Creating artificial intelligence (AI) driven operating model in incumbent firms: challenges faced by the leaders
Lead Research Organisation:
University of Cambridge
Department Name: Engineering
Abstract
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.
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.
Organisations
People |
ORCID iD |
Letizia Mortara (Primary Supervisor) | |
Pradeep Debata (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/T517847/1 | 30/09/2020 | 29/09/2025 | |||
2436337 | Studentship | EP/T517847/1 | 30/09/2020 | 30/07/2026 | Pradeep Debata |