There has been rapid innovation in the telecom industry with the advent of AI and IoT. The telecommunication industry is no longer limited to telephones and internet services but is now the centre of technological advancements with an immense focus on Artificial Intelligence. Telecom players are adding AI capabilities, given their massive volume of data being exchanged and consumed across multiple mobile devices. 

However, one of the challenges faced by board directors and C-Level executives is - with data so widely distributed in company-wide operations, it takes tremendous vision to combine all data sources into a unified AI operating intelligence hub. This is where Juniper Networks comes in. The company integrates AI in their large operations, allowing them to secure actionable insights, provide better customer experience, improve operations, and increase revenue. 

We reached out to Ravinder Singh, Country Director, Enterprise & Govt., Juniper Networks, to understand how the company is helping establish an agile network infrastructure and the increasing role of AI in networking.

Earlier, you build a secure INDIA ROUTER for security-sensitive Govt. infrastructure. So, what's your take on the Government networking infrastructure? How can it be made more robust in the coming times?  

Current events have created new challenges for state and local governments, driving them to accelerate their digital transformation initiatives. To better manage constricted budgets, distributed workforces, and heightened risk, they’re looking to network architectures that seamlessly connect at-home workers to the cloud, lower total cost ownership, and simplify operations through AI-based automation. Their infrastructures need to improve the network experiences of both government workers and citizens while providing a comprehensive security framework that supports zero trust access control for optimum data protection. 

Juniper integrates AI-based analytics, automation, and security defences with network infrastructure, enabling smart operations and multilayer data protection across every point of connection, from client to cloud. With this, organizations get continual visibility into real-time user experiences coupled with a dynamic, automated resolution of network issues to ensure premium-quality experiences for government workers and citizens.  

How is Juniper Networks utilising its in-house Mist AI cloud infrastructure to provide transformational products for IT Teams? 

Mist AI uses a combination of artificial intelligence, machine learning, and data science techniques to optimise user experiences and simplify operations across the wireless access, wired access, and SD-WAN domains. 

Data is extracted from numerous sources, including Juniper Mist Access Points, Switches, Session Smart Routers, and Firewalls, for end-to-end insight into user experiences. These devices work parallel with Mist AI to optimise user experiences from client-to-cloud, including automated event correlation, root cause identification, Self-Driving Network™ operations, network assurance, proactive anomaly detection, and more. 

Juniper also leverages Mist AI for next-generation customer support, which is the foundational element behind Marvis, the industry’s first AI-driven Virtual Network Assistant. 

Can you tell us about your Marvis Virtual Network Assistant (VNA) product? 

The Marvis Virtual Network Assistant (VNA) leverages Mist AI to transform how IT teams interact and engage with enterprise networks. With Natural Language Processing (NLP), a conversational interface, prescriptive actions, Self-Driving Network operations and integrated help desk functions, Marvis VNA streamlines operations and optimizes user experiences from client-to-cloud – i.e., across wireless access, wired access, and SD-WAN domains.  

The power of the Marvis conversational interface is that it can contextualize requests to accelerate troubleshooting workflows, answer product or feature-specific questions, provide information about the network, and help find any type of network device. It can make recommendations to:

• Get real-time answers about the network in a few clicks  
• Deduce user intent from general statements and inquiries using advanced NLP with NLU and NLG. Improve specific user experiences by learning from user feedback 
• Ask generic questions beyond troubleshooting, like “How to setup RRM?” and “Does AP have capacity 

Marvis automates troubleshooting and support so IT teams can get to a shorter mean time to resolution and innocence. It presents a comprehensive network view with the user, client, and device insights, eliminating the need to pull up multiple dashboards or memorize CLI commands.

Do you think autonomous networking is the future? Any solid data or points to back your claims? 

Readiness is a key factor for autonomy. AI tools need direct access to the network’s raw data streams and control systems to collect and push information through an analytics engine. The simpler it is to connect the network and the AI engine, and the more data sources you feed, the more intelligent your autonomous network will be. The complexity of the underlying IT architecture can complicate AI analysis and automation, as not every legacy network will be fit for AI.  

Having one transparent ecosystem built on a common software-defined architecture will provide the clearest visibility from edge to edge, and those already making investments in SD-WAN, SD-networks, and SASE will be better prepared. SD-WAN and SASE are the first steps in the path to an autonomous network as they establish a software-defined control plane and agile IT infrastructure where security is integrated.