This paper explores the critical transition from Generative Artificial Intelligence (GenAI) to Innovative Artificial Intelligence (InAI). While recent advancements in GenAI have enabled systems to produce high-quality content across various domains, these models often lack the capacity for true innovation. In this context, innovation is defined as the ability to generate novel and useful outputs that go beyond mere replication of learned data. The paper examines this shift and proposes a roadmap for developing AI systems that can generate content and engage in autonomous problem-solving and creative ideation. The work provides both theoretical insights and practical strategies for advancing AI to a stage where it can genuinely innovate, contributing meaningfully to science, technology, and the arts.

Artificial intelligence and machine learning are increasingly automating human tasks, but it's not all bad news for us. In fact, among the first areas where this advanced technology is already adding value is AI marketing and conversational commerce. Intelligent systems are helping businesses more effectively connect customers in important touch points ranging from marketing and sales to customer support. The irony of it is that AI can help make business personal again via "extreme personalization", where systems and their human counterparts, learn to deliver meaningful value to individual customers at scale through each engagement over time. But to do so requires more than intelligent technology, it requires a change in executive mindsets to shift the course of AI from scaling one-to-many customer experiences to that of customer-centered extreme personalization.