The warped perception around “self-learning” systems leads to a misconception that AI systems necessarily learn over time, automatically because they are "intelligent". Failed implementations generate much frustration as systems cannot fulfill unreasonable expectations. However, this capability is critical for the future. To build resilient business processes and flexible decision models, organizations need to integrate into their AI solution blueprints' capabilities leading to adaptive systems.

Organizations are under pressure to make better business decisions, even as decisions become more complex. Data and analytics leaders must take the lead to apply decision models, ML, BI, optimization, business rules and other AI techniques for maximum benefit. Session includes:
• Trends in decision intelligence
• How to apply decision thinking
• The role of D&A in decision intelligence.

Organizations often struggle with operationalizing ML and AI projects. In this session, we'll focus on the key considerations for operationalization, best practices from around the world and lessons learned from some case studies.