Leadership Behavioral Integration in the AI Era: The Root–Reset–Rise Framework
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) is transforming organisations by accelerating decision cycles, increasing informational density, and expanding behavioural visibility. While these technologies enhance analytical capability, they do not resolve a persistent leadership challenge: the knowing–doing gap between leaders’ intentions and their enacted behaviour under pressure. This article introduces the Root–Reset–Rise framework, explaining how leaders sustain behavioural alignment and credibility in AI-augmented environments where technological acceleration intensifies integration strain. The article develops a conceptual framework through integrative theory synthesis drawing on identity theory, behavioural integrity, self-regulation, emotional regulation, recovery science, and institutional theory to explain why leaders struggle to translate knowledge into consistent action and how technological acceleration amplifies this challenge. It argues that AI does not correct leadership weakness; it amplifies the conditions under which behavioural misalignment occurs. As decision tempo and informational inputs increase, leaders experience greater cognitive load and integration strain. The Root–Reset–Rise framework explains how leadership stability can be sustained through three reinforcing capabilities: Root, anchoring leadership identity through values, habits, and emotional stability; Reset, interrupting behavioural drift through recalibration practices such as reflection and recovery; and Rise, institutionalising credibility through modelling, norm formation, and structural reinforcement. By positioning AI as a structural amplifier of the knowing–doing gap, the model provides a novel conceptual explanation of leadership effectiveness in AI-accelerated organisations.
Source: Businesses
Author: Kate McCombs
Institution: Samford University
Language: English
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7203920773