Simon, as the Senior Vice President of Azul’s global channel and alliances, is a seasoned executive with a wealth of experience spanning over three decades in the industry. His expertise encompasses Software Asset Management (SAM), IT Asset Management (ITAM), machine learning, insight engines, data analytics, and information governance. Beginning his career with humble roots as a project manager at Glaxo Smithkline, Simon has since ascended to various executive roles in renowned organizations such as Commvault, Proofpoint, Firemon, Lucidworks, and Nitro. Throughout his career, he has demonstrated a remarkable ability to rapidly develop channel and partnership organizations. Simon’s strategic focus lies in core go-to-market areas, including application modernization, digital workplace, and transformative AI solutions. His leadership has been instrumental in driving innovation and growth within these sectors. As an accomplished industry speaker, Simon is a recognizable figure on the global stage, frequently traveling worldwide to support value-added SAM/ITAM practices. His engagements extend to advising integrator businesses and providing strategic insights to alliance organizations.
Solution Study
Monday, October 06
03:05 pm - 03:30 pm
Live in Amsterdam
Less Details
Java licensing has become one of the most pressing challenges for IT Asset Management (ITAM) and Software Asset Management (SAM) professionals. Evolving terms, complex licensing models, and the hidden sprawl of Java instances across hybrid estates make compliance a moving target. Add to this the rising risk of security vulnerabilities from outdated or unsupported versions, and ITAM/SAM teams are being asked to manage more than contracts—they are now on the front line of risk, security, and cost optimization. Artificial Intelligence (AI) provides a way forward. By applying machine learning models to real-time asset and usage data, AI can detect underutilized or unauthorized Java deployments, predict future license demand, and highlight vulnerabilities long before they become audit findings or incidents. Supervised learning models can forecast licensing trends, unsupervised models can uncover shadow IT or anomalous usage, and reinforcement learning agents can recommend proactive remediation and migration paths. In this session you will learn: