Will generative AI take your job, or will it transform it for the better?

This question is increasingly at the forefront of many people's minds. The rise of generative AI technologies, such as ChatGPT and Microsoft Copilot, has sparked intense discussions about job security, productivity, and the changing landscape of work. Yet, intriguingly, the same workers who express concern about AI potentially displacing their roles are often among those using it actively to enhance their own productivity.

Recent research from Stanford University's comprehensive audit on AI's impact across occupations highlights significant disruptions in white-collar roles. According to Stanford, nearly half of occupational tasks currently performed by workers are prime candidates for AI-driven automation, primarily because they're repetitive or low-value tasks that workers themselves prefer not to do. Similarly, the Finance Sector Union (FSU) in Australia found that 61% of finance workers surveyed felt AI threatened their job security, illustrating early, tangible signs of disruption.

But here's where things get interesting. Despite these fears, more than 75% of finance workers reported regularly using AI tools to improve productivity - even though many felt under-informed about the technology. There's an evident gap between how quickly individuals adopt AI for personal efficiency and how slowly organisations formally integrate and communicate about it. According to the FSU, nearly 40% of workers reported having a low or very low understanding of AI, pointing to a significant information gap at an organisational level.

Stanford’s task-based audit categorised AI integration into clear zones: the "Green Light Zone", where automation aligns strongly with both worker preferences and technical feasibility; the "Red Light Zone", highlighting tasks workers prefer to retain despite technological feasibility; and the "Opportunity Zone", areas where demand for AI assistance is high but current capabilities are limited.

Interestingly, current organisational investments in AI often do not align closely with these zones. Companies frequently pour resources into automating tasks in the Red Light or Low Priority Zones, rather than strategically focusing on areas workers would welcome most. And an analysis of AI startups shows the same pattern - they’re setting out to automate tasks that are low priority, or threatening to employees.

This misalignment risks exacerbating tensions within workplaces. The Australian Financial Review recently reported an incident where AI-driven sentiment analysis flagged a finance worker negatively, simply due to using the word "unfortunately" on a customer call, leading to unnecessary disciplinary action. Such examples illustrate the potential for mistrust and friction if AI adoption is mishandled.

So how can leaders navigate these challenges effectively?

Firstly, education is essential. Workers need comprehensive AI training—not just technical skills but also a thorough grounding in AI ethics and governance. Training should also clearly address how specific AI tools affect different roles within the organisation.

Secondly, meaningful consultation matters. Workers should be involved in AI-related decisions early, continuously, and transparently. Organisations need clear communication strategies, addressing both the opportunities and genuine concerns employees have about AI.

Lastly, policies must be transparent and fair. Clear guidelines around AI use can mitigate fears of surveillance or unfair treatment, creating a more trusting work environment.

Ultimately, generative AI holds enormous potential to empower workers, enhance productivity, and transform organisations, but only if its adoption is thoughtfully managed. Leaders face a choice: allow uncertainty and fear to widen gaps between management and staff, or proactively build bridges that foster trust and alignment. In workshops I've facilitated with organisations, we've focused precisely on this alignment, ensuring leaders and teams are equally educated about AI, united in optimism about its opportunities, and genuinely benefiting from its capabilities. These experiences highlight a critical insight: the real power of AI is unlocked when everyone, from leadership down to individuals tackling everyday tasks, moves forward together.

So, how are you ensuring your AI strategy empowers rather than alienates your workforce?

I’ve added three things that many people have been asking for:

• A clear recommendation to use Section’s ProfAI coach, because learning is faster and more effective when guided.

• Role-specific pathways. Whether you’re an L&D lead, product manager, educator, public sector worker or organisational leader, there’s now a curated set of courses tailored to your needs.

• Distinct learning journeys for Microsoft Copilot and OpenAI's ChatGPT

Using generative AI shouldn't be a mystery any longer. There's plenty of good ways to get your & your colleagues' skills up to speed.

Over the last three years I’ve lost count of the number of courses I’ve taken on generative AI (of course, I haven’t - it’s 42 courses, and 8 multi-course Specialisation certificates on Coursera). The technology has been changing so quickly that pausing learning is effectively moving backwards. And if I’m not bang up to date, how can I give good advice to others?

And so I think I’m in a good position to make some recommendations about what your learning journey into generative AI could be.

I've just updated my Generative AI Learning Journey document, to map out a clearer route for individuals, allowing for how much time you have, and which AI system you’re going to be using.

It includes a range of free courses and paid courses on Coursera and elsewhere.

I’ve also included some of the free online courses offered by Institute of Applied Technology which have been funded by NSW Government and Australia's National AI Centre - and available currently to anybody.

All of the detail, links, recommendations and summaries are in the AI Skills Learning Journey document. But here’s an overview of the key steps and a recommend route through it.

AI Skills Learning Journey - recommendation as of 20 May 2025 (click for the full-size flowchart)

Download the full AI Skills Learning Journey Document

There’s a new kind of workforce forming inside your organisation. It’s not on the org chart. It’s not in the policies. But it’s there. And it’s growing fast.

According to the new KPMG and University of Melbourne “Trust, attitudes and use of AI” report, nearly twice as many employees are using free public AI tools like ChatGPT than are using their employer-provided ones. And half admit to using AI in ways that aren’t transparent - either to their managers, their colleagues, or their systems.

This isn’t rebellion. It’s reality.

Employees are using AI because it helps them do their jobs better. Faster. Smarter. The issue isn’t that AI is being misused - it’s that most organisations haven’t kept up with how it’s being used. Policies lag behind practice. Training is patchy or non-existent. And in countries like Australia, we’re near the bottom of the global table for AI training access.

The result? A workforce that’s experimenting in the dark. People are using powerful tools with little guidance - not because they’re reckless, but because the tools are useful, and the guardrails aren’t clear. Two in three users rely on AI outputs without checking them. Almost half have seen others upload sensitive data to public tools. Most haven’t had any formal training. And yet, 60% say they feel effective using AI. Imagine what they could do with proper support.

What’s really going on here isn’t just a gap in governance - it’s a gap in trust.

If employees are hiding their use of AI, ask why. Are they unclear on what’s allowed? Are they worried their colleagues will think they’re cheating? Or are they simply moving faster than the system around them? This isn’t unlike the early days of BYOD (bring your own device) or unsanctioned internet, messaging or software use. But the scale is bigger. And the stakes are higher.

You can’t stop shadow AI. But you can shape it.

That means rethinking your approach to training, to policy, and most of all - to culture. AI literacy should be seen as a core skill, not a niche one. Policies should be living documents, not just rulebooks. And most importantly, leaders need to send a signal: we trust you to use these tools - but we expect you to do it responsibly.

Because if shadow AI is already the norm, the real question isn’t whether to allow it. It’s how to lead it.

Productivity Paradigms

And that brings us to the three pillars of AI productivity: Personal, Process, and Paradigm.

Most organisations today are still operating with AI in the personal productivity phase - where individuals are using AI to get their own work done more efficiently. That’s where the shadow AI trend is most visible.

But it’s only the beginning.

The next phase is Process Productivity - where teams and departments start reimagining workflows. Think call centres using AI to triage queries, or legal teams using it to sort documentation. This is where real gains start to scale.

Then comes Paradigm Productivity - the point where organisations stop applying AI to old models and start rethinking their entire approach. New services. New value. New business models. Think Netflix replacing video stores, or Uber reimagining urban mobility.

If AI is already being used by two-thirds of your people, the question isn’t how to catch up. It’s how to harness that momentum to move forward - faster - into the phases where real transformation happens.

So - how is your organisation meeting that challenge?

But talk to the people who attend these sessions and you’ll hear a different story: confusion, frustration, and a nagging feeling that none of it really sticks.

As one Reddit user bluntly put it:

“I took an AI course, but it was all math and no real-world application. I still don't know how to use AI in my job.”

Here’s the uncomfortable truth:

AI training is broken.

And for executives looking to make progress on digital transformation, that’s a bigger problem than it might seem.

What’s going wrong?

1. Too much theory, not enough application
   Most training focuses on how the tech works under the hood. Great for data scientists – useless for your sales, ops or finance teams. What people actually need is “What does this mean for my job?” and “How can I use this tomorrow?”
   Too many times I’ve seen people turn up to show off some amazing AI tool, and show an amazing output - but not spend the time to show how you actually create it, step-by-step. It’s like somebody just invented the pencil, and they show you this amazing Mona Lisa drawing they’ve created with it. Great, but when you get it home, the best you can get is stick figures
2. Generic advice, no business context
   AI is not one-size-fits-all. What’s useful for a retailer isn’t useful for a bank. Most training doesn’t adapt to sector or role, so learners come away with abstract knowledge, but no clear next step.
   Almost without exception, whatever group I’m talking to will hear about how AI is being used in their sector, and in organisations like theirs. It’s not difficult to find the right context and examples - there are thousands of case studies - but it is time consuming to prepare and make the context fit the trainee.
3. No follow-through
   Even when people learn something useful, there’s little support to help them apply it. It’s like sending someone to a cooking class, then never giving them a kitchen.
   I’ve trained teams where they weren’t even given access to AI tools after the training. Getting people motivated to experiment with AI is a key goal of any AI training (or should be!), so what a waste of time and money to not enable the experimentation
4. Outdated content in a fast-moving world
   With new AI tools launching every month, most course content is stale before it hits the LMS. The result? A disconnect between what’s taught and what’s possible.
   When I’m delivering face-to-face training I will check on the morning what’s happened overnight and whether any of it is relevant to the trainees. Ask me to provide my slides a month in advance? Hah! You’re guaranteeing that the training will be out of date. And imagine the lead times for a typical course in an LMS. While 90% of the content might still be up to date two months later, the 10% that needs to be bang up to date is so important to your learners.
5. No link to strategy or decision-making
   AI skills mean nothing if they’re not tied to business goals. Without clear alignment to priorities like customer growth, operational efficiency or compliance, AI remains an experiment rather than a driver of transformation.
   I once ran a workshop for a management team who asked “Why are we here? We aren’t allowed to use AI”, and yet it was the CEO that put the program in place. Having a strategy well communicated is also clearly helpful!

So what’s the alternative?

The most effective organisations are shifting away from standalone training and towards structured, applied learning experiences that directly support business outcomes.

Here’s what that looks like:

• Start with real business problems, not AI features
  Help teams identify friction points, inefficiencies, or risks they already face – and frame AI as one of several tools that might help solve them.
  In my experience, the team will need some training in AI to be able to identify the opportunities to use AI, so this is something that needs to get built into the training, not done beforehand
• Use methods like pretotyping and prioritisation frameworks
  Let teams experiment with low-cost, low-risk ideas before scaling. This builds confidence and de-risks the investment.
  I love pretotyping, and AI allows us to do much more of it really quickly and in a way that stays agile and keeps people engaged in the end goal
• Focus on practical fluency, not technical depth
  You don’t need everyone to become prompt engineers. You need people who can identify good use cases, ask smart questions, and lead responsible implementation.
  And you need people with a balance of IQ and EQ. Not just one or the other
• Embed AI into the rhythm of work
  Use coaching, live projects, reflection tools and AI copilots to help people apply what they learn as they go.
  AI training simply can’t be “one and done” - you need to create a community that enables and encourages continuous learning

Final thought

AI isn’t just a technical shift – it’s a cultural one.

If your teams are disengaged from AI training, the solution isn’t more training. It’s smarter, more contextual, more strategic support.

Because in this landscape, it’s not the most AI-literate companies that will win.

It’s the ones who know how to turn AI understanding into business impact.

This matters, because it fundamentally changes what it means to lead, manage, and upskill an organisation. The big question is no longer, “What AI tool should we buy?”. Instead it’s, “How do we lead teams where some of the employees aren’t human?”

I help organisations prepare for exactly this kind of shift, and it’s fascinating to see two reports from authoritative sources drop in the same week.

• Microsoft’s WorkLab is a team of researchers looking into AI and the future of work. They produce regular research reports of the trends they are seeing in the way that people are working within Microsoft and in their customer organisations.
• Professor Jules White, from Vanderbilt university, is the Director of Vanderbil’s Initiative on the Future of Learning and Generative AI. His AI courses on Coursera have been taken by half a million students (and personally, I rate them as some of the best and most pragmatic I’ve taken)

And having studies both reports, and the differences and similarities, here's what you need to know.

Human-agent teams: not the future, but the present

Microsoft describes the rise of the Frontier Firm – organisations where humans and AI agents work side-by-side. They're faster, leaner, and able to scale knowledge work on demand. They don't restructure based on function – they restructure around work.

Jules White goes further, arguing that organisations need to think of AI as labour, not software. Prompts are job assignments. Tokens are the currency of output. And managing this new AI workforce is no different from managing people – it requires training, oversight, and leadership.

The idea of “every employee becoming an agent boss” isn’t a gimmick. It’s a necessity.

What's changing?

• Managers become AI orchestrators
  Leaders are being asked to manage AI-powered teams. That includes hiring (selecting the right models), onboarding (training them for specific tasks), and performance management (reviewing and improving outputs). If your managers aren’t ready to supervise AI labour, your organisation isn’t ready.
• Every role is becoming hybrid
  Whether it’s customer service, compliance, finance or product design, agents are already being used to handle repetitive, cognitive tasks. Employees need to know when to delegate to AI, how to prompt effectively, and how to course-correct when things go off track.
• Architecture becomes strategy
  White makes a critical point: the way your IT systems are set up dictates who you can “hire” as AI labour, what they can do, and how well they work. If your AI agents are locked inside individual tools, you’re not building a team – you’re building silos.

What this means for upskilling and culture

Organisational readiness in the AI age isn’t just about technical skills. It’s about cultural and strategic adaptability. Here’s what forward-thinking companies are doing:

• Shifting the mindset from efficiency to amplification
  The goal isn’t to reduce headcount. It’s to do more, with the same people – and give them the time and tools to focus on higher-value work.
• Training everyone, not just IT
  Every employee should be trained to work with AI: how to prompt well, how to refine outputs, and how to collaborate with digital teammates.
• Creating space for experimentation
  AI’s value often comes from unexpected places. Organisations that encourage exploration – not just compliance – get better results, faster.
• Redesigning workflows, not just plugging in AI
  Adding AI to broken processes won’t fix them. Leaders need to rethink workflows entirely – and often need new metrics like the human-agent ratio to get it right.

A quiet revolution is underway

We’re entering a phase of rapid reorganisation – not by departments, but by tasks and capabilities. AI isn’t replacing people. But it is forcing every organisation to ask: how will we work when we’re not the only ones doing the work?

At Stratentia, we help organisations answer that question by blending culture change, upskilling, and systems thinking. Because if your people aren’t ready to lead AI, your organisation won’t be ready to scale it.

Let’s talk.
If you’re exploring how to upskill your workforce and prepare your leadership for an AI-powered future, get in touch. The tools are here. The strategy is what makes the difference.

Identifying and Scaling AI Use Cases

From the “Identifying and Scaling AI Use Cases” guide, three things stood out:

• AI should be led and encouraged by leadership.
  If leadership doesn’t model usage and back the investment, adoption stalls. It’s that simple.
• Complex use cases often slow you down.
  Yes, it’s tempting to build the big impressive prototype. But the fastest way to value is often letting employees spot and solve their own problems—starting small and scaling what works.
• Adoption accelerators matter.
  Hackathons, use case workshops, and peer-led learning aren’t fringe tactics. They’re key enablers. They help surface ideas, build momentum, and show people what’s possible.

They also talk about focusing on three types of work: repetitive tasks, skill bottlenecks, and ambiguous workflows. It’s the last one that always gets me. When people say, “Oh, we couldn’t give that to AI - everyone would do it differently,” I’d argue that’s exactly where AI shines. It can bring consistency to tasks where human variance is high. Take customer complaint emails: AI won’t write them all the same, but it can help set tone and language so they're all clear, calm and on-brand. That’s a win.

AI isn’t just a speed tool. It’s a consistency tool. It can bring structure to messy processes, standardise tone and language, and help teams start from a shared foundation—even if the final output still needs human judgment. That’s not a limitation. That’s a strength.

And in my own conversations with teams across industries, I’ve seen the same pattern again and again: the biggest barrier to AI adoption isn’t fear or lack of tools—it’s misjudging where the real opportunities are.

AI in the Enterprise

Then there’s the “AI in the Enterprise” guide, which shares lessons from companies like Klarna, BBVA and Mercado Libre. My favourite bit? Lesson #5: Get AI in the hands of experts. It’s the people closest to the work who know what’s slowing things down, and how AI might help. As they say:

“The people closest to a process are best-placed to improve it with AI.”

I've seen this in action. When teams get the right tools and trust, they find efficiencies no roadmap could predict. They spot the hidden friction points. They know where the real value is.

Tie that in with two other lessons - Set bold automation goals and Start now, invest early - and you’ve got a powerful message.

When I was at Google, I learned about applying 10X thinking. The idea wasn’t to be 10% better - it was to ask how we could do 10 times more this year. It was challenging, sometimes stressful. But it led to a strange realisation: the effort to do 10X wasn’t always 10X more work. Sometimes, it was about changing how you think. New questions unlocked new answers.You just needed a different lens.

AI is no different. Start where the knowledge is. Scale with ambition. And don’t wait for perfect clarity.

Both guides are worth a read if you’re figuring out how to make AI work in practice.

• Set a Clear Vision and Narrative: Articulate why AI matters for your organisation in simple, compelling terms. Connect it to your mission or strategic goals (e.g. “to improve customer experience” or “to drive efficiency and growth”). Communicate this vision relentlessly. Employees need to hear not just the rational case (“AI will automate X process”) but also the emotional case (“this will make our jobs more interesting and help the company thrive”). When leaders communicate a clear plan for AI adoption, employees are nearly 5 times as likely to feel comfortable using AI in their role​. Storytelling from the top can align and energise the whole organisation.
• Cultivate an Adaptable, Learning Culture: Encourage a culture that celebrates learning, curiosity, and adaptability. Make it safe for employees to experiment with new ideas or tools without fear of punishment if something fails. One way is to share “failure stories” in a blameless way – what was learned from a pilot that didn’t meet its goals? When people see that leadership values agility and growth over perfect outcomes, they’ll be more willing to get on board with AI changes. Remember, an adaptable culture is strongly correlated with better business outcomes and growth​. As the saying goes, “adapt or die” – and that applies to culture as much as to strategy.
• Invest in Skills and Confidence: Don’t assume your workforce will figure AI out on their own. Provide training, upskilling, and hands-on support. This could include formal courses on data analytics, workshops on using new AI tools, or one-on-one coaching for managers on leading AI-augmented teams. Bridging the skills gap is critical: no one wants to feel left behind by technology. By empowering employees with knowledge, you not only improve their ability to use AI (the “Knowledge” and “Ability” in ADKAR) but also reduce fear of the unknown. Consider creating an “AI academy” internally or leveraging online platforms – and encourage leaders to lead by example (e.g., an executive sharing how they personally used a new AI insight in decision-making).
• Lead with Change Management (ADKAR in practice): Approach AI initiatives with a formal change management plan. Start by assessing the readiness of your people – do they understand why the change is happening? Address rumors and concerns head-on to build Awareness and Desire. Involve influential employees as change champions to spread positive momentum. Provide forums for two-way communication (town halls, Q&As, internal forums) so people feel heard during the transition. As new AI tools roll out, ensure there’s adequate training (Knowledge) and time to practice (building Ability). And don’t forget to follow up: reinforce the change through recognition, adjusting KPIs to support new behaviors, and continuously highlighting wins. By leveraging ADKAR or similar frameworks, you treat AI adoption not just as a tech rollout, but as a human transformation – which is exactly what it is​.
• Align AI Projects with Business Value: Prioritise AI use-cases that clearly solve real business problems or improve customer outcomes. This might sound obvious, but it’s easy to get caught up in deploying AI for AI’s sake. By focusing on initiatives that have tangible impact, you create pull from the organisation (because who doesn’t want to hit their targets faster or serve customers better?). For example, if your culture prides itself on customer service, introduce an AI tool that helps service reps respond more quickly, and frame it as enhancing that core value. Early wins build credibility. They also make it easier for skeptics to see the upside, thus converting some fence-sitters into supporters. As one report noted, “targeted AI solutions – designed to solve core operational challenges – can deliver measurable ROI faster”​, which helps sustain momentum.
• Mind the Ethics and Trust Factor: Ensure your AI adoption is accompanied by strong ethics, governance and transparency measures. Business leaders should proactively address questions of data privacy, bias, and accountability in AI systems. This isn’t just about avoiding regulatory issues; it’s about building trust with employees and customers. When people see that AI is being implemented responsibly – with guidelines on usage, oversight in place, and clear respect for privacy/security – they are more likely to embrace it. For instance, involving a diverse group in testing AI algorithms can catch biases early and signal the organisation’s commitment to fairness. In sectors like finance or healthcare, demonstrating ethical safeguards can turn wary stakeholders into cautious champions. Trust is the currency of change – earn it, and your AI initiatives will face far less friction.

Benchmark and Learn from Others: Finally, don’t go it alone. Look at how peers or even companies in other industries are successfully adopting AI. Australian businesses can draw inspiration from global case studies (and vice versa). If you’re in the public sector, examine how another government department piloted a chatbot or analytics solution. If you’re a bank, study how an overseas bank modernised its fraud detection with AI. This external perspective can spark ideas and also help calibrate your own progress. Networking with other leaders, joining industry forums on AI, or bringing in experts can prevent insular thinking. It’s a way of injecting fresh cultural DNA – showing your teams that “if they can do it, so can we.” Plus, these connections might surface partnership opportunities to share risk and reward on certain AI projects.

Research and surveys give some weight to this perception. In Australia, almost 60% of board members admitted that innovation had never or only rarely been a board agenda item​ –  a sobering statistic that hints at complacency. A report by CSIRO (Australia’s national science agency) and the University of Queensland went so far as to say Australian companies were stuck in a “corporate stone age”, reluctant to embrace new ideas and technologies​. That’s a provocative phrasing, but it underscores a real challenge: a cultural comfort with the status quo. Australian firms have tended to be fast followers rather than first movers, adopting tech once it’s proven elsewhere rather than betting big early. Directors often cite the country’s regulatory environment and strict director liability laws as factors that nudge them toward caution​. The result can be a low appetite for experimentation, and a focus on avoiding downside risk over chasing upside opportunity.

In contrast, U.S. companies – particularly in the tech sector – operate in an environment that rewards bold moves. Corporate failure in the U.S. carries less stigma; venture capital and large markets encourage aggressive scaling; and success stories of big bets (from Amazon to Google) set an example that embracing risk can lead to outsized rewards. This isn’t to say American executives are reckless – they have their own governance and accountability – but there is generally a stronger cultural narrative that not innovating is the bigger risk. It’s telling that Australian boards are seen to under-prioritise innovation and disruption risks compared to overseas boardrooms​. Where a U.S. firm might allocate budget to a moonshot AI project, an Australian firm might require more exhaustive business cases and pilot studies first.

However, the gap is not insurmountable, and generalisations have exceptions. Many Australian companies are actively shedding their conservative approaches, inspired by global competition and the success of local tech champions (like Atlassian or Canva, which do embody a bold, innovative culture). And plenty of U.S. companies in traditional sectors can be quite conservative internally. But awareness of these differing mindsets is important. Australian directors and leaders might need to consciously challenge their instincts – to ask, “Are we avoiding this initiative because it’s truly unwise, or just because it’s unfamiliar?”

One encouraging sign is that Australian business leaders are increasingly talking about strategic risk – the risk of missing out on new opportunities. There’s a growing realisation that a risk-averse culture, with misaligned incentives, is less likely to support and invest in innovation, trial new processes, and adopt new technologies​. In other words, playing it too safe is itself a danger. Company directors in Australia are being urged to broaden their perspective: to weigh the risk of trying something new against the risk of sticking with the old. As the CEO of the Australian Institute of Company Directors put it,

“Innovation is often missing from Australian boardroom agendas… traditional risks are the focus rather than the risks – and opportunities – associated with innovation”​

For any business leader (not just in Australia), the takeaway is to foster a balanced risk culture. This means creating room for experimentation and smart risk-taking – running controlled experiments, setting aside an “innovation budget,” and even tolerating some failures – while still managing downside risks in a sensible way. Leaders can set the tone by celebrating intrapreneurs and project teams that take initiative, not just those who hit short-term targets. Over time, an organisation that learns to take calculated risks in pursuit of innovation will outpace one that only plays defense.

Why? Because when organisations can’t keep pace, they risk disruption from those who can. The average lifespan of an S&P 500 company has plummeted from 60 years in the 1950s to under 20 years today​. In disruptive times, the only truly risky move is to not take any risks at all. Companies that cling to “business as usual” while the world changes around them may suddenly find themselves irrelevant – the proverbial Kodak moment in a bad way. AI is likely to accelerate this trend: it has the potential to reinvent business models and processes overnight. Firms that adapt slowly could see agile competitors (or new entrants) swoop in with AI-driven products, winning customers with better, faster, cheaper offerings.

So how can organisations bridge this adaptation gap? A few strategies emerge:

• Lifelong Learning and Upskilling: Create a culture of continuous education so employees at all levels can rapidly acquire new skills as technology demands. Encourage everything from formal training programs to self-paced online courses, and recognise those who take initiative to learn. As Friedman noted, embracing “lifelong learning” is crucial to catch up with technological change​.
• Agile Structures: Replace rigid, hierarchical decision-making with more agile, cross-functional teams that can implement new tech in bite-sized chunks. Methodologies like agile and DevOps in IT, or more broadly a network of empowered teams, allow organisations to iterate quickly rather than get bogged down in analysis paralysis.
• Pilot and Scale: Rather than waiting for perfect information, adopt a test-and-learn approach. Pilot an AI tool in one department, measure impact, learn from mistakes, and then refine and expand. This approach lets the organisation adapt in smaller increments, which accumulate into big change over time.
• External Sensing: Keep a close eye on technological trends and be willing to bring in outside perspectives. This could mean partnerships with startups, participating in industry consortia on AI, or even hiring advisors with cutting-edge expertise. The goal is to avoid being blindsided by “what’s next” – instead, you see it coming and have a plan for it.
• Align Tech and Strategy Continuously: Make technology adoption an integral part of strategic planning, not an afterthought. Companies bridging the gap well are constantly aligning their strategy (where the business is going) with their capabilities (what the technology enables). If the strategy shifts, they pivot resources and training accordingly. This prevents the scenario where tech is zooming ahead but the company’s strategic mindset is stuck in yesterday’s world.

Bridging the gap is not easy – it requires foresight and a willingness to disrupt yourself. But consider the alternative: losing talent (who leave for more forward-thinking employers), falling behind competitors, and facing that moment when you realise the market has moved on without you. By proactively adapting culture, skills, and processes, business leaders can ensure that their organisations ride the wave of technological change rather than being dunked by it.

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​.

Public sector organisations (government agencies, public services) often take a cautious, deliberate approach to AI. These bodies operate under intense public scrutiny and accountability, which fosters a culture of prudence and risk-aversion. Failure can lead to public outcry, so experimentation tends to be measured. It’s not that laws outright ban AI – indeed, many governments are exploring AI for better citizen services – but the bureaucratic culture and legacy processes slow things down. Government leaders must balance innovation with mandates for transparency, fairness, and security, which can make AI projects proceed carefully.

Cultural barriers loom large. Public sector employees may worry that AI will disrupt civil service jobs or that they lack the skills to work with advanced tech. Comprehensive change management is crucial for successful AI adoption in government, as leaders and employees often face cultural barriers or skill gaps when incorporating AI into daily workflows​. In practice, we see many agencies start with pilot programs or public-private partnerships to build confidence and expertise gradually​.

Yet change is afoot. Most public sector leaders now see AI as pivotal to the next phase of digital transformation​. To turn that belief into reality, agencies are investing in training and upskilling programs, workshops, and ongoing support so that AI isn’t an alien intrusion but “woven into the organisational fabric”​. By focusing on a mission-driven narrative (e.g. how AI can improve public safety, health outcomes, or service efficiency) and creating a safe environment for experimentation, some public organisations are beginning to overcome the inertia. The cultural shift is from “we’ve always done it this way” to a mindset of continuous improvement and responsible innovation in service of the public. Progress may be gradual, but with the right leadership and change strategy, even cautious bureaucracies can harness AI in meaningful ways.

Traditional Regulated Companies: Innovation Under Compliance

Heavily regulated industries – think banking, insurance, healthcare, utilities – are by necessity risk-conscious and process-driven. These traditional companies have long histories, complex structures, and strict compliance obligations. But while regulations shape what they must consider (privacy, safety, etc.), it’s often the internal culture and governance that determine how fast they move on AI. Many regulated firms have a culture of prudence: decisions go through layers of approval, and there is low tolerance for failure or anything that might threaten stability. Innovation happens, but incrementally.

For example, a large bank might be legally free to deploy AI in customer service or fraud detection, yet its board and executives could slow-roll the initiative out of an abundance of caution.

Directors in these companies often prioritise traditional risks (compliance, financial controls) over “the risks – and opportunities – associated with innovation and disruption”​.

The result? AI adoption projects compete with legacy priorities and can stall unless clearly tied to risk reduction or efficiency gains. Indeed, a major study of Australian corporate boards found that many boards weren’t prioritising innovation or digital disruption as much as their international counterparts, contributing to a risk-averse corporate culture​. While that study was Australia-specific, the pattern rings true in many regulated environments globally.

Yet being regulated doesn’t have to mean being rigid. Some forward-looking banks and hospitals are carving out innovation teams or “digital garages” to experiment with AI in a sandbox environment that doesn’t threaten core operations. Leadership makes the difference – when top executives champion AI with a clear vision and allocate resources, it signals to the organisation that innovation is not only allowed but expected. These companies often take a “trust but verify” approach: adopt AI solutions but layer them with strong governance, ethics reviews, and compliance checks. The cultural challenge is to maintain their strengths (safety, reliability, trust) while shedding the “not invented here” syndrome and fear of change that can plague long-established firms. Those that succeed find they can both comply with regulations and compete on innovation.

Traditional Unregulated Companies: Breaking Out of Legacy Mindsets

Not all traditional companies are in tightly regulated sectors. Manufacturers, retailers, consumer goods firms, and others often face fewer direct regulatory hurdles to AI adoption. In theory, this gives them freedom to innovate; in practice, many still struggle if they carry a legacy culture. An established retail chain or manufacturing giant might not have a regulator looking over its shoulder on AI usage, yet it can be just as slow as a bank to implement AI if its leadership is change-averse or unconvinced of the ROI.

Many of these firms are “born-traditional” companies trying to become more digital, with mixed results. They may have siloed data, outdated IT systems, and employees who’ve “always done it this way.” If past technology projects fizzled out or if the workforce is unfamiliar with AI, skepticism can run high. On the other hand, competitive pressure in unregulated markets can be intense – if your rival uses AI to optimise supply chains or personalise e-commerce, you can’t afford to sit still. This creates a tension between the urgency to innovate and the inertia of old habits.

Here again, culture is the tipping point. Organisations that encourage experimentation and learning from failure tend to adopt AI faster, even without regulatory pressure. A culture of experimentation “eliminates the fear of failure and is essential for fostering innovation… ensuring that employees are willing to embrace new technologies, including AI, to achieve business goals”​. Companies that cultivate this mindset – perhaps inspired by lean startup principles or after hiring fresh digital talent – will pilot AI in one part of the business, prove the value, and then scale it. Those stuck in rigid hierarchies, by contrast, might endlessly deliberate or wait for a “perfect” solution.

We often see unregulated incumbents forming partnerships or acquiring startups to jump-start their AI capabilities, effectively importing a more innovative culture. Others create cross-functional “AI task forces” internally to break down silos and spark new ideas. The bottom line: freedom from regulation doesn’t automatically speed up AI adoption – it’s the willingness to challenge legacy thinking and empower teams that does. Traditional companies that break out of their own comfort zones can level the playing field with more tech-native rivals.

Digital-Native Businesses: Innovating by Default

On the opposite end of the spectrum are digital-native businesses – companies born in the internet era (or later) with technology in their DNA. For these organisations, adopting AI isn’t a bold leap; it’s the natural next step. Their cultures typically celebrate innovation, speed, and “fail-fast” learning, which gives them a major head start in extracting value from AI. A recent study contrasts “born-digital” companies with “born-traditional” ones and finds significant differences in mindset. For example, 83% of born-digital companies say that adopting new technologies will drive growth for their business, and 80% have fully digitised their customer journey – versus only 20% of born-traditional companies having done so​. In short, digital-natives view AI as central to their strategy, not just a tool to try out.

This makes sense: many digital-natives are tech companies (think e-commerce, fintech, software firms) or disruptive startups attacking old industries. They often build AI into their products from day one – whether it’s a recommendation engine, an intelligent chatbot, or analytics baked into a service. AI is “woven into the very fabric” of these organisations’ business models, giving them a head start​. Moreover, such companies tend to have flatter hierarchies and a culture that empowers employees to experiment without endless approval chains. Failure is seen as a learning opportunity rather than a career-ending blunder.

“Digital natives build a culture where it’s ok that not every investment will pay off, and where failures are just as valuable as successes,”

as one analysis noted​. This environment makes it much easier to spin up an AI pilot, iterate quickly, and scale what works.

Another advantage: digital-natives often manage talent differently, attracting and nurturing people with strong AI and data skills. They invest in training their teams and can “adapt and retool” their platforms when technology takes a leap forward​. In other words, they expect change. Even these companies aren’t immune to challenges – they can still make tech bets that don’t pan out, or hit roadblocks with data quality – but their inherent agility means they rebound faster. For incumbent businesses, the lesson from digital-natives is less about copying specific tech, and more about embracing a culture that values agility, continuous learning, and the strategic use of AI at every level. The gap between digital-native and traditional organisations is wide, but it’s crossable if the latter are willing to transform how they think and operate.

“By failing to address organisational culture, even the most advanced technology will fail to achieve its full potential”​.

In other words, culture is often the deciding factor in AI success, overshadowing industry regulations. This blog series explores how different types of organisations – public sector bodies, traditional companies (both regulated and not), and digital-native businesses – adopt AI differently due to their unique mindsets around innovation, risk, and transformation. We’ll also look at the human side of change (through the ADKAR model), the challenge of technology racing ahead of organisational change, and why Australian companies may need to rethink their aversion to risk. Finally, we’ll offer practical tips for business leaders to make AI adoption smoother and more effective.

AI Adoption as a Cultural Choice, Not a Compliance Task

It’s easy to blame strict regulations or red tape for slow AI adoption. In reality, organisational culture and leadership mindset shape AI adoption far more than the rulebook does. Research consistently shows that companies with adaptive, innovative cultures gain more value from new technologies​. Adaptability is key – firms that can “shift the culture to align with the new strategy” when technologies like AI emerge tend to outperform those stuck in their old ways​. Conversely, organisations clinging to:

“outdated cultural norms – such as those resisting AI – are at a clear disadvantage”

in today’s fast-changing landscape​.

Put simply, AI adoption is a people issue as much as a tech issue. If employees fear or distrust AI, or if leadership is too cautious, even permissive regulations won’t spur innovation. A forward-thinking bank and a conservative bank may face the same banking laws, but their AI journeys can diverge wildly depending on whether their culture embraces change or fears it. The following sections examine how four organisational archetypes – public sector agencies, regulated traditional companies, unregulated traditional companies, and digital-native businesses – each approach AI adoption through the lens of their culture and risk posture.