This century is the major contributor to the change and the growth of innovation ecosystems is Artificial Intelligence (AI). This also changed the time and way of thinking and approaching the company market. Moreover, it also changed the ways to create value. By doing so, it has enabled the co-creation of knowledge among industries. The emergence of AI technologies like machine learning, natural language processing, and intelligent automation are opening up countless opportunities for companies to optimize their processes, boost their innovation and enhance their productivity like never before.  This study takes a multi-disciplinary approach and covers the fields of strategic management, information systems, and innovation studies. This will help us understand how AI use acts as a mediator between internal R&D intensity and sectoral characteristics leading to business performance. Based on a cross-sectoral dataset of 120 companies from manufacturing, services, IT/technology, and healthcare, the study employs descriptive statistics, correlation, and regression analyses to quantify the impact of its adoption on innovation output and productivity gains.  The findings reveal that the use of that leads to greater productivity and moderate increase in innovation output to a large extent and to a moderate extent respectively. The company’s leaders are thus seeking to leverage AI as a technology, as well as a strategic organisational capability. This paper provides useful theoretical input on innovation ecosystems while offering a bounty of useful and feasible ideas to business executives, policymakers and practitioners who are keen to build mission-oriented AI-innovation ecosystems..