Many AI projects are struggling due to rising costs and increasing risks, as recent reports have highlighted. Gartner predicts that by the end of 2025, around 30% of generative AI projects will be abandoned after the proof-of-concept stage. Companies are finding it hard to demonstrate and achieve value in their endeavors, which are requiring upfront investments ranging from $5 million to $20 million.
Another report from Deloitte revealed similar findings. Out of 2,770 surveyed companies, 70% have only moved a small portion of their GenAI experiments into the production stage, mainly because of lack of preparation and data-related issues.
Research by the think tank RAND indicates that despite a significant increase in private-sector investments in AI, over 80% of AI projects fail — twice the rate of failure in corporate IT projects without AI involvement.
The financial discrepancies and completion rates may have led to the “Magnificent Seven” tech companies collectively losing $1.3 trillion in shares in just five days last month.
Investing in GenAI projects can be costly. Gartner estimates that utilizing a GenAI API might require an upfront cost of up to $200,000 with an additional annual expense of $550 per user. Building or fine-tuning a custom model can cost between $5 million and $20 million, plus an annual cost per user.
Despite the high costs, many IT leaders across the globe are increasing their AI investments. There are concerns about the return on investment, as well as doubts expressed by investors in major tech companies about the potential payoff of their backing.
One of the primary reasons for the failure of enterprise GenAI projects is a lack of preparation. Organizations often lack the required technology infrastructure and data management capabilities to scale up AI projects successfully.
Data quality also poses a challenge in completing GenAI projects. Many businesses have avoided certain AI use cases due to data-related issues such as sensitivity, privacy concerns, and inadequate data for effective model training.
Despite these challenges, businesses continue to pursue new GenAI projects. There are tangible impacts on revenue savings and productivity driving this perseverance, as organizations see strong early value in their AI investments.
However, there are concerns that GenAI projects may become a distraction, potentially diverting attention from essential IT functions and infrastructure concerns. It is essential to acknowledge the challenges in estimating the value derived from GenAI projects, as benefits can be specific to each company, use case, role, and workforce.
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