AI is no SKU—and what that means for the enterprise (www.techradar.com)

A recent MIT study highlights a striking 95% failure rate among generative AI pilots to deliver tangible financial results, reflecting a broader trend where four out of five AI projects stagnate, according to RAND's research. As organizations abandon AI initiatives at an alarming rate—doubling from the previous year—the underlying issue appears to stem more from poor strategy than flawed technology. The prevalent misconception that AI can be treated like a ready-made product leads enterprises into a "science-experiment" trap, where initiatives falter due to a lack of clear business objectives and measurable outcomes. To turn the tide, experts suggest a shift in focus from merely acquiring AI solutions to understanding specific organizational challenges and aligning projects with clear ROI-driven goals. This involves assessing the necessary data infrastructure and ensuring that initiatives are built upon solid business needs rather than technical curiosity. By prioritizing projects that directly connect to measurable business outcomes, organizations can mitigate risks and enhance the potential for AI to drive meaningful transformation, ultimately moving past the current trend of AI skepticism in enterprises.