organisations are using GenAI to transform their business models and create new opportunities, but these approaches come with significant costs, ranging from US$5 million to US$20 million.
Sallam points out that there isn't a one-size-fits-all solution with GenAI. Costs aren't as predictable as with other technologies. The expenditure, use cases, and deployment approaches all determine the costs. Whether an organisation aims to infuse AI everywhere as a market disruptor or focuses conservatively on productivity gains and extending existing processes, each approach comes with different levels of cost, risk, variability, and strategic impact.
Gartner's research indicates that GenAI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). Historically, many CFOs have been uncomfortable with investing today for indirect value in the future. This reluctance can skew investment allocation towards tactical outcomes rather than strategic ones.
Early adopters across industries and business processes report a range of business improvements that vary by use case, job type, and the skill level of the worker. According to a recent Gartner survey, respondents reported an average revenue increase of 15.8 percent, cost savings of 15.2 percent, and productivity improvement of 22.6 percent. This survey included 822 business leaders and was conducted between September and November 2023.
Sallam notes that while this data is a valuable reference point for assessing the business value derived from GenAI business model innovation, it’s important to acknowledge the challenges in estimating that value. Benefits are very company, use case, role, and workforce-specific. Often, the impact may not be immediately evident and may materialise over time, but this delay doesn’t diminish the potential benefits.
By analysing the business value and total costs of GenAI business model innovation, organisations can establish the direct ROI and future value impact. This analysis is crucial for making informed investment decisions about GenAI business model innovation.
If the business outcomes meet or exceed expectations, it presents an opportunity to expand investments by scaling GenAI innovation and usage across a broader user base or implementing it in additional business divisions. However, if they fall short, alternative innovation scenarios may need to be explored. These insights help organisations strategically allocate resources and determine the most effective path forward.
