Over the last decade, algorithmic developments coupled with increased computation and data resources have led to advances in well-defined verticals of AI such as vision, speech recognition, natural language processing, and dialog technologies. However, the science of engineering larger, integrated systems that are efficient, robust, transparent, and maintainable is still very much in its infancy. Efforts to develop end-to-end intelligent systems that encapsulate multiple competencies and act in the open world have brought into focus new research challenges. Making progress towards this goal requires bringing together expertise from AI and systems, and this progress can be sped up with shared best practices, tools and platforms. This session will highlight opportunities and challenges for research and development for integrative AI systems. The speakers will address various aspects of integrative AI systems, from multimodal learning and troubleshooting to development through shared platforms.