How to Use AI Tools to Better Manage the Device Lifecycle

In our last post, looking at the AI tools available to support device lifecycle, we gave a 50,000-foot view of some of the AI tools now available to support the device lifecycle or built into the underlying technology offerings. They range from on-chip neural processing to device optimization to security to analytics. Most of the major OEM’s, including Dell, HP, Apple and Microsoft also offer AI-powered apps to improve productivity.

These tools have the potential to change the game when it comes to device management. Organizations need to begin thinking about how they will incorporate them into their standard toolset. Only then can they begin to derive real business value from AI tools.

Many organizations are using ServiceNow as their ticketing system. An enormous investment in process automation is built into these deployments. And now with the implementation of AI extensions for tools such as ServiceNow, organizations can do more to streamline the activities within the ticketing system. AI also allows for self-service enhancements that benefit both the IT organization and the business. Users can resolve many of the issues they face throughout the device lifecycle, saving a tremendous amount of time and money.

Start with What You Have

The challenge for most organizations is to cut through the marketing fluff and figure out which tools can deliver value in their environments. There’s so much hype out there that it’s difficult to know where to begin. For most organizations, the first step is to dive into what’s available within their existing portfolio. If you’re a Dell shop, you need to understand what Dell Optimizer does and how you can take advantage of it. If you’re using the Microsoft Intune portfolio and Microsoft 365, you need to look at implementing Copilot so that you can leverage the AI capabilities.

The data analytics team can become a critical part of this process. In the old way of doing things, it could take six months or more to implement the program enhancements for process automation. The data analytics team can help the business figure out how to use AI tools to do it for less money and in a shorter timeframe.

Rely on a Trusted Partner

When we sit down with clients, we talk with them about their AI objectives.  Our experts can review the AI tools that are available from the manufacturers and how they can incorporate them into their processes. We also talk about planning their device acquisitions. What does it mean to move from Gen 13 to Gen 14 chips? We work hand in hand with Intel to talk about how having an NPU on the system impacts performance. With Gen 14, the organization may be able to standardize on i5 versus i7 or i9 processors to save money while still getting better performance. We also look at how the organization can better manage the provisioning process with Autopilot and then leverage analytics to make management more effective.  Lastly, how are our clients leveraging device analytics to enhance their refresh plans, basing refresh on systems and application performance rather than only a time based model.

Many clients want to rush out and test everything available without understanding the capabilities of the tools already deployed across their enterprise.  We work with our clients to help them understand the capabilities of what they’ve already purchased. We recommend that our clients take time to define their requirements and document the objectives before the start deploying the available offerings in the market.  Once they have a clearly articulated strategy for AI, they can begin the process of analyzing their current environment to ensure they understand the existing capabilities.  At that point, they will be well positioned to identify any gaps that need to be filled with additional tools. It’s all about managing the environment in the most effective way.


There’s a lot of information available on AI tools, some from hardware manufacturers and software vendors that want to sell you the latest product. It can feel like getting blasted by a firehose. Many organizations don’t know where to begin with AI because they are inundated with information. Rather than rushing to evaluate AI tools, organizations should take a step back and look at how the tools they have can be applied to low-hanging-fruit projects. Let KST data help you push those projects forward and begin developing a strategy for using AI to better manage the device lifecycle.