Contrary to fears about AI replacing ITAM professionals, the conference revealed AI is becoming the ultimate sidekick. When implemented thoughtfully and with adherence to organizational policy and governance, AI handles repetitive tasks that have traditionally consumed too much time:

• Automatically reconciling inventory data from multiple sources
• Identifying optimization opportunities in complex licensing metrics
• Running predictive scenarios for future planning
• Streamlining compliance reporting processes

This shift elevates ITAM professionals from "the licensing people" to strategic business partners. With AI managing data-crunching, teams can focus on high-value activities like negotiation strategy and aligning technology investments with business goals.

For successful AI integration, start with a specific pain point, keep humans in the loop for compliance decisions, establish feedback mechanisms, and invest in team upskilling. The biggest wins come from viewing AI as a partnership between human expertise and machine capability.

Open-source software discussions highlighted an urgent security message: most enterprise applications today are built with OSS components that require regular updates as vulnerabilities are discovered. The window between vulnerability disclosure and exploitation continues to shrink dramatically.

Creating sustainable update processes requires:

• Maintaining a complete inventory of OSS components
• Implementing automated scanning tools in your Continuous Integration (CI) and Continuous Delivery/Deployment (CD) pipeline
• Establishing clear update protocols
• Developing emergency response procedures for critical vulnerabilities

Beyond avoiding breaches, strong OSS management offers competitive advantages: reduced technical debt, lower crisis management costs, improved security assessments, and more stable systems with less unexpected downtime.

While compliance messaging often falls flat outside IT and legal departments, successful ITAM teams are reframing conversations with executives around time and resource savings—addressing the universal business challenge of doing more with less.

Effective leadership persuasion strategies focus on concrete operational benefits:

• Faster software request fulfillment
• Hours reclaimed from manual tasks
• Quicker audit response times
• Streamlined employee onboarding

To build this time-saving narrative, document current processes and identify bottlenecks, create small pilot projects demonstrating improvements, build ROI models accounting for both hard costs and time savings, and customize messaging for different stakeholder groups.

Sessions on Effective License Position (ELP) preparation highlighted the critical importance of data quality. Poor data creates a domino effect of exhausting hours spent on cleanup, questionable purchasing decisions, compliance exposure, and damaged credibility.

Forward-thinking organizations are implementing:

• Robust discovery tools that continuously maintain accurate deployment data
• Standardized processes for capturing license entitlements
• ITAM-specific data governance
• Regular data quality checks with clear remediation ownership

With on-demand access to quality data, ELP preparation becomes routine rather than dreaded, leadership gains confidence in compliance positions, vendor audits are mitigated and collaborative, and the focus shifts from reactive firefighting to proactive license optimization.

As AI tools become increasingly accessible, organizations face new risks from employees (and even third-party stakeholders) experimenting with public AI tools using work-related information. This "Shadow AI" presents four distinct risks:

• Ransomware: AI-enhanced malware that encrypts a victim's data and demands a ransom payment for its decryption. Attackers often use it to hold data hostage and extort money from individuals or organizations.
• Data Loss: Employees inadvertently sharing confidential information with third parties
• Security Vulnerabilities: Consumer AI tools lacking enterprise-grade controls
• Unplanned Licensing Costs: "Free" usage triggering paid thresholds or violating terms of service

Developing balanced AI governance requires clear policies on approved tools, data classification guidelines, technical controls to monitor adoption, and procurement/legal review processes. The most effective approaches create sanctioned pathways for responsible AI adoption through enterprise-approved alternatives, fast-track review processes, and training on responsible usage.