Following our look at the SD-WAN and secure access service edge (SASE) reader metrics, the next hot topic to dig into is artificial intelligence (AI). We wanted to analyze our own data and let your actions tell us about this broad and complex technology and its adoption at the enterprise level.
What SDxCentral members are readingEveryone is trying to figure out what AI is or, more precisely, what it means to them in the context of enterprise or service provider infrastructure and operations.
Not surprisingly, reader metrics show explosive growth in AI as a topic on SDxCentral, with traffic tripling from Q1, 2023 to the Q3, 2023. The top-line numbers below compare AI over that period and reflect the rise in engagement — with some interesting twists:
- Page views for our AI Topic Hub increased by almost 500% with the average reader consuming at least two pieces of content on each visit.
- Engagement on news content was up over 265% and press releases were up 300%.
- Definitional content (e.g., What is AI) was up over 300% — and interestingly — What is networking switch fabric was popular as readers attempted to figure out if they needed to update their data center infrastructure.
- Looking more closely, our AI definitional article has been read by IT and telecom pros with a wide spectrum of job functions, with more higher-level executives reading it compared to similar articles on other topics. Engagement was spread equally among CxOs, operations leaders, architects, engineers and product managers. Our definition articles tend to skew more toward non-executives, so these metrics reinforce that during these early days, even top IT leaders are hungry for clear information.
Our content and reader engagement data reveals that very few companies articulate clear value propositions around their AI solutions or product offering. The result is that potential users have little motivation to act, which is shown by limited vendor-specific AI research being done on SDxCentral. It seems like every company in the SDxCentral organization directory claims some level of AI is incorporated in their product or solution collateral, making it nearly impossible to provide a clear, data-driven ranking of which companies readers are researching for AI infrastructure.
Furthermore, SDxCentral articles that cover industry predictions and company earnings calls that discuss the impact of AI typically offer bold public statements and rosy industry predictions without offering clear guidance on where, when or how to leverage AI infrastructure. This, in part, explains higher-than-expected engagement by enterprise line of business executives who have a fear of missing out. This is matched by lower-than-expected company and product research from architects and engineers who are attempting to sift through the fear, uncertainty and doubt to determine if AI infrastructure and automation are relevant to their enterprise and if it should be a 2024 priority. This mismatch and confusion is slowing adoption of AI-enabled or enabling technologies.
Analyzing enterprise priorities for AI infrastructure and automationWhile SDxCentral has published many articles about large language models (LLMs) and generative AI (genAI), the greatest engagement came from content that was about AI and another core topic. The most common pairings were AI + Networking, AI + Cloud and AI + Security.
To help better understand how AI fits into these other core topics, we’ve defined the meaning of AI infrastructure and automation for enterprise and service provider IT professionals in specific product categories:
Observations- AI data center. Our data reveals that we’re in very early days for the AI data center. AI will likely drive future network and data center infrastructure budgets.
- AI security. Only a handful of companies come to mind when exploring how to build AI data security and compliance solutions today and this feels like the most likely place enterprises will adopt AI infrastructure tools. This is one of the reasons we see why traditional SD-WAN is no longer good enough for most enterprise use cases.
- AIOps and automation. This is the most advanced within single-vendor solutions, because the vendor providing this service (in most cases) controls what data is collected, the models that organize the data, and the machine learning (ML) algorithms required to build automated solutions.
- AI data centers: We expect few enterprises will build out an AI data center themselves and will rely on specialized providers with the expertise to build and operate such complex environments.
- AI security: AI is a natural addition to SASE and SSE solutions to inspect, visualize and enforce policy and ensure users are not leaking enterprise data to public genAI platforms or tools.
- AIOps and automation: We have seen strong customer buyer interest and research for multivendor data center and telecom network environments where there is a long history of common data types and models. One of the most interesting aspects of our reader engagement in this area is that it's a different persona reading AI networking operations, AI security operations and AIOps content. We expect this to converge over time as multiple stakeholders will need to understand, trust and leverage these different tools.
AI brings with it countless technological and ethical questions, which are important to ponder and debate. However, SDxCentral readers, as cited above, want AI articles that help them today, tomorrow and in 2024 — not five years from now. A quick look at the five most popular AI articles published in the last 90 day shows that you want answers, you want AI in context, not in theory.
- Cisco explains why AI networking and Ethernet fabric are a perfect match
- Versa Networks adds more AI power to boost SD-WAN, SASE networking
- What is artificial intelligence (AI)? Its applications, architecture and future
- 3 tips for proactive risk management in generative AI
- Why data centers need to invest in infrastructure to meet AI demands
Comments