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.