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Artificial Intelligence (AI) technologies are transforming many industries and our society. While both academia and industry consider AI closely intertwined with innovation, we have limited knowledge of AI’s opportunities and challenges in the business innovation context. This dissertation seeks to address this gap (i) by proposing a novel construct to identify strategically innovative firms; (ii) by examining how deep learning (DL)-based AI capabilities affect knowledge innovation; and (iii) by investigating the economic impacts of service robotics in a customer-facing service industry. In the first essay, we propose a novel firm-level construct, Strategic Competitive Positioning (SCP), to identify distinctive strategic positioning (i.e., first-movers, followers) and competition relationships. Drawing on network theory, we develop a structural hole-based, dynamic, and firm-specific SCP construct. Using a large dataset of 10-K annual reports from US public firms, we demonstrate the value of the proposed measure by examining the impact of SCP on subsequent IPO performance. In the second essay, we study the impact of DL capabilities on exploration to determine how AI’s value creation can facilitate knowledge innovation. Drawing on the theories of organizational learning and path dependence, we theorize how DL capabilities can help firms overcome path dependence and pursue exploration. The findings show that a firm’s DL capabilities have a positive impact on exploration, and conventional innovation-seeking approaches negatively moderate the positive impact of DL capabilities on exploration. In the third essay, we examine the economic impacts of service robots, embodied AI technologies with physical presence, in customer-facing restaurants. The empirical findings suggest that service robot adoption increases restaurant performance, specifically through improved dining experience of existing customers. By distinguishing two forms of managers’ intent in adopting service robots, we further find that the adoption with collaboration intent positively affects perceived service, management, and atmosphere quality and that the adoption with replacement intent positively impacts perceived atmosphere quality only. In sum, this dissertation makes a significant contribution to the literature on AI and innovation by enhancing our understanding of the opportunities and challenges regarding AI in the business innovation context.