How AI Will Change Android Device Management

In the enterprise, AI seems poised to disrupt aging pieces of the software toolchain. One link in that chain is mobile device management (MDM). For decades, legacy MDM tools have lurched forward inch by inch, dragged along by comparatively explosive innovation in the device world. The rise of wireless, the cloud, and modern mobility and operating systems like Android have totally transformed the way businesses use devices. AI seems set to similarly transform the ways they manage them, and we’ve made a list of where we think are in the future: 

Android Device Management with Agentic AI Support

The ability to intelligently report and alert against certain device states is one of the hallmarks of modern Android device management. However, even today’s most advanced predictive tools can only do so much — after all, there’s no software fix for a failing battery. But it’s not difficult to imagine a future where agentic AI plugs into our device reporting and status engines and connects the dots for humans to take action.

Automating Android device support with agentic AI

Imagine you run device operations for a large retail chain, and a mobile Android POS tablet at a location has a failing battery. Your MDM has automatically flagged this issue to the appropriate operations staff, but getting the retail team on site to take action still requires asynchronous communication (i.e., emailing the site manager and asking them to raise a ticket). This slows your time to a solution and keeps a failing device in service longer than it should be.

With agentic intelligence, your MDM could pop a dialogue on the device for the next staff member who picks it up — initiating a conversational support flow.

• “Hi, this tablet’s battery may be failing. Can you hand the device to your line manager right now?” (Yes)
• “Have you or your staff noticed poor battery life on this tablet, or does it power off shortly after it is disconnected from power?” (Yes)
• “Thanks. Do you have any spare tablets in the store to replace this one?” (No)
• “No problem. Your IT team has been notified and will ship you a replacement tablet soon. Please use this tablet only when connected to power, or set it aside in a safe location.” (OK)

Agentic support is nothing new — online retailers have been leveraging it for many years now. But agentic support that intelligently leverages real-time device status data from your MDM to proactively initiative and resolve a ticket? That’s a game-changer.

AI for Adaptive Android Device Configuration

Defining your Android device configurations and having the confidence they will remain configured is hard enough. Modern Android MDM has advanced device configuration by leaps and bounds, with policies that reapply automatically and devices that self-heal when they’re tampered with. But in the future, what about devices and configurations that are responsive to business and customer needs?

How data + AI can build responsive Android device experiences

One of the biggest challenges for consumers using self-checkout and self-ordering experiences is quickly learning the interface. If it’s too complicated, distracting, or unintuitive, customers will flag staff for assistance or abandon their carts. And depending on market demographics, it’s entirely possible the “optimal” self-checkout experience in one location will differ from another. 

By gathering and analyzing user data across each store, your Android MDM could use AI to suggest adaptations to your standard device configuration — and then test those changes and report back on their success. For example:

• Display language picker at startup in locations where device language is changed by >X% of customers.
• Lower audio volume to 25% in locations where device is muted by >X% of customers

Similarly, consider a scenario where employee inventory management handhelds are used on a big-box retail floor. If those handhelds frequently don’t end up back on their chargers at the end of business, you face problems the next day. If the problem reaches a particular threshold, your MDM could proactively suggest a new behavior. The handhelds will buzz and beep once a minute for the 10 minutes before 11PM end of shift, showing a full-screen notification to place the device back on the charger immediately.

These sorts of adaptive configurations may not be suited for every store or every device, and they absolutely require intelligent monitoring, reporting, and ongoing adaptation to become scalable. AI’s ability to recognize patterns and maintain constant visibility make it the perfect fit for a new age of adaptive device management — optimizing outcomes beyond the fleet and regional level, and down to every location.

AI for Intelligent Android Device Deployment

Creating a sustainable device deployment strategy is one of the biggest challenges for scaling Android device fleets. The process of scoping the device need for a new location, documenting the deployment process, and executing that process are huge projects in and of themselves. These needs have driven the adoption of modern Android MDM solutions with powerful automated deployment tools. AI is likely to be a key driver of the next generation — intelligent deployment — in the not-too-distant future.

When Android device deployment is finely-tuned, this process becomes a highly automated and repeatable motion. But exceptions always arise, and deployment is never a “one and done” operation. Changing business conditions and site-specific challenges mean that your deployment strategy can always be better optimized, but such optimizations may seem impossible to integrate into what are inherently strictly-defined processes. 

Using AI to optimize utilization and allocation of Android devices

A large quick-service restaurant chain is a business that needs an extremely robust, repeatable device deployment strategy. New openings need to be choreographed down to the hour, and Android devices (POS, KDS, self-serve kiosk, drivethru) must all be operational before doors open. But this often leads to rigidity in the deployment process — any deviation from plan could disrupt business, and so strict adherence is crucial to success. While this tends to drive real innovation in terms of automation and streamlining, it necessarily puts a massive premium on flexibility and responsiveness as a result. Deployments happen quickly and reliably, but with relatively little room to accommodate specific site or market needs.

With AI, deployment strategies can take the next great leap forward, with intelligent allocation of resources and responsive scaling. Consider the same quick service restaurant scenario above, but with a device management AI that can inform each new store deployment. 

• Leveraging historical device usage data from similar deployments, your AI can suggest that you include more mobile Android POS systems for your opening, to support higher initial customer traffic. 
• Over time, your MDM can monitor the usage of those supplementary devices and determine if they’re still necessary. If they aren’t being utilized, it can also suggest other, under allocated locations where they would be  — so that hardware isn’t just sitting unused in a storeroom. 
• Hardware can be allocated seasonally based on that same usage data, ensuring that every location has the optimal device deployment footprint to maximize transaction volume and maintain customer satisfaction.

In a world where we’re constantly being asked to do more with less, AI looks primed to help us do just that — taking the guesswork out of where and when our Android devices need to go to do the most good for the most people.

The Not-Too-Distant Future of Android Device Management

While it seems inevitable that AI will change the landscape of Android device management in the coming years and decades, there’s still plenty of innovation happening right now. Self-healing device configurations, automated software rollback, and enterprise toolchain integration are all achievable today — with an advanced Android MDM solution.