Artificial intelligence (AI) can process massive volumes of asset data faster and more accurately than humans, uncovering patterns, predicting risks, and optimizing costs. So why aren’t more organizations fully leveraging AI for Software Asset Management (SAM) and IT Asset Management (ITAM)?
Modern IT estates are sprawling, hybrid, and constantly changing. Keeping tabs on every license, instance, and contract detail is increasingly beyond human capacity. AI bridges this gap by:
- Continuously analyzing asset usage data to forecast constraints or failures.
- Automating scaling, provisioning, and maintenance tasks.
- Identifying underutilized software and hardware to cut waste.
Yet many organizations still underutilize AI in ITAM/SAM. Why? Often, business leaders struggle to define where AI can add value, and teams lack expertise in choosing and training the right machine learning (ML) models.
Key challenges in ITAM/SAM
Despite its clear benefits, SAM still faces these enduring challenges:
- Incomplete asset visibility — Shadow IT and fragmented estates hide critical data.
- Siloed data — Integrating asset data across cloud, on-prem, and distributed teams is complex.
- Manual processes — Repetitive tasks drain time and increase human error.
- Security risks — Outdated or unpatched software exposes vulnerabilities.
- License compliance — Mismanaged licenses can lead to costly audits and penalties.
- Unreliable usage tracking — Inaccurate data undermines planning and reporting.
Zylo’s 2024 SaaS Management Index found that companies waste an average of $18 million annually on unused software — an amount that keeps growing.
How AI solves these challenges
When properly deployed, AI can tackle all six pain points:
| Learn More | AI-Powered Solution |
|---|---|
| Incomplete visibility | Autonomous network scanning detects overlooked devices and software for a real-time, complete asset map |
| Siloed data | AI integrates and normalizes disparate data streams, providing a unified asset view across environments |
| Manual processes | Intelligent automation handles discovery, tracking, reporting, and anomaly detection — freeing teams for strategic tasks |
| Security risks | AI continuously monitors for outdated software and unpatched vulnerabilities, prioritizing remediation |
| License compliance | Predictive models flag potential overuse or underuse, and ensure organizations stay aligned with dynamic licensing terms |
| Unreliable usage tracking | Machine learning identifies shadow IT and usage anomalies, improving accuracy of audits and forecasts |
Examples: AI in leading ITAM tools
Many ITAM/SAM platforms already embed AI and ML:
- ServiceNow ITAM/SAM — Uses ML for license compliance trends and virtual agents for workflow automation.
- Flexera One — AI delivers insights on software optimization and cost-saving opportunities.
- Snow Software — ML identifies shadow IT and automates anomaly detection.
- Ivanti Neurons for ITAM — AI bots provide real-time asset discovery and health scoring.
- USU Software Asset Management — Predictive license optimization and AI-powered dashboards mitigate risk.
- BMC Helix Discovery — ML supports dependency mapping, anomaly detection, and automated change impact analysis.
Understanding AI learning models in ITAM/SAM
Different AI learning models bring different strengths:
- Supervised Learning: Models learn from labeled historical asset data to predict future trends (e.g., license renewal patterns).
- Unsupervised Learning: Discovers hidden patterns in unlabeled data, such as unexpected usage spikes or shadow IT.
- Reinforcement Learning: AI agents autonomously learn optimal actions through trial and error — for example, dynamically reallocating licenses based on usage changes.
- Narrow AI vs. General AI: Today’s ITAM/SAM solutions use narrow AI — highly specialized, task-focused models designed to automate specific workflows and decision points.
The quality of outcomes depends on good training data — without it, even the best model can’t deliver accurate recommendations.
Example: JVM Inventory as high-value training data
Azul’s JVM Inventory in Azul Intelligence Cloud illustrates this perfectly:
- It gathers real-time data on all in-use Java Virtual Machines (JVMs) across an organization’s environment — including Oracle Java.
- It enables organizations to understand actual usage patterns, plan migrations from Oracle Java, and maintain compliance.
- This runtime data feeds AI/ML models, enhancing predictive insights about Java usage, security risks, and licensing costs.
This empowers ITAM and SAM teams to negotiate more effectively with vendors and make faster, data-driven licensing decisions.
Learn more in a live webinar
AI is transforming IT Asset Management (ITAM), but what’s the real value behind the hype—especially for organizations managing complex, Java-heavy environments? This session explores how AI is helping ITAM teams automate discovery, improve compliance, and optimize costs, with a special focus on Java. From identifying shadow IT and managing open-source Java components to forecasting licensing risks and support costs, AI brings clarity and efficiency to one of IT’s most challenging domains. Register now.
Conclusion
AI is already transforming SAM and ITAM from manual, error-prone processes into proactive, intelligent operations. By understanding the right types of AI models and providing them with high-quality, real-world data like JVM Inventory, organizations can:
- Reduce costs
- Minimize risk
- Optimize license usage
- Free teams to focus on strategic growth
Explore Azul Intelligence Cloud to see how your team can fully harness AI and ML for SAM.
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