As IT Asset Management (ITAM) evolves to meet the demands of increasingly complex software environments, industry leaders will converge at IAITAM ACE 2025 to discuss advanced methodologies for compliance, cost optimization, risk mitigation, and IT asset lifecycle management.
Open iT, a pioneer in software license management for engineering and specialty applications, will be participating at IAITAM ACE 2025, taking place on April 22-24, 2025, at the M Resort & Spa, Las Vegas, Nevada.
Insight-Rich Sessions to Drive License Management Efforts
Open iT will present in three speaking sessions that delve into leveraging the latest innovations to further enhance software license optimization:
- Leveraging License Utilization Analytics to Cut Recurring Software Spend
- True Active Usage: Optimizing Consumption-Based Licensing
- Optimizing Software Asset Management with AI and Machine Learning
These sessions will provide deep insights into optimizing high-value engineering software investments through advanced analytics, data-driven decision-making, and AI-driven automation.
Leveraging License Utilization Analytics to Cut Recurring Software Spend
Recurring software expenditures continue to drive IT budget inflation, particularly within engineering and R&D environments where specialized applications constitute a significant portion of software spending. As software vendors shift toward subscription-based, consumption-based, and hybrid licensing models, organizations struggle to balance the need for technological advancement with cost containment.
License utilization analytics delivers quantifiable intelligence on how engineering applications are consumed at a granular level, exposing:
- True peak vs. allocated capacity to eliminate unnecessary license holdings
’ - User-based efficiency metrics to optimize allocation and provisioning
- Idle-time analysis to identify underutilized or wasted licenses
By integrating multi-source usage data, organizations can model various licensing scenarios, implementing just-in-time renewals, automated reclamation, and predictive demand forecasting to curb redundant expenses while maintaining operational integrity.
True Active Usage: Optimizing Consumption-Based Licensing
The proliferation of consumption-based pricing models introduces both cost-saving opportunities and hidden inefficiencies. While these models promise flexibility, a lack of true active usage visibility often results in organizations overpaying for inactive or inefficiently utilized licenses.
True Active Usage addresses these inefficiencies by providing insights that distinguish between passive license occupancy and real, productive engagement. Metrics captured include:
- CPU load and process execution times to differentiate active computation from idle states
- I/O operations, keyboard, and mouse activity tracking to validate human engagement
- Session-based analytics to detect habitual inefficiencies such as license hoarding and inactive session retention
By integrating these insights into an automated license harvesting and allocation framework, organizations can:
- Train end users to adopt responsible license utilization behaviors
- Identify candidates for automation-driven license harvesting
- Implement real-time policy enforcement to terminate inactive sessions dynamically
As engineering software vendors refine their consumption-based pricing models, organizations without robust active usage analytics will continue to pay for non-value-generating license instances. Leveraging true active usage tracking ensures that costs align with actual productivity, rather than mere license occupancy.
Optimizing Software Asset Management with AI and Machine Learning
The introduction of artificial intelligence (AI) and machine learning (ML) into IT Asset Management (ITAM) and Software Asset Management (SAM) frameworks enables a paradigm shift from reactive cost control to proactive optimization and intelligent automation.
- Pattern recognition & anomaly detection: AI-powered analytics autonomously identify license utilization deviations, uncovering waste and inefficiencies before they escalate.
- Predictive renewal optimization: ML models analyze historical utilization trends and external factors (e.g., project cycles, user behaviors) to forecast future software needs, ensuring data-driven renewal decisions.
- Automated compliance assurance: Intelligent systems map software entitlements against actual consumption to prevent unintentional over-licensing or non-compliance risks.
- Dynamic provisioning & reallocation: AI dynamically redistributes underutilized licenses based on real-time demand, maximizing software availability without increasing costs.
As AI/ML capabilities mature, enterprises must integrate these technologies into their SAM strategies to maintain competitive cost efficiency and ensure compliance in an evolving IT landscape.
See You at Our Speaking Sessions!
For IT asset managers overseeing high-cost engineering applications, attending these sessions will provide the strategic technical depth required to transform software licensing strategies from reactive cost containment to predictive, data-driven decision-making.
Drop by Booth #42 to explore our solutions, connect with our team, and discover how we can turn your software licenses into valuable business assets.
See you at IAITAM ACE 2025!
