How to prepare IT asset management for AI
AI in IT asset management can already be used to support tasks such as device recommendations, end-of-life decisions and other parts of the IT asset lifecycle. But these use cases depend heavily on the quality of the underlying data, integrations and processes.
If asset information is spread across different systems, lifecycle processes are unclear or basic automation is still missing, adding AI can easily create more work instead of reducing it. This is why the first step should not be building an autonomous AI agent. It should be making sure the IT asset management foundation is ready for AI in the first place.
Bring your IT asset data together
Typically, information related to IT assets is spread across several systems and it is often edited multiple times during the asset's lifecycle. Your ITAM system may contain the main asset record, while the UEM platform holds technical information about the device, such as its operating system, compliance status and hardware. Purchase and delivery details come from suppliers, while leasing and financing partners provide contract information, costs and important lifecycle dates.
If the underlying data is fragmented and disconnected, building AI solutions on top of it will not solve any problem or speed-up the work. This is why centralising the relevant asset data is one of the most important parts of the foundation. It does not mean replacing Intune, Entra ID, supplier systems or financing platforms with one tool. Those systems still have their own roles in the wider IT asset management ecosystem. The aim is to make the information needed for lifecycle management available in a structured way, so both people and AI can work with it.
One way to do this is to bring the relevant data together in your ITAM solution through integrations. Instead of connecting AI separately to several different systems, it can work with the data that has already been collected in one place.
Document your IT asset lifecycle for AI
On top of structured data, AI also needs a semantic layer that gives the information and processes the needed context. For example, an AI agent needs to understand what different fields, statuses and relationships mean, and what normally happens at different stages of the lifecycle.
Another thing that is often lacking is a clear process. For people the process may be obvious. They know who should be contacted, which system needs to be updated and what normally happens when a device changes user or reaches the end of its contract. The problem is that this knowledge is not always documented.
An AI agent cannot work in the same way as a person without clear documentation, at least not if you want reliable outcomes. If the agent is expected to take part in a lifecycle process, the different steps, responsibilities and decision points need to be clearly defined. This is why mapping and documenting the lifecycle is as important as the asset data you provide to it.
Even if you do not plan to build any AI-powered processes yet, this work is still useful. It helps identify which parts of the lifecycle can already be handled with integrations and normal automation, and where AI could add something that those approaches cannot.
Do not use AI for something a simple rule can handle
Not every process needs AI. If a task can be handled reliably with a fixed rule, normal automation is usually the better choice because the outcome is predictable and easy to control.
A reminder sent 30 days before the end of a leasing contract is a good example. The same applies to alerting IT when an employee has left the company but still has a device assigned to them. In both cases, the trigger and the required action are clear, so there is little benefit in creating an AI agent to do anything.
AI becomes more useful when the answer depends on several pieces of information that can change over time. Device replacement is one example. A simple rule can tell you that a laptop is three years old, but age alone does not tell you whether it should be replaced. Device health, security status, performance, contract information and the way the employee actually uses the device can all affect the decision.
Our tip is simple: use fixed rules and normal automation when the right action is clear in advance. Involve AI when you need to make a fixed process or automation more dynamic by using changing data from multiple sources to decide what should happen next.
Give AI agents only the access they need
Security and governance become more important once AI moves from analysing information to taking action. An agent that produces a report has a very different risk from one that can order hardware, update records or remove information from a system.
Permissions should therefore follow the task. An agent that only needs to read asset information does not need other permissions. If it needs to update one part of a record, there is no reason to give it permission to change or delete everything else.
In many cases, the safest approach is to let the agent prepare the action without being able to complete it on its own. It can gather the relevant information, analyse the situation and suggest what should happen next, while the final approval or system change remains with IT.
Start with a clear ITAM use case
Before deciding what kind of additional AI solution or agent to build, start by defining what you actually want to achieve. The outcome should be concrete. Once the wanted outcome is clear, it becomes much easier to understand what data and actions are needed. This also helps avoid starting with AI simply because it is possible.
Keep in mind that preparing your IT asset management for AI is an extension of work you should already be doing. Relevant data needs to move between systems, lifecycle processes need to be documented, and predictable steps should be automated with reliable rules. Only when the foundation is in place should AI be added where basic automation reaches its limits.
Want to explore the topic more? Watch our How to use AI in IT asset management webinar here.
Written by Mikko Lehmus
Mikko is the CEO of IDR and has gained a comprehensive understanding about managing the lifecycle of IT devices, all the way from purchasing to recycling. Mikko is on a mission to minimize manual labor by automating as many ITAM processes as possible.