We are living in a uber-digital world where each and every part of our life is touched by technology. In fact, since the time we wake till the time we go to sleep, we depend on the usage of technology, smart devices and digital tools to meet our daily needs – either personal or professional, including business as well as consumer requirements. The way technology is advancing, there is no stopping of this dynamic movement and its ever-growing influence.
If we are allowed to talk about one single technology that is making a whole lot of noise today, it will be Artificial Intelligence. It is one of the most dominating innovations that is making maximum impact in all other technological arenas.
Larry Page rightly puts it, “Artificial intelligence would be the ultimate version of Google. The ultimate search engine [that] would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”
In the middle of all these influxes, the two most-talked-about subjects are ‘Enterprise Mobility’ and ‘Artificial Intelligence’. And together, they make quite a combination! After all, human decisions are driven by two core aspects: mobility and intelligence. Before we discuss how AI and Machine Learning are impacting Enterprise Mobility, let’s just have a high-level understanding of these two terms.
What is Enterprise Mobility?
Today’s progressive organizations believe in successfully integrating enterprise mobility into every level of their employee operations and workflow management. In fact, their success and growth depend heavily on how they have leveraged enterprise mobility as a solution to drive overall employee productivity, customer experience, IT performance, cost efficiency, business profitability and corporate sustainability.
In simple terms, enterprise mobility is the company’s ability to utilize the latest mobile technologies in order to perform business activities, exchange information, and drive enterprise operations from anywhere and anytime. The software that is globally used to administer and drive enterprise mobility in companies using management tools, security policies, and monitoring processes is known as enterprise mobility management.
Enterprise Mobility Management comprises the right set of processes, technology, policies, and people to manage, secure, and monitor the usage of company devices, networks, information, and applications within a secure corporate environment.
What is Artificial Intelligence?
Artificial Intelligence can be defined as a branch of Computer Science that empowers machines with the capability to learn from observations and experience, provide intelligent inputs and exhibit human-like performance with the help of technologies such as natural language processing and deep learning, which train computers to process huge amounts of data, recognize the data patterns and extract actionable insights from them.
Any machine that has the ability to implement cognitive activities like leaning, perceiving, solving problems, answering and reasoning is said to have obtained Artificial Intelligence. AI works in a specific manner through a set of steps. It starts with identifying data from varied sources like devices, networks, usages, activities, and structured and unstructured information. After that, the identified data gets processed and applied to AI models, which are then trained to assess the underlying problems and deployed to provide outputs.
Playing Nice Together to Maximize Business Outcomes
One of the core strengths of enterprise mobility is the fact that it enables company employees to work, perform, and stay productive from anywhere and anytime with the help of the right tools, platforms, and devices. Using the perfect enterprise mobility management solution helps employees stay productive at work, accurate with results, and efficient with operations.
Companies are extensively encouraging employees to use smartphones at work to stay productive and performance-driven throughout the day. Mobile devices empowered with the right set of applications and content are meant to drive efficiency at several operational levels. Since the last few years, smartphone manufacturers are exploring and imbibing AI/ML in smartphones and there is a list of technologies that are being introduced in smartphones with the help of it.
Smart Devices
These are smart cameras, in-built language translation, improved mobile applications, and mobile device security. AI-powered smartphones and mobile devices can analyze users’ behaviors and device usage patterns and accordingly rearrange apps as per users’ tasks, preferences, and priorities. Machine Learning enables these devices to manage resources and optimize CPU, RAM, and ROM performance to enhance the way these devices operate, also closes unnecessary and memory-exhaustive apps running in the background. One of the recent examples of AI-powered devices is Huawei Mate 9.
Improved BYOD Strategy
Companies supporting a BYOD culture can also experience heightened employee productivity and operational efficiency when employees use personal smartphones powered by the capabilities of AI, which enhance the performance of phone speed, features, documents, and applications, and also the ease of using the device. Collectively, it implies improved functionality, employee productivity, eliminated operational costs, reduced system delays, and enhanced business decision making.
App Analysis
Artificial Intelligence also aims to alter and improve the business logic within the apps as they become capable of leveraging user interfaces, using speech, gesture, and visual recognition. When Machine Learning is applied to user activity streams, it offers organizations insights on how end users spend their time with the apps, which helps app developers and companies improve processes and business operations.
Chatbots
Companies that have integrated Chatbots within their enterprise mobility framework can also witness multiple advantages such as enhanced employee experiences and improved visibility on employee performance and operational workflows, which led to better decision-making, improved team communication, employee productivity, and real-time issue resolution.
Companies are also using Chatbots to fulfill multiple tasks like collating and collecting all necessary information and data, conducting employee/customer surveys, communicating with clients and customers, etc. Chatbots can perform and deliver results in real-time and can be seamlessly integrated with the already existing mobile, technology, and IT ecosystem to promote operational excellence, improved CX, and secure communications without any hitches.
The other areas where AI has proved to be a benefactor and facilitator of enterprise mobility include sentiment analysis, data mining, predictive analysis, and automated reasoning.
Sentiment Analysis
Artificial Intelligence is utilized by companies in the form of Appbot that helps companies analyze and understand the sentiments, opinions, emotions and attitudes of users towards the particular apps to help developers manage and supervise app reviews. Appbot also provides a full picture of the content and features by assessing customer sentiments and activities. AI-based Natural Language Processing is used to know what customers are talking about your brand on social media platforms and other digital channels.
Data Mining and Predictive Analysis
AI provides a set of tools and algorithms for storing, maintaining, collecting, and analyzing big data, which discovers possible relationships between data and extracts meaningful conclusions necessary to drive effective and accurate business decisions. This descriptive analysis of data is called data mining. Apart from this, AI is also used for Predictive Analytics where AI models are applied in data mining to predict a particular pattern, behavior, or outcome.
Automated Reasoning
AI-powered Automated Reasoning is being used by certain companies wherein the AI algorithms pick-up and analyze already stored data that are previously used or accessed by customers or companies, in order to provide relevant resolution to a current requirement. As in today, the feature is used by Uber wherein the AI algorithms collect data like previously traveled routes and directions and uses the same as references in future trips.
AI Security Boost within the EMM Framework
Several EMM solution providers have already started to leverage Artificial Intelligence and its ability to recognize a particular set of patterns to mitigate certain security concerns and to achieve visibility and predictability over mobility operations. AI/ML is leveraged by many players to improve performance. The following are the scenarios where AI/ML is used to eliminate mobile security threats:
Machine learning is used to enhance the Threat Defense tool of a specific solution, which analyzes usage patterns and behavior to identify suspicious behavior within networks and mobile apps. After that, it digs into the collected information to gather more intelligence and to constantly improve its ability to detect unsafe networks and malware.
Deep Learning can be integrated with endpoint security solutions to offer predictive security by extending to all endpoints including smartphones and mobile devices. AI can also be used as an email protection tool to ensure that no endpoints will receive an infected email ever.
Certain companies use AI to help IT departments fight security risks in an improved way. AI/ML is used to collect, gather, and analyze all data resting into the devices that enter the workplace. The AI integrated security analytics monitors the devices to help IT apply dynamic security policies and regulations to ensure that corporate networks remain secure.
AI also empowers mobile security by defending the system against external risks, threats, and abnormalities as it keeps a constant watch over all the continuing activities. As soon as it detects/identifies peculiarities, it triggers alarm to notify the IT admins. This AI-driven automatic identification and detection of anomalies makes a great deal of difference to the enterprise environment.
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