A global survey of 1,244 IT leaders published today finds three quarters (75%) work for organizations that have successfully deployed some type of artificial intelligence (AI) initiative, while another 23% said there have been a mix of success and failure.
Conducted by Hitachi Vantara, a provider of IT infrastructure platforms, the survey also finds that only 1% characterized their AI efforts as being a failure.
However, only 36% said AI is now part of critical functions, while another third (33%) said there is widespread adoption. Additionally, only just over a quarter (26%) rated their AI readiness as strong, while 31% said the quality of their data is also strong.
More challenging still, only 37% said they are able to calculate a return on investment (ROI) in AI. A full 71% said their organization today treats AI investments like a research and development expense. Nevertheless, 68% said AI investments are specifically aligned to revenue, cost savings, or customer experience improvements.
Overall, AI success may be in the eye of the beholder but the top use cases for AI are data analysis and insights generation (21%), automation and workflow streamlining (21%) and accelerated data processing and operational productivity (20%). Top reasons cited for success are having a dedicated AI team (49%), followed closely by high-quality data (48%), regular monitoring/evaluation (45%) and support from employees (44%), the survey finds.
Conversely, the top reasons cited for a project being unsuccessful are not enough data (28%), followed by inaccurate results (27%), unskilled workers (25%) and no clear use case (25%).
Specifically, IT leaders report they have invested in AI tools and platforms to address observability (85%), vulnerability discovery (81%), unauthorized use of personal data (80%), copyright infringement (78%) and explainability (70%) to advance adoption of AI initiatives.
It’s also apparent that organizations are struggling to manage increasing volumes of data, says Jay Subramanian, senior vice president and general manager for core storage platforms at Hitachi Vantara. For example, 69% of respondents said their data storage requirements will increase, while 73% said complexity in their data storage environments is increasing extremely quickly or rapidly. In fact, many of the data management issues that have long been ignored are about to become much bigger challenges in the age of AI, adds Subramanian. “A lot of organizations continue to struggle,” he says. “There isn’t a lot of visibility.”
Well over half of survey respondents (56%) are also concerned about data security, including a data breach-enabled AI attack (43%) and data breaches caused by an internal mistake (41%).
In total, AI projects are being led about a third of the time by an internal IT team, while another quarter are led by a chief AI officer, the survey finds. In the short term at least, it appears the jury is still out over which teams in an organization will lead the charge forward from here.
Regardless of who assumes the AI mantle within an organization, however, there is little doubt that the number of AI initiatives that organizations will be funding in the months and years ahead will also increase. The challenge and the opportunity now is determining which ones to move beyond the research and development phase to deliver meaningful value to the business at a time when many organizations have not yet fully mastered data management fundamentals.
