Three out of four that adopt artificial intelligence (AI) do so without having the ability to scale-up its use or automate processes using the technology, according to a survey. The F5 2024 State of AI Application Strategy Report reveals that while 75% of enterprises are implementing AI, 72% report significant data quality issues and an inability to scale data practices.
► Only a quarter are yet to deploy GenAI at scale
► Lack of application-specific use-cases remains an issue
Many organisations are likely to face significant challenges as they deploy AI more widely, the report states. While many are enthusiastic about generative AI, only 24% have implemented generative AI at scale. The most common use cases to date are deployments of simple productivity tools (in use by 40% of respondents) and customer service tools such as chatbots (used by 36%). Tools for workflow automation (36%) were named the highest priority AI use case, however.
The report said that three main concerns with AI adoption encountered at the infrastructure layer are the cost of compute to scale AI (62%); model security (57%); and performance (55%).
At the data layer, data maturity is a more immediate and potentially bigger challenge with 72% of study respondents citing data quality and an inability to scale data practices as the top hurdles to scaling AI. More than half (53%) said a lack of AI and data skill sets are an issue. While 53% of enterprises claim to have a defined data strategy in place, over 77% state they lack ‘a single source of truth’ for their data.
Cybersecurity is a key concern with AI-powered attacks, data privacy, data leakage, and increased liability ranking among the top AI security issues.
Asked how they plan to defend against these threats, 42% said they are using or planning on using API security solutions to safeguard data as it traverses AI training models; and 41% use or plan to use monitoring tools for visibility into AI app usage; 39% plan to use DDoS protection and 38% to deploy bot protection for AI models.
