The pace of technology change, particularly due to artificial intelligence (AI), has accelerated to a point where it might be seen as overwhelming.
Gone are the days when generative AI was regarded as merely a useful chat tool − AI agents are gaining more capabilities by the day, and employees are concerned about potential job losses because of it.
The World Economic Forum projects that AI could displace 92 million roles worldwide by 2030 and could decimate entry-level positions.
In the UK, recent research by King's College London found that 69% of workers and 64% of employers are worried about the economic impact of AI job losses, while in the US, a Reuters/Ipsos poll found that 53% of Americans fear AI could put them or someone in their household out of work.
In South African white-collar workplaces, there is also a heightened level of anxiety around AI, while − paradoxically − the same anxious workforce is embracing the AI tools embedded in productivity suites.
And yet, as the workforce grapples with AI anxiety and fatigue, we can use that very technology to help solve the issue of traditional IT adoption and change management.
The more things change…
Every technology deployment leader has faced this reality: the ultimate challenge of an IT project is not the technical implementation required, it is the change management requirement.
Whenever an organisation rolls out a new SaaS platform or enterprise tool, human beings instinctively cycle through a painful, emotive change curve of shock, denial, frustration and resistance, before reaching competency.
AI becomes a self-service trainer that is always available and never tires of answering questions.
Traditionally, easing this discomfort required a heavy, human-intensive playbook of communication, stakeholder engagement, generic training sessions, rigid PDFs and an overstretched IT helpdesk.
Integrating AI into enterprise IT rollouts transforms the implementation lifecycle from two distinct angles; namely, boosting the efficiency of change management practitioners and fundamentally changing how end-users interact with new systems.
Change management practitioners can use it to develop plans, training materials and personalised communications for various roles faster. It enables them to generate and personalise content at scale, freeing practitioners up to be more productive and hands-on.
AI could also be used to support sentiment analysis on employee surveys and feedback. It does not replace the skills required from change management practitioners, but it can make them more efficient, allowing them to spend more time on high-touch, face-to-face engagements where deep emotional resistance lies.
At the same time, AI directly helps employees learn to trust the technology and adapt during change, becoming a self-service trainer that is always available and never tires of answering questions.
AI acts as a 24/7 on-demand tutor or assistant, allowing users to ask questions, get explanations, and receive step-by-step guidance whenever they need it. They can learn at their own pace, repeat questions and request answers in formats that suit them, such as simpler explanations or diagrams.
Now, instead of searching a 50-page PDF manual on how to file an expense on a new ERP, or clicking through multiple menu options in search of that "Eureka − finally!" moment, an employee simply asks an AI assistant: “How do I submit my travel receipt on X system?” and receives an instant guided walkthrough.
This technical evolution completely disrupts the traditional software training model. In the past, IT projects relied on teaching employees rigid user menus and static button clicks. However, the sheer pace of modern software development means that by the time you train an employee on a specific interface feature, that feature has likely changed.
Therefore, tech-driven change management must pivot from teaching static functionality to teaching prompt engineering, critical thinking and algorithmic scrutiny.
Instead of memorising where a button sits, employees must learn how to direct the software to achieve a business outcome. By training our workforces to master basic prompting and to critically interrogate system responses, we don't just achieve a successful launch for a single IT project; we build an agile workforce capable of adapting to any future software update.
However, harnessing AI to enable change management can become something of a catch-22 situation. If users have not used AI effectively previously, how can they rely on an AI companion to teach them a new platform?
In some cases, this may lead to essentially running two projects simultaneously; firstly, the primary IT deployment, and secondly, the underlying AI adoption project.
Instead of launching a full-scale AI adoption project, leaders can bridge this literacy gap by leaning on micro-learning, such as quick 'show-and-tell' demos of AI in action. Think of it as one-shot prompting the user, instead of the AI agent.
AI doesn't necessarily replace human interaction and engagement in change management − the empathy, active listening and leadership.
But by offloading some of the mechanical aspects of training and the manual creation of content to AI, we free up change practitioners to focus on what they do best: helping humans navigate the future.
Share
