Is your operations team overwhelmed by alarms and constantly stuck in reactive, firefighting mode? It’s no wonder. According to recent research conducted by Enterprise Strategy Group (ESG), 73% of organizations stated their network environment has become somewhat or much more complex than it was just two years ago.
The results of ESG’s survey are detailed in a recently published white paper, AI-Native Requirements for Modern Networks. In the survey, the authors suggest that overcoming this complexity will require better end-to-end visibility and contextual intelligence to help organizations transition to a more proactive approach and drive greater operational efficiencies.
How, you ask? Good question. According to the paper, leveraging artificial intelligence (AI) and machine learning (ML) tied to automation will help network operations teams drive operational efficiency, create better experiences and shift from reactive to proactive—even predictive—management.
That said, AI adoption in the networking space is easier said than done. This is due to significant challenges associated with providing AI/ML models with the level of high-quality, abundant data they require. Some of these challenges include:
Technical: For starters, network operations data has typically been siloed in customers’ on-premises management systems and cannot be shared. When it comes to network management solutions, they typically have not been unified, creating even more puddles of unshared data. In addition, models are inappropriate for sensitive or proprietary data that must not be exposed.
Cultural: On the culture side, after becoming legacy troubleshooting experts, many network operation team members are simply reluctant to trust these models. Another issue is that without explainable AI (XAI), full acceptance will be difficult to achieve. And without a closed loop system, experienced network operators can’t provide feedback promptly.
Overcoming Barriers
According to the white paper, to overcome the barriers to operationalizing AIOps effectively, AI-driven networking platforms must meet the following five criteria:
- Cloud-based architecture: This critical feature eliminates data silos by aggregating comprehensive data from across different domains, thereby providing a unified view of the network’s health and performance.
- End-to-end visibility and context: A platform must offer a complete and contextual picture of the network environment and user experience. This holistic view is essential for understanding the intricate relationships and dependencies within the network.
- Conversational interface: To simplify operations, an intuitive interface is necessary. For example, a virtual assistant that allows for natural language queries and responses, making the management of complex networks more accessible to a broader range of users.
- Trusted, granular data: The platform should leverage session-level telemetry and metadata to gain deep insights into network operations. This level of detail is crucial for accurate analysis and decision-making.
- Bidirectional API ecosystem: An open and extensible API ecosystem is vital for enabling integration with other systems and tools. It also facilitates a feedback loop that supports continuous improvement and adaptation to changing network conditions.
Juniper’s Solution
As you’ll see, the white paper confirms Juniper’s fundamental belief in experience-first networking and the importance it puts on AI and automation to deliver the best possible experiences across an organization’s end-to-end network.
Juniper’s AI-Native Networking Platform was purpose-built from the ground up to leverage AIOps as a means of overcoming the barriers to AI adoption. By harnessing the power of AI, it enables proactive issue resolution, predictive analytics and automated optimization, which together are essential for delivering on the promise of AI in networking. Not to mention, it helps put an end to alarm fatigue.
To learn more about the challenges to leveraging AI in network environments and what to look for in solutions that overcome them, be sure to grab your free copy of ESG’s white paper, AI-Native Requirements for Modern Networks.
And finally, be sure not to miss Juniper’s upcoming live virtual event, AI-Native NOW. We’ve put together an amazing lineup of Juniper AI experts, industry-leading customers, and a not-to-be-missed keynote address by AI luminary and ex-Interim CEO at OpenAI, Emmett Shear. It’s a free virtual event, so register now, and we’ll see you at AI-Native NOW.
