(Panel) Return on Technology: Measuring the Success of AI, XR & Digital Twins

While many organizations have validated AI, XR, and digital twin use cases, translating early success into sustained investment – especially amid economic uncertainty and workforce upheaval – requires clear, defensible ROI. In this session, enterprise leaders share best practices for defining, measuring, and communicating the business value of emerging technologies. Topics include prioritizing measurable use cases, capturing both quantitative and qualitative impact, managing stakeholder expectations, and more:

• Success criteria: Establishing baselines; aligning technical metrics with business KPIs (productivity, revenue, cost)
• Use case selection: Prioritizing use cases with measurable outcomes, accessible data, and low integration overhead 
• Data capture & KPIs: Capturing reliable data, connecting to analytics systems, and tracking outcomes like time-to-proficiency, error reduction, and downtime avoidance
• Cost modeling: Evaluating total cost of ownership vs. projected savings and revenue 
• Indirect value: Incorporating qualitative factors like employee experience, safety, and engagement 
• Benchmarking: Comparing results against legacy processes, alternatives, and industry benchmarks 
• Communicating the business case: Translating results into financial terms (ROI, payback period, NPV)
• Managing expectations: Setting realistic timelines for early results vs. scaled impact
• Organizational enablers: Role of executive sponsorship, internal champions, and cross-functional alignment