F5 (NASDAQ: FFIV) has published a new report offering a unique insight into the current state of enterprise AI adoption. According to F5’s 2024 State of AI Application Strategy Report, while 75% of enterprises are implementing AI, 72% are grappling with significant data quality issues and an inability to scale data practices.
Effective adoption and optimization of AI hinge on the quality of data and the robustness of the systems used to obtain, store, and secure it.
“AI is a disruptive force, enabling companies to create innovative and unparalleled digital experiences. However, the practicalities of implementing AI are incredibly complex, and without a proper and secure approach, it can significantly heighten an organization’s risk posture,” said Kunal Anand, EVP and CTO at F5.
“Our report highlights a concerning trend: many enterprises, 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.”
As enterprises develop new infrastructure to support a growing array of AI-powered digital services, the study emphasizes challenges across the infrastructure, data, model, application services, and application layers that must be addressed for widespread scalable adoption.
The Promise and Reality of Generative AI
Organizations are enthusiastic about the business impacts of generative AI, naming it the most exciting technology trend of 2024. However, only 24% of organizations have implemented generative AI at scale. The most common use cases for generative AI often serve less strategic functions, such as copilots and other employee productivity tools (used by 40% of respondents) and customer service tools like chatbots (36%). Tools for workflow automation (36%) were identified as the highest priority AI use case.
Enterprise leaders cite three main concerns in scaling AI-based applications at the infrastructure layer:
- 62% cite the cost of compute as a major concern.
- 57% cite model security, with enterprise leaders planning to spend 44% more on security in the coming years.
- 55% are concerned about performance across all aspects of the model.
At the data layer, data maturity presents a more immediate and significant challenge:
- 72% of respondents cite data quality and an inability to scale data practices as top hurdles.
- 53% cite a lack of AI and data skillsets as a major impediment.
- Although 53% of enterprises have a defined data strategy, over 77% lack a single source of truth for their data.
Cybersecurity Remains a Key Concern
Cybersecurity is a principal concern for those delivering AI services, with AI-powered attacks, data privacy, data leakage, and increased liability ranking among the top concerns. To defend against these threats, respondents are focused on app services such as API security, monitoring, and DDoS and bot protection:
- 42% are using or planning to use API security solutions.
- 41% use or plan to use monitoring tools for visibility into AI app usage.
- 39% use or plan to use DDoS protection for AI models.
- 38% use or plan to use bot protection for AI models.
