Published 5 August 2026
For a long time, organisational change followed a familiar rhythm. A new system was selected, a project team was formed, a plan was built, and people were guided through communications, training, champions, launch dates and support models. It was rarely simple, but there was usually a sequence people could follow.
That rhythm is changing quickly. GenAI has made it much easier to move from an idea to a first draft, a rough plan or a working version of something. The workflows being disrupted are not just administrative tasks. They are the everyday moments where work takes shape: turning meeting discussions into decisions, customer feedback into insights, policies into practical guidance, and early ideas into usable plans. AI is changing work faster than many organisations can change around it.
This is what makes AI-enabled change different in practice. Change no longer arrives neatly in waves. It is constant, layered and uneven. Some teams are experimenting every day, some are cautious, and others are waiting for clearer direction.
At Engage Squared, we are seeing this pattern play out with organisations adopting Microsoft 365 Copilot and AI-enabled ways of working. Adoption is moving quickly, but the real work is helping people build the confidence, judgement and habits to use AI well in the flow of work.
For leaders and change practitioners, this is where the real work starts. AI-enabled change cannot be managed as a one-off rollout. It needs to be treated as an ongoing shift in how work is designed, governed, led and learned. Good change practice still matters, but it needs to be practical, responsive and grounded in what people are experiencing day to day.
In this blog, Engage Squared consultant Kasey Drinnan explores why AI adoption is only the beginning of organisational change, and what leaders, change practitioners and teams need to do to build confidence, governance and new ways of working in an AI-enabled world.
AI is compressing the change cycle
This is not the same as a typical technology rollout. AI is not only introducing a new tool, it is changing expectations about how quickly work can be drafted, reviewed, improved and shared.
Traditional change approaches, structured, phased and controlled, are being stretched. Plans that once took months can now be tested within weeks. Guidance may need to be refined before a program has formally launched, and a process that made sense at the start may need to change halfway through.
Datacom’s 2025 State of AI Index helps show the difference between early use and embedded change. It reports that 87% of New Zealand organisations are now using AI, but only a small proportion have scaled it organisation-wide. That matters because it suggests AI experimentation is moving quickly, while the supporting change work, governance, capability and operating rhythms are still catching up.
So the question is not only whether people can use the tool. It is whether they understand when it helps, where it creates risk, what needs checking and where human judgement still needs to lead.
People are already moving
One of the most significant differences with AI-enabled change is that people are not always waiting for formal permission to begin. They are already experimenting, testing prompts, summarising meetings, drafting emails, creating templates, reviewing documents, and looking for smarter ways to manage the pressure of their day-to-day work.
This is a positive signal. It shows curiosity, initiative and a willingness to improve how work gets done. But it also introduces complexity. Teams may be using AI in very different ways, with different levels of confidence and care. One person may check outputs carefully, while another copies content straight into a client email. One team may have strong habits around privacy and accuracy, while another may not yet understand the boundaries.
One of the key takeaways from Microsoft’s 2026 Work Trend Index was that, in many cases, people are ready but the systems around them are not. KPMG and the University of Melbourne’s Global Trust in AI Study also found that nearly half of Australian employees admitted using AI in ways that went against company policies. These findings are not a reason to slow everything down. They are a reminder that people are already making choices, and those choices need to be understood, supported and guided.
That is why effective change starts with listening to what people are actually doing in the flow of work. Useful insights often sit in everyday behaviours: the workaround someone has created, the prompt library a team has built, the task people no longer want to do manually, or the moment confidence drops because guidance is unclear.
For many organisations, the challenge is not introducing AI from scratch. People are already using it in their personal lives daily. The opportunity is helping teams transfer that confidence into approved workplace tools and new ways of working that create lasting value.
The pressure point is not the tool, it is the work
This is where the conversation needs to shift from the tool to the work. People know they need to adapt, and there is real awareness that AI is changing expectations.
At the same time, most people are already stretched. They are carrying existing priorities, systems, habits and pressures. Interest in working differently does not always come with the time, confidence or leadership support to redesign the work itself.
That is where organisations can get stuck. They want the benefits of AI, but continue layering it onto old processes. They want productivity gains, but do not make space to ask whether the work itself should change. They want responsible use, but bury guidance in policy language that feels too far removed from people’s roles.
This is the execution gap many organisations are now facing. ADAPT’s State of the Nation: Data and AI in Australia highlights that AI deployment is moving faster than the people, processes and data foundations needed to operationalise it well. In practice, this includes things like clear decision rights, simple quality checks, access to the right data and clear escalation pathways when something does not look right. The issue is not simply whether AI is available. It is whether the work, governance and capability around it are ready.
The practical challenge is to give people enough clarity to experiment well, without slowing them down or leaving them to work it out alone.
A recurring theme in user feedback is that people are eager to explore AI’s potential, but finding the time to experiment and embed new habits into existing workloads remains a significant challenge.
The measure of success is changing
For a long time, success in technology change was often measured through adoption. Are people using the tool? Did they attend training? Did they complete the learning module? Did the launch land?
Those measures still have a place, but they do not tell the full story anymore. Someone might use AI every day and still miss the risk in an output. A team might look highly active, but not improve the quality of its work. High usage on its own does not tell us whether people are making better decisions.
A more useful question is whether people are making better decisions, improving the quality of their work and building confidence over time. Are good habits spreading? Are teams learning from each other? Is trust growing, or quietly dropping?
This is why outcome measures matter. ADAPT’s Australian research reported that more than 70% of organisations said their AI initiatives had not yet delivered measurable business value. That is a useful reminder that adoption activity needs to be connected to practical outcomes, such as better decisions, improved quality, reduced rework and more confidence in the work being produced.
This changes the role of change work. It cannot be treated as a one-off push at launch. It needs regular listening, practical coaching, visible leadership and enough flexibility to adjust as people learn what works.
The organisations seeing the strongest results are not only asking whether AI has been adopted. They are asking what comes next. Continuous engagement and support help maintain momentum, build confidence and ensure people can take advantage of new capabilities as they emerge.
Where the value is shifting
AI is taking on more of the early research and creation work. For many teams, this reduces blank-page effort, speeds up routine tasks and creates more space for higher-value thinking.
Microsoft’s 2026 Work Trend Index reinforces this value shift. It found that 66% of AI users say AI allows them to spend more time on high-value work, and 58% say they are producing work they could not have produced a year ago. The opportunity is not just to do more, but to help people redirect effort towards the work that needs context, judgement and care.
But when something becomes easier to create, the skill shifts. People need to know what is worth creating, what needs context, what needs a second look, and what should stay firmly with a person.
Through our client engagements, we are increasingly hearing that the greatest benefit of AI is not necessarily doing more work, but creating more time for the work that matters most. A recent participant from a not-for-profit organisation reflected that many previous technology initiatives had increased complexity, while Copilot had significantly reduced their administrative effort. In environments where staff success is measured by the quality of support they provide to clients, even small reductions in administration can have a meaningful impact.
This is where judgement becomes more visible. AI can give people options, but people still need to decide what is appropriate, accurate and aligned to the organisation’s values.
Leadership matters more, not less
That is why leadership does not become less important in an AI-enabled workplace. If anything, it becomes more visible. AI is strong at structure, scale and speed, but it cannot replace the trust, context and judgement that sit behind good leadership.
Leaders do not need to have every answer. In fact, pretending to have certainty in a fast-moving environment can reduce trust. What leaders do need is the ability to create clarity where they can, be honest about what is still emerging, and model the behaviours they expect from others.
One theme that continues to emerge through our user surveys is the importance of visible leadership. As one government leader recently shared, “Once your team sees you using Copilot, they start to use it as well.” Change is often social before it is technical. When leaders demonstrate new ways of working, they create permission for others to experiment and learn.
In practice, this means leaders need to model appropriate AI use, explain the judgement behind their decisions, make uncertainty safe to discuss, and be clear about where human review is required.
Governance should enable confidence
We are hearing more stories of AI outputs being trusted because they sound confident, only for those outputs to be copied into client materials, published online or shared before anyone has properly checked them. The risk is not always carelessness. Often, the output looks polished enough to feel safe. That is why governance needs to be practical, visible and close to the work, so people know when to pause, review and ask for another set of eyes.
Responsible AI guidance needs to be practical enough to use in the moment. If governance only appears as long policy documents, people may either ignore it or avoid using AI altogether. Neither outcome helps.
Good governance should make responsible use easier, not harder. People need practical prompts they can apply in the moment: Can I use this information? Does this output need checking? Should I be transparent about AI involvement? Is this a decision a person needs to make?
Datacom’s 2025 State of AI Index highlights that while AI adoption is widespread, many organisations are still in early or exploratory stages of maturity. That makes clear guidance and practical support essential if organisations want to scale AI use safely, rather than leave responsible practice to chance.
Bringing the work back to people
AI is changing the speed and shape of work, but the success of that change still depends on people. It depends on whether they have the confidence to experiment, the judgement to know when to pause, the support to build better habits, and the leadership to make sense of what is changing around them.
For change practitioners, the opportunity is to help organisations move beyond rollout thinking. The work is not just to introduce AI, but to help people use it with care, curiosity and confidence where work is actually happening. That means asking: Where is AI already changing work? Where does human judgement matter most? What guidance do people need in the moment? And what outcomes are we measuring beyond usage?
This is where effective change work is shifting. Not just helping organisations adopt new tools, but helping people use them with clarity, confidence and care as the shape of work continues to change.
How Engage Squared can help
At Engage Squared, we help organisations make AI adoption practical, people-centred and sustainable. We start by understanding how AI is already showing up in the work, where it can add real value, and what teams need to use it with confidence and care.
We work with teams to build practical skills, create guidance people can actually use, support leaders and connect experimentation with clear governance. TheOur aim is to make AI feel like part of the work, not another separate program people have to make room for.
The point is not to reach a perfect end state. It is to build the habits, confidence and leadership needed to keep learning as the work changes. If your organisation is ready to move beyond AI adoption and make AI work in practice, Engage Squared can help you take the next step.
About the author:
Kasey Drinnan is a consultant in Engage Squared’s Transformation and Change team, helping organisations get the most value from Microsoft 365 through effective adoption, change management, and employee engagement strategies. Kasey works closely with clients to support the successful rollout of digital workplace technologies, creating training, communications, and enablement experiences that help people confidently embrace new ways of working.
References
Datacom- State of AI Index 2025
Microsoft –Work Trend Index 2026
KPMG & University of Melbourne – A Global Study 2025
ADAPT – State of the Nation 2025: Data and AI in Australia
PwC Australia – Global Workforce Hopes and Fears Survey 2025 – Australian insights
