A recent Australian Financial Review opinion piece described AI as a “freight train” coming for white-collar work, with business leaders warning of a possible “tsunami” across industries built on knowledge, judgement and intellectual output.

It is a vivid metaphor, but the bigger point lands.

AI is no longer confined to pilots, PowerPoint decks or innovation theatre. As the article noted, organisations such as Qantas, Telstra and La Trobe Financial are already seeing operational gains from AI in live environments. This is not a future scenario. It is happening now.

For those of us working in digital adoption, change and transformation, that shifts the conversation in an important way.

The question is no longer whether AI matters.

The question is whether organisations know how to adopt it well.

 

AI is not just a technology shift. It is a people and work shift

Much of the public discussion about AI swings between excitement and alarm.

On one side are the productivity optimists. They see AI as a powerful lever for growth, efficiency and better decision-making. On the other side are those warning of significant disruption, particularly for white-collar roles and business models that depend on expertise, analysis and professional judgement.

James Thomson’s opinion piece captured both ends of that debate. But from an adoption perspective, neither side arrives at the heart of the challenge on its own.

AI does not create value simply because a tool is available. It creates value when people know how to use it in real work, understand its limits, trust it appropriately, apply judgement where needed, and adapt their habits and workflows around it.

That is why AI is not merely a technology rollout. It is a work redesign challenge. It is a leadership challenge. And above all, it is an adoption challenge.

 

The biggest risk is not only job loss. It is poor adoption

The article is right to focus on what Reserve Bank governor Michele Bullock called the “pains in the middle”.

Even if AI does drive long-term productivity, the short and medium term may be difficult. Roles will shift. Expectations will change. Some work will be augmented, some automated, and some redefined altogether.

But in many organisations, the first real problem will not be dramatic job losses. It will be poor adoption.

That usually looks less like a headline and more like a slow operational mess.

It looks like leaders talking confidently about AI while teams are unclear on where it fits into the flow of work.

It looks like employees experimenting quietly because formal guidance is too slow, too vague or too restrictive.

It looks like managers being asked to drive productivity gains without role redesign, capability uplift or realistic support.

It looks like scattered pilots, inconsistent practices, patchy governance and benefits that sound impressive in theory but remain frustratingly elusive in practice.

This is where many AI strategies will come unstuck. Not because the technology is weak, but because the organisation has mistaken access for adoption.

Putting a tool in people’s hands is not the same as embedding a new way of working.

 

Adoption leaders are no longer the support act

For years, organisations have often treated adoption and change as something that happens after the important decisions are made.

A new system is selected. A program is launched. Then someone says, somewhere near the end, “We should probably do some comms and training.”

That approach was shaky before. With AI, it is completely inadequate.

AI changes more than screens and process steps. It changes how work is performed, how decisions are made, what good judgement looks like, where accountability sits, and which capabilities become more valuable rather than less.

That means the people leading adoption should not be brought in late to socialise a pre-baked solution. They need to be involved early, while the organisation is still defining use cases, guardrails, role impacts, manager expectations and measures of success.

In other words, adoption leaders are no longer the support act. They are central to whether AI creates value or confusion.

 

Why this matters especially in Australian business and government

Australian organisations face a particularly challenging environment for AI adoption.

In business, there is pressure to lift productivity, reduce costs, modernise service and stay competitive. In government, there is the added complexity of public scrutiny, privacy obligations, legacy systems, procurement constraints and a very low tolerance for visible mistakes.

In both sectors, workforces are already tired. Many employees have lived through years of restructuring, system changes, channel shifts, automation programs and capability initiatives. Now AI arrives carrying both big promises and a fair amount of fear.

That combination matters.

When organisations introduce AI into environments already stretched by change fatigue, they should not be surprised when people hesitate, resist, overuse the tools, avoid them, or work around formal processes altogether. Humans are funny like that. Tell them something is transformational, urgent and safe without explaining what it means for their job, and they tend not to become instantly serene.

This is why Australian organisations need more than AI ambition. They need disciplined adoption.

 

What good AI adoption leadership looks like

For leaders responsible for digital adoption, transformation and change, the task now is to shift from reactive support to strategic influence.

That means asking practical questions early:

How is work actually changing?

Which decisions can be supported by AI, and which still require human judgement?

What risks need to be managed in the flow of work, not just on paper?

What do people need to stop doing, not just start doing?

How will managers lead teams through ambiguity, fear and capability gaps?

How will success be measured beyond usage statistics or pilot activity?

These questions are not peripheral. They determine whether AI becomes part of productive, trusted work or remains stuck in a cycle of hype and uneven experimentation.

Good adoption leadership also means being honest. Not every use case is ready. Not every team has the same maturity. Not every promised benefit will arrive quickly. And not every concern raised by employees should be dismissed as resistance.

Sometimes concern is not resistance at all. Sometimes it is the organisation’s early warning system.

 

The next wave of digital adoption needs to be more mature

The last decade taught us that technology implementation alone does not guarantee transformation. Many organisations learned that the hard way.

AI raises the stakes because the pace is faster, the use cases are broader, and the implications for knowledge work are deeper. This is not just another system rollout. It touches capability, judgement, risk, trust and identity in ways that are more personal and more immediate.

That means the next wave of digital adoption needs to be more mature than the last.

It needs to connect strategy to operations.

It needs to link technology decisions with work design.

It needs to support managers as local leaders of adoption, not just messengers from head office.

It needs to build confidence and judgement, not just tool familiarity.

And it needs to recognise that sustainable adoption is not driven by urgency alone. It is driven by clarity, support, trust and visible leadership.

 

A defining moment for adoption leaders

Thomson is right about this much: AI is not approaching. It is already here, and its effects are already being felt.

Whether it becomes a productivity breakthrough, a workforce shock, or a muddled mix of both will depend on more than the capability of the tools. It will depend on how organisations lead adoption in the middle of uncertainty.

That is why this moment matters so much for those of us in change, transformation and digital adoption.

The front line is no longer at the point of technical deployment.

The front line is where people are trying to make sense of changing work.

It is where managers are balancing performance with ambiguity.

It is where teams are deciding whether to trust a tool, ignore it, test it, or quietly work around it.

It is where governance either becomes practical and enabling, or heavy and irrelevant.

And it is where organisations will either realise value from AI or discover, rather expensively, that adoption was never the side issue.

At Asporea, our view is simple.

AI will not succeed because the technology is clever.

It will succeed when organisations do the harder work of helping people adopt it well.

That is the challenge now.

And that is why adoption leaders are on the front line.

 

Closing call to action

If your organisation is moving from AI curiosity to real implementation, now is the time to ask a better question than “What tool are we using?”

Ask instead:

Are we ready to help our people adopt new ways of working with clarity, confidence and trust?

Because that is where the real value will be won or lost.