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Enterprise mobility, AI & robotic process automation – Actions speak louder than deep thoughts

(Image credit: Image Credit: MNBB Studio / Shutterstock)

The ubiquitous use of the term “Artificial Intelligence” (AI) in recent years has led to a devaluation in its meaning. This is problematic, as, when utilised effectively, the technology can unlock countless new revenue streams and drastically improve business operations for almost any organisation. One aspect of AI that is already making waves in the business world is Robotic Process Automation (RPA); a facet of AI that can extract and process different types of data to provide actionable insights. In essence, RPA provides a way of automating the menial and basic but essential tasks, such as billing and tax returns, that every company is required to carry out.

This has particular potential in the field of enterprise mobility – the use of mobile devices for business. For enterprise mobility, RPA will help businesses do more than just simple mobile device management (MDM). In fact, it will enable companies to go beyond basic MDM by providing their IT teams with a solution that can identify patterns in mobile data and usage from company devices.

When we consider that most companies that make use of MDM services typically have thousands of devices in their portfolio, the potential to go beyond MDM with AI and RPA is tremendous. These companies deploy MDM because they want to know the health and status of each of the devices in their portfolio. With AI and RPA, IT teams will be able to know the health of every device in a company’s portfolio in real-time, and they will have the tools available to prevent a problem before it occurs or to fix it extremely quickly.

The technology to do this is now available and this situation is simple for a business to achieve. Once implemented, the benefits are that it can reveal valuable insights into device battery health, device functionality, geolocation, and generally help run an efficient enterprise mobility network. What’s more, through the use of application programming interfaces (APIs), businesses will be able to directly query their data and act upon it, without any human interference in the process at all. So, AI has the potential to completely revolutionise mobile device management, but only if utilised effectively as part of an efficient enterprise mobility strategy.

Perceptions vs. reality

In literature and at the cinema, popular culture has indelibly linked our perception of AI to constructs such as The Terminator or to the superhuman intelligence of the HAL 9000 computer in 2001: A Space Odyssey. Clearly, the claim of AI is centred around the computer’s ability to understand. RPA has a different claim; RPA is about the computer’s ability to carry out an action.

Current RPA solutions are often simple automation jobs such as extracting some information from a scanned document. This requires some minimal “understanding”. If, for example, the computer is supposed to extract an address from an invoice then it has to learn the format that addresses may appear in and then be able to detect them in a variety of documents. However, we do not use such a system in the hope that the computer will extract some novel insight regarding the nature of addresses within invoices.

When it comes to how this translates to enterprise mobility, it helps to think about how we can use AI to identify patterns in mobile device and mobile process data – patterns that do reveal novel insight into how a mobile operation might be run more efficiently. Where RPA comes in is on top of application programming interfaces (APIs) that enable programmatic access to the analytical results of mobile data. Using APIs, a computer system can directly query mobile data and act upon it, without any human interaction in the process at all.

For example, this could be to report on the health of each of the smart-batteries in all of the devices in a company’s mobile estate with a simple rule to enable the automatic order of replacements for batteries operating below a set threshold. We might not call that “intelligent”, but these are real-world actionable results that make mobile deployments run with greater efficiency, enabling more time for human intelligence to engage in higher value tasks within an organisation.

Teaching technology to teach itself

To harness the full potential of AI and RPA, though, businesses need to trust in the most valuable asset they have – the data. Only when we integrate the data on device health with data on the business process you want to optimise and the key performance indicators you measure success with, can we train AI models to find the best solution for you and RPA to automate the action using that data. That’s when we will see more businesses cutting waste – and consequently costs – and thereby increasing the productivity levels and happiness of their workforce by allowing them to focus on doing the parts of the job they enjoy.

Naturally, the key to much of this is analytics. As a standard, most mobile analytics products collect many basic operational metrics of devices, such as battery health or network connectivity. Based on those metrics you might want your batteries to last long and your networks to have a high signal strength. But these are only indicators of your business running smoothly, you do not actually want to optimise your processes around maximising battery performance. Instead what you might be interested in is the number and duration of support calls to your help desk or the number of parcels handled by a worker in a warehouse.

With the introduction of AI and RPA, we can go beyond simply ensuring the smooth running of a mobile deployment. With advanced AI and RPA, we can enable the system itself to figure out the most important predictors for optimal performance. A teacher chooses the right inputs to create a conducive learning environment, a teacher gives its students the right tools to solve a problem, and they might also have to keep oversight of the students such that they cause no harm to themselves or others in the process.

Anton Flugge PhD, Data Scientist, B2M Solutions

Anton Flügge PhD is a Data Scientist at B2M Solutions. He specialises in machine learning and data analysis and has extensive experience with working with mobile device analytics with real-time data.