A new Foundry survey shows IT leaders are all-in on the idea that artificial intelligence (AI) can help them address a longstanding struggle with enterprise networks: making day-to-day management of networks easier. That, in turn, promises to free up IT to spend more time on strategic initiatives while maintaining a superior experience for end users.
AI can deliver transformational change in today’s networks with a variety of benefits, respondents say. But the survey also uncovered variations in how IT leaders are implementing AI in their networks, with some leveraging a “bolt-on” approach that may not deliver the desired results.
Objectives for AI in networking
The 110 senior IT leaders identified these objectives for pursuing AI in networking, each chosen by 30% or more of respondents:
- Data-driven decision-making
- Compliance and risk management
- Enhancing quality of service (QoS) and user experience
- Improving network reliability and uptime
When asked to pick their single most important objective, “improving network reliability and uptime” was number one, chosen by 17% of respondents.
That makes sense to Sharon Mandell, CIO for Juniper Networks, who argues that “you don’t get to do the cool stuff in IT until the core functions work well enough not to be a distraction.”
Such “cool stuff” includes focusing on digital transformation efforts, which was the number one project respondents said they’d spend more time on. Close behind: data analytics and business intelligence projects as well as cybersecurity.
But those time savings will only materialize, Mandell continues, if companies successfully deploy AI in their networks, a job that is not without its challenges, including these top four identified by respondents:
- Safeguarding the network against AI-specific threats
- Ensuring the long-term sustainability of AI initiatives
- Allocating resources effectively between AI and other initiatives
- Keeping up with the rapid pace of AI networking technology evolution
Assessing deployment options
The survey also turned up stark differences in the approach companies are taking to implement AI in their networks.
Twenty percent of respondents favor a bolt-on approach, where they add AI solutions to existing networks without significant infrastructure changes. More than a third (35%) say they’re taking an “integrated” approach, which involves redesigning networking infrastructure to fully integrate AI capabilities. Slightly more (37%) favor a hybrid approach that uses a mix of bolt-on and integrated AI solutions.
Those using a bolt-on approach will inherently struggle to incorporate AI into their IT infrastructure and, therefore, take full advantage of AI’s transformational benefits.
Juniper Networks takes a different tact. According to Mandell, its AI-Native Networking Platform was conceived and developed with AI integration as a core component. Unlike other platforms, Juniper Networks’ cloud-based Mist AI platform uses real-world data to recognize network issues as they’re developing and address them before they result in performance problems or downtime – with no aid from a network administrator.
It’s difficult to bolt on that ability to deliver data to the AI engine as well as receive instructions from it, Mandell says. “That’s why the cloud-native piece is important,” she says. “It’s a combination of AI-Native and cloud-native that makes it work.”
