Back to Posts
Posted by Phil Alsop on 04 July 2024 at 7:53 am
  • news

In fact, 72 per cent of enterprises admitted to facing significant challenges with data quality and scaling data practices, having a significant impact on the output quality of AI models.

Out of the 750 IT decision makers surveyed, a quarter (24 per cent) said that they had implemented generative AI at scale, with employee productivity tools and customer services tools the most common implementation.

“Our report highlights a concerning trend: many enterpirses, in their eagerness to harness AI, overlook the need for a solid foundation. This oversight not only diminishes the effectiveness of their AI solutions but also exposes them to a multitude of security threats,” said Kunal Anand, CTO at F5.

The report also highlighted key barriers to scaling AI technology within the data layer, with 72% of respondents pointing to data quality and the inability to expand data practices as major issues.

Additionally, 53% of respondents identify a lack of AI and data-related skillsets as significant obstacles.

When it comes to the infrastructure layer, enterprises express concerns about the cost of computing resources (62%), model security (57%), and overall model performance (55%).

Commenting on the findings, Roman Kucera, CTO and Head of AI, Ataccama: “Before deploying AI, enterprises must ensure the quality of their data otherwise poor data will impact the accuracy of output. Insufficient or poor quality dat

Sign up to continue reading

Sign up for our email updates to continue reading our news and blog posts.
By signing up for email updates you agree for us to contact you about this and any future AI Transform events.