Creating artificial intelligence (AI) driven operating model in incumbent firms: challenges faced by the leaders

Disruptive innovation theory is still evolving and while there are some strong conceptual pillars, the theory doesn't explain examples such as UBER, TESLA and other AI-driven business and operating models well. Iansiti and Lakhani attempt to expand the theoretical understanding of competition in this direction and argue that AI-driven operating models could translate into the emergence of completely different kind of firm which can fundamentally alter industries and the nature of competitive advantage (Iansiti and Lakhani, 2020). A further theoretical framework is dynamic capabilities (Teece, 2014). Teece posits that dynamic capabilities represent higher-order activities by the firm which enable the firm to direct its ordinary activities towards higher pay-off endeavours. The dynamic capabilities framework is ambitious as it aims to create a general framework to understand the firm-level competitive advantage and associated value creation and maintenance. It argues that dynamic capabilities combined with strategy and non-imitable resources of the firm provide a competitive advantage to the firm. But how leaders make decisions amid AI-induced disruptions remains under-researched.

While theories on competition provide a relevant framework to understand and deal with AI-driven operating models for incumbent firms they don't explain (Christensen et al., 2015) or is very light touch (Iansiti and Lakhani, 2020; Teece, 2014) on how the micro-foundations of leaderships actions about sensing and responding is fundamental to gaining competitive advantage. My research will provide insights into this under-researched but hugely important and consequential space in micro-foundations of leadership actions, in reference to responding and adapting to the AI-driven operating model within incumbent firms.