The research uncovered that many organizations have moved beyond the assessment and experimentation phase of AI, and today 65% are accelerating their AI strategies with investments in both infrastructure and talent. Another 23% are in the final stage in which AI is fully integrated into their business processes.
Still, organizations continue to struggle with data collection and normalization as well as their organization’s readiness to implement AI. The survey showed that 85% consider data a critical factor in implementing AI, however 69% said they were concerned about the effectiveness of their organization’s data for AI usage. Just 43% rated their data as excellent for completeness and 40% for accuracy, and 42% said their data quality is a barrier to further AI investment. Data security concerned another 76% of respondents who said they worry about their proprietary data being accessible in the public domain, Riverbed reports.
“If you have better data, that means you get better AI, you get more precise AI outcomes,” Gargan says. “More data is out on the edge, on edge devices, in the cloud, and in data centers, really spread all over the place. Organizations have to move data from where it is to where it needs to be in order to really implement generative AI. Having the ability to do that in a safe, secure, fast, and efficient way is one of the things are IT teams, in particular networking departments, are working with.”
