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Sep 22, 2026

8 minute read

AI change management: A human-centric approach

Change management has long covered two familiar kinds of change: the rollout of new tools and technologies, and broader human-led transformations such as leadership changes and restructuring.

But that distinction starts to blur as artificial intelligence becomes woven into the fabric of organizations. Although AI is software, it has a unique capacity for open-ended, context-dependent work that involves collaborating with people. This raises the question: Should we simply treat AI like software to be deployed, or should we think of it as an active participant in our workflows whose role needs to be defined?

The instinctive answer is just to treat it like software. Organizations often try to retrofit AI into existing infrastructure and processes, assuming it can slot into the same guardrails, integrations, and workflows that support CRM systems, ERP platforms, and other enterprise software.

But maybe this isn’t the right approach. Maybe AI isn’t just a tool to be installed; it’s a participant to be onboarded. And when we force it into software-shaped boxes, we miss the opportunity to leverage its unique strengths — pattern recognition, scalability, and adaptability — in ways that complement human judgment rather than compete with it. AI isn’t just a tool to be installed; it’s a participant to be onboarded.

In this post, we’ll look at what AI change management involves and the key considerations for enterprises as AI becomes a more integral part of day-to-day work. What is AI change management? AI change management is the work of guiding an enterprise through the organizational changes required to adopt AI at scale, from preparation through to day-to-day use. It includes understanding how AI changes roles and responsibilities, helping employees develop new ways of working, communicating new expectations, and adapting workflows and oversight as adoption progresses.

Those changes may mean employees spend less time producing work themselves and more time directing AI, working with its outputs, or focusing on tasks that depend on human expertise. Why does AI require a distinct approach to change management? Consider the difference between the launch of a new sales platform and the appointment of a new CEO. A new sales platform usually involves training sessions, a phased rollout, and minor process adjustments around a system whose role and behavior are relatively well understood. Meanwhile, a new CEO can have much broader and less predictable implications for an organization’s culture, priorities, division of responsibilities, and ways of working.

AI presents a distinct change-management challenge because it combines elements of both kinds of change. It represents both the adoption of a new technology and a broader shift in how work is distributed across an organization. AI can reshape roles, workflows, and decision-making For AI to function as a collaborator, enterprises need to design workflows that accommodate both human and artificial intelligence.

On the AI side, that might mean providing AI with the relevant organizational and task context much as you would when onboarding a new employee. It can also mean building human-in-the-loop mechanisms that allow AI to ask for missing information or escalate cases that require human judgment or approval.

On the human side, it might mean creating workflows where AI can act as a thinking partner rather than simply a tool for executing delegated tasks. For example, AI might help a person analyze information, explore different options, or identify relevant patterns as the work progresses. For judgment-heavy tasks, AI can synthesize evidence and surface options and trade-offs while leaving the final decision to a person. The point isn’t to pretend AI is human, but to recognize that its effectiveness can depend on how well it’s integrated into human-led workflows.

In these new human-AI workflows, employees start to act like managers of AI-assisted work, setting direction, assessing quality, and deciding when human input is needed. However, this shift introduces a risk: mistaking the speed and volume of AI-generated outputs for quality. This can result in “AI slop”: superficial or low-impact work. Employees therefore need to adopt a manager mindset, prioritizing the quality and impact of...