Gartner recently predicted that at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025. Reasons given include poor data quality, inadequate risk controls, rising costs, or unclear business value.
During the Gartner Data & Analytics Summit in Sydney, Rita Sallam, VP Distinguished Analyst at Gartner, noted that, after last year's hype, executives are impatient for returns on generative AI investments, but organizations are struggling to prove and realize value. As the scope of initiatives expands, the financial burden of developing and deploying generative AI models is increasingly felt.
A major challenge for organizations is justifying substantial investments in generative AI for increased productivity, which can be difficult to translate directly into financial benefits. Many are leveraging generative AI to transform their business models and create new opportunities, but these deployment approaches come with significant costs.
Sallam noted that there's no one-size-fits-all approach to generative AI, and costs aren't as predictable as for other technologies. Spending, use cases, and deployment approaches determine costs. Whether a market disruptor looking to infuse AI everywhere, or an organization with a more conservative focus on productivity gains or extending existing processes, each approach has different levels of cost, risk, variability, and strategic impact.
Regardless of AI ambitions, Gartner research indicates that generative AI requires a greater tolerance for indirect and future financial investment criteria than for immediate return on investment (ROI). Historically, many CFOs have been uncomfortable investing today for indirect value in the future. This reluctance can skew investment allocation toward tactical rather than strategic outcomes.
Realizing Commercial Value
Early adopters across industries and business processes are reporting a variety of improvements that vary by use case, work type, and worker skill level. According to a recent Gartner survey, respondents reported an average 15,8% increase in revenue, 15,2% in cost savings, and 22,6% in productivity improvements. The survey was conducted between September and November 2023 with 822 business leaders.
Sallam emphasized that this data serves as a valuable benchmark for assessing the business value derived from generative AI business model innovation. However, it's important to recognize the challenges in estimating this value, as the benefits are highly specific to each company, use case, function, and workforce. Often, the impact may not be immediately evident and may materialize over time, but this delay does not diminish the potential benefits.
Calculating Commercial Impact
By analyzing the business value and total costs of generative AI business model innovation, organizations can determine the direct ROI and future value impact. This serves as a crucial tool for making informed investment decisions about generative AI business model innovation.
Sallam concludes by emphasizing that if business results meet or exceed expectations, there is an opportunity to expand investments by scaling innovation and use of generative AI to a broader user base or implementing it in additional business divisions. Otherwise, it may be necessary to explore alternative innovation scenarios. These insights help organizations strategically allocate resources and determine the most effective path forward.
My Personal Impression
Na Flexa Cloud After working on hundreds of AI projects, I've seen that the main problem is when the client focuses on the tool and not the real problem it solves. When we move beyond this phase, the use cases become real and have a significant impact. It's essential to remember that technology is just a tool; the true value lies in how we use it to solve specific challenges and create transformative opportunities.
Let's continue to focus on solutions, not tools, and together we will achieve meaningful and lasting results.
