Remote working is now an established part of everyday life. However, as COVID-19 restrictions gradually begin to lift, many employees will start to return to the corporate office. Whether it’s on a full-time or flexible working basis, offices that have largely sat empty for over a year will begin to welcome employees back. But modern Wi-Fi needs to keep up with modern demands, from the unprecedented growth in collaboration tools such as Microsoft Teams and Zoom, which require a strong, reliable connection for maximum benefit, or the ever-growing number of connected devices in the office, which are putting strain on legacy platforms. Adopting a Wi-Fi platform that is driven by Artificial Intelligence (AI) can meet these needs and more.
How Can AI Enable IT?
In the age of “big data”, the amount of information being collected is too large for a person to analyse and process. As a result, AI has been eagerly adopted within many industries, including financial and legal. AI is being used in businesses in everything from research and document assembly to recruitment and predictive coding. An increasing number of businesses are turning to the technology to enable employees and make processes quicker and easier.
Despite the long-lasting repercussions of COVID-19, 30% of organisations plan to increase investments in AI, according to a Gartner poll.
Understanding AI and its benefits is essential as part of any organisation’s digital transformation journey.
Why Does Wi-Fi Need AI Now?
Wi-Fi has grown rapidly since its first release in 1997 to become an essential component of businesses. Whether it’s required in-store for customers or in offices for employees, a reliable and resilient Wi-Fi means that business is not halted. The growth in Wi-Fi popularity has also been accompanied by a vast increase in the number of devices connecting to it. Nowadays, in a standard office, any number of computers, laptops, tablets, mobile phones, IoT devices and more are reliant on Wi-Fi.
However, using Wi-Fi is often far from smooth sailing, being notoriously patchy and problematic. Businesses are often plagued by poor connectivity, configuration errors and can be difficult to troubleshoot. IT teams often spend large amounts of time trying to tediously troubleshoot and solve Wi-Fi errors.
Wi-Fi is increasingly becoming the primary method of Internet access. As a result, it is more business-critical than ever, yet legacy platforms are continually falling short.
Wi-Fi Built on Modern Needs
Predictable, reliable and measurable should be factors considered when adopting a new Wi-Fi platform. Legacy platforms are prone to bugs, expensive to scale and difficult to manage. They were built in a time where Wi-Fi was not prolific and aren’t designed to meet the requirements of modern-day devices. With some offices having hundreds or thousands of devices trying to connect to their Wi-Fi, legacy solutions are failing to match demand.
AI within Wi-Fi can provide analytics to IT, helping solve network issues quickly and efficiently. AI can be utilised for event correlation, rapidly finding the source of the problem to ensure it does not reoccur. It can also use the likes of anomaly detection and other features in order to prevent and avoid common problems.
The Juniper Mist Cloud is the first AI-driven wireless platform, designed specifically for the smart device era. It combines AI, machine learning and data science with the latest microservices technologies, delivering a smart, scalable solution that optimises the wireless experience for digital transformation.
The Juniper Mist Cloud uses AI and data science to analyse large amounts of rich metadata collected from Access Points and EX Series Switches to provide actionable insight. For example:
- Supervised machine learning correlates events for rapid root cause identification.
- Time-series anomaly detection identifies negative trends and determines the magnitude of their impact.
- AI-driven Radio Resource Management (RRM) optimizes the RF settings in real-time based on changing conditions.
- Natural Language Processing (NLP) is used for making complex queries simple and fast.
- Unsupervised machine learning is used with Mist’s vBLE technology to accurately locate users and devices.
The Marvis Virtual Network Assistant (VNA) brings conversational AI to networking, supporting IT teams by delivering streamlined operations, simplified troubleshooting, and exceptional user experiences. Since 2016, the Mist AI engine has applied various data science tools to continue to learn and improve, expanding its knowledge base as it becomes a fundamental component of The Self-Driving Network™.
Marvis’ conversational interface can contextualise requests to accelerate troubleshooting workflows, automate responses and make intelligent decisions or recommendations to:
- Get real-time answers about the network in a few clicks.
- Troubleshoot issues for rapid resolution by taking action directly in the conversation.
- 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, such as “How many iPhones are connected?” and “What was the number of peak devices in the office last week?”
The Key Components of the Juniper Mist Cloud
AI, Machine Learning and Data Science
- Adapts in real-time to changes in user, device, and application behaviour for predictable and reliable Wi-Fi and accurate location services.
- Monitors network trends in real-time and send alerts when service levels degrade. Recommendations are provided for troubleshooting and/or proactive configuration changes.
- Built on a microservices architecture that brings the agility of SaaS to wireless networks. As a result, on-demand network upgrades and patches take minutes instead of months.
Modern Cloud Elements
- Web-scale enables Mist to collect, analyse and store real-time metadata from all wireless clients.
- Containers ensure portability and fault tolerance.
- Kafka, Storm, Spark and other elements provide speed, scale, and resiliency.
- A global cloud instance provides insight into macro-level trends.