Performance under pressure is paramount, yet most organizations allocate 90% of their training budgets to knowledge delivery rather than the practice that drives behavioral change. Since 70% of skill acquisition occurs through experience, many employees enter high-stakes interactions without the muscle memory necessary to perform.

While 1:1 role play is the gold standard for shifting behavior, it cannot scale without exhausting manager bandwidth. In this session, we will demonstrate how AI practice environments can make skill development repeatable and measurable. Using sales as a primary case study, we will illustrate how to connect practice, feedback, and readiness data within a single system to ensure new skills become permanent habits.

We will also show you how to build realistic scenarios in minutes. This ensures that your training remains as agile as the business's goals. Although our focus will be on sales, this model applies to any team where field performance is the primary objective.

Session Takeaways

• Identify readiness gaps: Move beyond course completions to track behavioral markers that measure actual skills.
• Remove training and coaching bottlenecks: Let AI handle front-line feedback so managers and trainers can focus on high-level mentoring.
• Certify field readiness: Replace subjective scoring with data-backed scores that validate skills before live application.
• Standardize quality practice: Ensure every learner receives consistent, objective feedback through realistic scenarios.
• Build with agility: Create custom practice environments in minutes to adapt to new messaging instantly.

William brings deep expertise in AI-driven learning and talent development. He is also an experienced public speaker who has presented at international conferences and executive forums.
 
William Rintz is a specialist in AI-driven talent strategy, with a focus on bridging the gap between enterprise AI adoption and workforce development. Holding a master’s degree in educational studies, he leads global initiatives at UMU, where he helps organizations unlock the full potential of their people through AI-powered learning. As a lecturer for UMU’s AI Talent courses, William combines practical frameworks with the latest research to equip leaders with the tools needed to build AI-ready workforces. His work sits at the intersection of learning science, digital transformation, and performance enablement.