4 ways AI is transforming IT asset management

Due to the uncertain nature of demand, managing asset inventory through traditional means, by setting stock rule thresholds, doesn’t always work in enterprise-class organizations. To approach the problem more systematically, IT managers can use machine learning techniques to make monthly demand predictions for each asset model.

To do that, machine-learning models must be trained on two types of data that factor most heavily into demand forecasting: data generated from HR processes and asset retirement data. On the HR side, that includes information on job openings, new hires, position changes, and terminations. With asset management, the models need a range of asset characteristics (including equipment models, types, and retirement dates), as well as stockroom data.

The training data helps the models spot tendencies and see how they are interrelated historically with asset demand. Then they produce demand forecasting models that continually “learn” from ongoing HR and asset retirement trends. If current stockroom data predicts you won’t cover demand in a given quarter, managers can use the tools to automatically create purchase orders to ship needed assets. (Read on to see how AI can manage overall procurement.)

With the ability to better predict asset demand, managers can slash downtime costs by improving asset provision, which in turn helps boost service quality.

As corporate spending on business software rises, so does the need for companies to better manage license distribution and vendor audits. Here, too, machine-learning tools can make a difference. They can more accurately recognize software usage patterns to optimize license allocation.

To make it happen, companies must compile the right training data sets. Those include the total list of apps, all user entitlements and roles, time spent in apps, features used, and other data points. Data engineers then help the models learn by identifying which software usage pattern they deem optimal. For example, they might look at time spent in the app or the number of features, but also take into account possible exclusions from the rule: when people are on vacation, sick leave, etc.

The ML models can then segment users according to their roles in various applications to adjust optimal software usage to the specifics of each group. Then, after being fed new incoming data, the models can spot behavior deviations and make corresponding suggestions.

For example, if a particular app isn’t mission-critical and is rarely used, AI can recommend handing off the license to someone else. Or, if some users aren’t taking advantage of core features, AI can recommend swapping their license for a more appropriate one.

Ultimately, the tools can help reduce IT license costs by making more efficient use of software investments.

Along with predicting asset demand, machine learning tools can manage asset procurement with equal efficiency, adding another layer of cost savings. For hardware assets, ML tools organize purchased assets by class, model category and model.

They look at each group’s purchase-order details to identify orders that had the most favorable price and delivery costs. Then they search for dependencies in the data to understand which specific variables —vendor discounts, order amounts, or shipment locations— were most influential. Once those are determined, the models recommend the ideal purchasing specs for each asset group. They can also merge multiple purchase orders or recommend switching vendors when they see a cost-saving opportunity in new data.

Ultimately, these tools reduce hardware purchasing costs by enabling a smarter and more transparent buying process.

As demands and workloads for ITAM teams increase, one thing seems obvious: humans can’t do this job efficiently enough on their own. IT teams can leverage the power of machine learning and AI to manage the ever-increasing complexity of IT asset management.