Organisational Change Management in AI-Driven Transformations

Driving AI transformation with traditional change management practices is like flying a spaceship with an old street directory. The terrain has changed, radically. AI doesn’t just tweak processes; it redefines how decisions are made, who makes them, and what roles even mean. To succeed, change leaders must upgrade their navigation tools to match the velocity, complexity, and psychological impact of this new frontier.

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Introduction

Organisational Change Management (OCM) has always focused on guiding people through transitions to achieve business goals; minimising disruption while maximising adoption. In AI-driven transformations, however, OCM must evolve beyond standard practices. AI integration reshapes workflows, decision-making, and roles at an unprecedented scale, demanding strategies that can keep pace and account for this paradigm shift. This article explores why AI changes differ from conventional ones, building on key distinctions:  

• The dual-edged impact on stakeholders,  

• Polarisation determined by individual adaptability, and  

• The relentless pace of both implementation and technological evolution.

With these challenges in mind the primary objective for change in this context is to move individuals from a survival-oriented mindset, characterised by fear, resistance, and a status quo bias, to an open-minded mindset – where people are ready and able to adapt to continuous evolution. This is a transition from asking "How do I survive this change?" to "How do I make the most of this progress?" Creating a culture of new social norms, where continuous evolution is embraced, is crucial for fostering sustainable adoption and is achieved by leveraging psychological principles.

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Why AI-Driven Change Demands Specialised Approaches

AI transformations stand apart from traditional changes, such as process reengineering or mergers, due to their profound, multifaceted implications. Traditional change approaches however can be expanded with practical insights.

First, AI's implications can be polarising; exceptionally positive and negative, varying by individual or group. For executives and innovators, AI promises efficiency gain, automating routine tasks to free time for strategic work and competitive edges, like predictive analytics boosting revenue. Yet, for frontline workers, it often signals job displacement or skill obsolescence, fostering anxiety and resistance. This polarity isn't just perceptual; it's structural. A developer might thrive with AI coding assistants, while a triage level customer service operator faces redundancy. Unlike uniform changes (e.g., a new CRM system affecting all similarly), AI amplifies the differences between roles, requiring OCM to address personalised impacts rather than blanket communications.

Second, the debate over AI stems from adaptability levels. The technology demands rapid learning of tools like Agentic AI platforms or prompt engineering techniques, but not everyone adapts equally. Tech-savvy employees may view AI as empowering, accelerating tasks and sparking innovation. In contrast, those with lower digital literacy experience it as alienating, widening rifts between "adopters" and "resisters." This is not mere preference; adaptability correlates with age, education, and prior exposure, turning AI into a cultural fault line. Traditional OCM often assumes similar starting points, but AI exacerbates divisions, necessitating targeted upskilling and inclusive dialogues to bridge gaps.

Third, the speed of AI-driven change is unprecedented, outpacing technology driven shifts in ways of working like large SaaS platforms or data analytics tools. Organisations must deploy AI solutions amid technology product hype cycles, with pilots turning into enterprise-wide rollouts in months, not years. This velocity leaves little room for gradual adjustment, compressing planning, training, and feedback loops. Compounding this, AI itself evolves rapidly, models like large language systems improve iteratively, with updates rendering prior implementations obsolete. For instance, an AI tool adopted today might require retraining tomorrow due to new capabilities or ethical guidelines. This dual rapidity, fast adoption plus evolving tech, creates a "perpetual beta" state, where stability is illusory, demanding OCM that's agile and ongoing rather than project-based.

These factors interlink: Speed amplifies polarisation, as slower adapters fall behind, while polarised impacts fuel resistance, slowing adoption further. Building on this, effective OCM in AI must prioritise ethics (e.g., bias mitigation), continuous learning, change readiness culture, and stakeholder segmentation to turn potential pitfalls into opportunities.

An analysis of common models show that these models treat change as finite and predictable, failing AI's volatile, divisive nature. They need augmentation: Integrate agile elements for responsiveness, ethical frameworks for impacts, and adaptive learning for divisiveness.

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Limitations of Traditional Change Models in AI Contexts

Traditional change models, while often effective for standard ICT projects, need to be re-framed to address the unique psychological and social shifts caused by AI. An assessment of commonly used models like ADKAR, Kotter, Lewin and newer agile change approaches reveals several recurring limitations when applied in the AI context:

• From Discrete Stages to Continuous Evolution: Traditional models assume change is a linear, sequential process where one step is completed before the next begins. This "one-and-done" approach clashes with the constant evolution of AI. As a result, the models struggle to account for the need for perpetual reinforcement, making it difficult to solidify new behaviours and reach a stable equilibrium.

• The Myth of Uniform Impact: These older models also assume a relatively uniform organisational response to change, often seeking broad buy-in and building coalitions across all stakeholders. This approach fails to account for the polarised effects of AI, which can create significant fear and division. A more effective strategy acknowledges that the impact of AI is not the same for everyone and requires a nuanced approach that addresses these specific, often divergent, concerns.

• Perpetual Change, Not Episodic Events: Traditional models treat change as a discrete, episodic event with a clear start and end. In the context of AI, change is a perpetual state. The sense of urgency is not a one-time phenomenon but a constant reality. This necessitates a shift in perspective, moving away from planned, one-off changes toward a framework that embraces an ongoing state of flux.

• The Challenge of Speed and Scope: While new, faster iterative approaches to change have emerged, many still rely on the fundamental assumptions of traditional models. They often fail to acknowledge a key challenge of AI: neither those managing the change nor those affected by it have a clear view of its full scale and scope. The sheer speed of AI development outpaces the ability of an organisation to fully comprehend and plan for its impact.

In essence, these traditional models struggle because they were built for a different era of change - one where projects had clear endpoints. The unique characteristics of AI, including its speed, non-linearity, and potential for polarising impacts, require a fundamental shift from these foundational frameworks.  This mismatch necessitates a more inclusive approach that can adapt to a perpetually evolving landscape.

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Ways to refine organisational change management in the AI Context

To facilitate this cognitive reframe, organisations need to upgrade from the local street directory to the spacecraft navigation systems - consider moving away from "big bang" implementations and adopt a more nuanced, phased approach that builds trust and normalises AI. Above all, give people time and opportunity to drive change rather than just be driven by it.

• Avoid "Big Bang" Implementations: A sudden, all-at-once rollout can trigger loss aversion and heighten resistance. A phased approach allows people to gradually acclimate to the new technology and its social norms.

• Leverage Early Adopters: Identify and empower early adopters, particularly those with influence. These individuals act as internal champions, demonstrating the benefits and normalising the use of AI. Their positive experiences can help mitigate bias and drive a wider shift in perception.

• Build self-efficacy: Self -efficacy shapes how employees respond to AI change. Those with low self -efficacy will feel overwhelmed, resistant, ‘left behind’ and may withdraw. In contrast, those with high self-efficacy will:

• have a greater willingness to experiment and are more likely to explore new tools and integrate them into work.  

• approach AI change with curiosity, persistence and openness,  

• see Ai as an enhancer of skills and productivity  

• be more likely to encourage peers

• Promote Psychological Safety:  When employees feel safe to experiment, make mistakes, and learn without judgement, AI adoption accelerates,  particularly amongst those initially resisting change.

• Pilot Programs and Phased Rollouts: Implementing small-scale pilots or phased rollouts allows for a controlled environment to build trust. This approach minimises risk, provides valuable feedback, and gives people a low-stakes opportunity to interact with and understand the AI. It is a key tactic for gently nudging individuals toward a more open mindset, making the technology feel less threatening and more a part of the everyday work environment.

Note: This article reflects our original ideas and arguments, with AI tools used to enhance research and refine editing under my direction.

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‍Authors:  

Leo Choudhary, Director. leo.choudhary@scyne.com.au, Leo Choudhary | LinkedIn.  
Jane Fearn, Managing Director. jane.fearn@scyne.com.au, Jane Fearn | LinkedIn
Jess Duffy, Director.  jessica.duffy@scyne.com.au, Jessica Duffy | LinkedIn

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