90% of Enterprises are Exploring Generative AI, But 70% of Projects will Fail to Deliver

Fully 88 percent of organizations surveyed by S&P Global Market Intelligence on behalf of Weka for its 2024 Global AI Trends Report are now actively investigating generative AI, ahead of other AI applications such as prediction models (61 percent), classification (51 percent), expert systems (39 percent) and robotics (30 percent).  

Access to graphics processor unit capabilities is an issue. About 40 percent of respondents surveyed suggest access to AI accelerators is a leading consideration in their infrastructure decision-making, and 30 percent cite GPU availability among their top three most serious challenges in moving AI models into production.

That seems to be a bigger issue in the Asia-Pacific region than elsewhere, where lack of access to GPUs is restricting organizations from deploying AI. For example, 38 percent of organizations in India see accelerator access among their top three challenges to moving AI projects into production.

In other regions, access to “GPU as a service” might obviate such concerns. 

For these end user enterprises, though, the greatest proportion of respondents (35 percent) indicate storage and data management are the primary infrastructure issues hindering AI deployments, significantly greater than compute (26 percent), security (23 percent) and networking (15 percent).

That noted, it remains the case that many AI projects fail to reach “deployment at scale” status. According to one Gartner study, more than half of AI projects never are deployed at scale. The S&P Global Market Intelligence survey of about 1,500 respondents tends to confirm that finding, as few of the respondents have more than a handful of AI projects in production, at scale.  

source: Weka

And Gartner analysts suggest that about half of all IT projects fail to reach their financial goals. That might be equally true for GenAI projects as well, given the difficulty of quantifying success for the wide range of AI use cases being implemented. 

source: Weka 

And AI projects are at least as complicated as other IT initiatives and projects, all of which often fail to meet objectives, for any number of reasons. 

source: Weka

Of course, since generative AI and other machine learning applications remain relatively new, perhaps that should not come as a surprise. But Gartner research suggests as many as 85 percent of AI projects fail, and made that prediction about 2018, as nearly as I can ascertain. 

That would not be wildly out of line with the general industry rule of thumb that about 70 percent of IT projects also fail to deliver their intended results. 90% of Enterpris

Study Name	Date	Publisher	Key Conclusions
Standish Group Chaos Report	Annual	Standish Group	Consistently reports high failure rates for IT projects, often exceeding 70%, with common causes including unrealistic expectations, poor communication, and inadequate planning.
Project Management Institute (PMI)	Ongoing	PMI	While not always quoting the exact 70% figure, PMI's research frequently highlights the challenges and risks associated with IT projects, contributing to high failure rates.
Harvard Business Review	Various articles	Harvard Business Review	Numerous articles have discussed the high failure rates of IT projects, attributing them to factors such as organizational culture, lack of executive sponsorship, and poor change management.
IT Governance Institute	Various reports	IT Governance Institute	Provides insights into the factors contributing to IT project failures, such as inadequate governance, insufficient resources, and unclear objectives.