Driving Change: The Human Side of AI Adoption
No matter the industry, one universal truth remains: AI adoption is as much about managing change with people as it is about deploying technology. A shiny new AI tool won’t deliver value if employees don’t use it, or worse, actively resist it. That’s where change management comes in.
Frameworks like Prosci’s ADKAR model – which stands for Awareness, Desire, Knowledge, Ability, and Reinforcement – are designed to guide individuals through change. They’re highly relevant for AI projects, which often require people to rethink how they do their jobs.
Let’s break down ADKAR in the AI context:
First, employees need Awareness of why the change (AI) is needed – e.g. “Our competition is using AI to serve customers faster, and we risk falling behind”.
Next, they need Desire to support and participate in the change – perhaps by understanding WIIFM (“what’s in it for me”), such as reducing drudge work or opening career opportunities.
Then comes Knowledge – training and education on how to use the new AI systems or work alongside them.
That must be followed by Ability, the actual hands-on capability to apply AI tools effectively in one’s role (which might mean practice, coaching, or process adjustments).
Finally, there’s Reinforcement – ongoing encouragement, feedback, and incentives to make the change stick, so people don’t revert to old habits once the novelty wears off.
Organisations that approach AI adoption with a structured change model like ADKAR tend to navigate the transition more smoothly. Why? Because they address the human fears and habits that can otherwise derail the best technology rollout. For instance, one common obstacle is employee resistance – fears that AI will replace jobs or that it’s too hard to learn. A good change management plan tackles these head-on through transparency and involvement. Leaders might communicate early and often about how “AI will augment rather than replace human roles,” and back that up by showing employees new career paths and providing reskilling opportunities. They invite team members to pilot the tools, give feedback, and even help improve the AI (so it becomes a collaboration, not a threat).
Critically, organisations must also be ready to invest in building AI fluency. It’s telling that nearly half (47%) of employees who use AI say their organisation has not offered them any training on how to use AI in their job. That’s a recipe for frustration and fear. To counter this, companies are starting to roll out AI training programs across all levels – not just for tech teams, but for general staff and managers, demystifying concepts like machine learning and data science. When people feel supported to develop new skills, their “Desire” and “Ability” to adopt AI both rise.
Finally, change management reminds us to celebrate wins and reinforce the new ways of working. Did an AI tool help the sales team save 100 hours last quarter? Share that story, recognise the people involved, and tie it back to the company’s purpose. This creates a positive feedback loop where employees see AI not as a flavor-of-the-month initiative imposed from above, but as an evolving part of their work life that they have a stake in. By focusing on the “people side” of AI adoption, using models like ADKAR to create awareness, desire, and ability, companies vastly increase the odds that their AI investments actually deliver value. After all, “the ultimate value [of AI] will result from people adopting and using the solutions,” as Prosci’s Chief Innovation Officer Tim Creasey notes.
