Riverbed has released the findings from its new global study, “The Future of IT Operations in the AI Era,” which uncovers a major disconnect between the promise of AI and its actual implementation. The survey highlights that while most organizations see the value in AI, their adoption is being held back primarily because they lack the clean, reliable data and robust IT operational framework necessary to successfully support AI initiatives.

In this interview, Jim Gargan, CMO of Riverbed, highlighted the rapid advancements in AI, emphasizing its dual role in enhancing IT operations and fostering business innovation. He noted that companies need to adopt AI cautiously, advocating for a testing and learning approach before broader implementation.

Insights from the survey revealed a disparity between the perceived and actual effectiveness of AI investments. While 78% of companies are increasing their AI spending, only 46% of respondents felt their data quality was adequate for supporting AI initiatives. Despite 90% of IT leaders believing in the ROI of their AI investments, only 10% of projects have been fully deployed, indicating a gap in understanding and alignment between IT leadership and staff. Jim emphasized the complexities of managing data for AI, including the challenges posed by using multiple observability tools and the need for strict governance to ensure data privacy and adherence to AI usage policies.

Some of the findings were:

  1. 88% survey respondents believe they will meet or exceed expectations when it comes to AI, but only 12% have AI in enterprise-wide production, with a large portion of these projects in test/pilot mode.  And shockingly, only 1 in 10 AI projects are fully deployed. 
  2. Enthusiasm for AI is increasing
  3. 78% are increasing AI spend
  4. Major Readiness Gap  
  5. Only 36% of total respondents feel prepared for AI
  6. 42% of leaders feel prepared versus 25% of IT staff
  7. Yet, they are aligned around being AI-ready by 2028
  8. 86% of all total respondents (both C-Suite and IT staff) believe they will be ready for AI in three years
  9. Data quality is a lingering issue hindering readiness
  10. Only 46% of total respondents feel their data quality is ready for AI
  11. Gap between the promise of AI ROI and actual AI implementation
  12. 88% of all respondents believe they will meet or exceed expectations, but only 12% have AI in enterprise-wide production, a large portion of these projects are in test/pilot mode.

RELATED ARTICLESMORE FROM AUTHOR