Change in the Face of AI

Flock of birds flying together across an open sky, symbolising leadership, adaptability, and collective movement through change

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summary || Change in the Face of AI: How Organisations Can Lead, Adapt, and Thrive

Artificial Intelligence (AI) is reshaping how organisations operate, compete, and grow — not just through technology, but by redefining strategy, leadership, and culture. While individuals have rapidly adopted AI in everyday life, many organisations are lagging in structured integration, posing risks not only to enterprise performance but to national productivity.

AI marks a significant inflection point, comparable to the Industrial Revolution. The challenge is not simply adoption, but full-scale organisational transformation. The winners will be those who embed AI into workflows, decision-making, and capability development — with ethics, trust, and people at the centre.

Key actions for leaders include:

  • Leading with clarity and courage, setting a purposeful vision for how AI supports people and performance

  • Investing in people, not just tools, with AI literacy, experimentation, and culture-building

  • Scaling thoughtfully beyond pilots, aligning AI with governance, processes, and workflows

  • Establishing ethical guardrails, ensuring responsible, transparent, and inclusive use of AI

  • Building resilience, not just readiness, through adaptive leadership and continuous learning

Change models like ADKAR, Kotter’s 8 Steps, and the McKinsey 7-S Framework remain valuable — not as rigid templates, but as guides for navigating change at scale.

Organisations that act early, invest in people, and lead with intention will be better positioned not just to adopt AI — but to thrive because of how they use it.

  • verb

    1. make (someone or something) different; alter or modify.

      "both parties voted against proposals to change the law"

    2. replace (something) with something else, especially something of the same kind that is newer or better; substitute one thing for (another).

      "she decided to change her name"

    noun

    1. an act or process through which something becomes different.

      "the change from a nomadic to an agricultural society"

    2. coins as opposed to banknotes.

      "a handful of loose change"

  • adjective

    1. made or produced by human beings rather than occurring naturally, especially as a copy of something natural.

      "her skin glowed in the artificial light"

    2. (of a person or their behaviour) insincere or affected.

      "she gave an artificial smile"

  • noun

    1. the ability to acquire and apply knowledge and skills.

      "an eminent man of great intelligence"

    2. the collection of information of military or political value.

      "the chief of military intelligence"

 

11min read

What Is THIS?

AI is no longer just a technical tool — it’s rapidly becoming a transformational force. It’s changing not just what organisations do, but how they operate, adapt, and evolve — and, at a larger scale, how they contribute to the economic performance of the regions they operate within.

Until recently, AI was treated largely as a specialised technology for IT or data teams. Today, it’s touching every part of the business — from how products are designed, to how customers are served, to how decisions are made at every level. AI is becoming embedded in strategies, operating models, and cultures. It’s also fundamentally shifting the way organisations think about change itself.

In everyday life, the shift is already well underway — and often happening faster than at work. A recent Harvard Business Review article (April 2025) found that most people now use generative AI daily, and increasingly for personal tasks like trip planning, recipe generation, or writing support over strictly professional use cases (HBR, 2025). AI is becoming as common as a smartphone or a search engine — woven into routines, casual conversations, and creative projects.

At work, though, there’s still a big gap.

To take an example from New Zealand: a 2024 Microsoft/Accenture report found that while Kiwis rank equal third globally for generative AI use at work (84% of people report using it), only 19% have access to employer-provided AI tools — well behind the United States, where 37% of workers have such access. (Microsoft, 2024) This mismatch points to a critical truth: while individuals are racing ahead, many organisations are lagging behind in providing structured, safe, and strategic ways to integrate AI into work practices.

Zooming out, the stakes become even clearer: widespread organisational lag could drag on productivity growth and competitiveness at a national level. In economies like New Zealand, where AI-driven productivity gains could significantly lift GDP over time, slow adoption within organisations could limit the benefits that are otherwise within reach.

However, for those organisations that are leading, they are doing so by moving beyond siloed pilot projects to enterprise-wide integration. According to McKinsey, over 78 percent of organisations reported using generative AI in at least one business function (namely, IT, marketing and sales functions, followed by service operations) — up from 72 percent in early 2024 and 55 percent a year earlier. (McKinsey, 2025)

Yet successful integration is about much more than simply deploying AI tools. It requires reimagining business models, redesigning workflows, reskilling people, and aligning AI initiatives with the broader organisational vision and culture. Leading organisations recognise that AI is not a standalone project — it’s a strategic enabler that must be deeply woven into how they operate and grow.

Importantly, this isn’t a gradual, linear shift. AI’s capabilities — from predictive analytics to natural language generation — are advancing exponentially. The organisations adapting now are building the foundations for long-term resilience and advantage, while those hesitating risk falling into irrelevance.

We are at a true inflection point — much like when steam power and mechanised manufacturing redefined industries centuries ago. The leaders of this era will be those who recognise AI not as an isolated IT initiative, but as a full-scale organisational transformation opportunity.

Why this matters?

The pace and scale of AI advancement are unlike anything we've seen before. Organisations aren’t just facing another wave of technological change — they’re facing a structural shift that will reshape markets, customer expectations, operations, and even the nature of work itself.

According to McKinsey’s 2023 report, three-quarters of executives expect generative AI to significantly disrupt their industry's competitive dynamics within just three years. (McKinsey, 2023)
The gap between those who are adapting quickly and those who are hesitating is widening fast — and the consequences won’t be confined within organisational walls.

While AI-driven productivity gains are projected to contribute trillions to the global economy, organisations — especially in smaller or mid-sized economies — risk slower GDP growth, reduced competitiveness, and missed opportunities if they fall behind.

At an enterprise level, the risks and rewards are equally stark. Organisations that integrate AI thoughtfully are already seeing advantages in speed, efficiency, customer insight, and innovation.
Those that delay or underinvest are increasingly vulnerable to disruption — not just from tech giants, but from fast-moving competitors who are embedding AI into how they work, decide, and serve.

But perhaps the most important reason this moment matters is that AI is not just a technology story — it’s a leadership, people, and ethics story too.

Organisational and political leaders have a critical role to play in shaping how AI is adopted — not just whether it’s used, but how it's understood, trusted, and embedded into culture and decision-making.

  • There are ethical considerations: How do we use AI responsibly? How do we ensure fairness, transparency, and human oversight in AI-driven systems?

  • There are human considerations: How do we help employees transition from fear to empowerment? How do we design work that amplifies human strengths rather than eroding them?

  • There are leadership considerations: How do we move beyond pilot projects and tech showcases to real, scalable transformation that aligns with our purpose and values?

In many ways, the biggest determinant of success in the era of AI won’t be the technology itself.
It will be the courage and clarity of leadership, the willingness to invest in people, and the ability to adapt operating models, mindsets, and ways of working to the opportunities — and responsibilities — that AI brings.

Individuals, organisations and nations that lead this shift thoughtfully are likely to find themselves stronger, more resilient, and more human — not in spite of AI, but because of how well they harness it.

How to navigate these times – the tools, methods and practical application

If AI truly marks an inflection point — much like the Industrial Revolution did — then navigating it demands more than technical upgrades. It calls for a shift in leadership mindset, new ways of working, and a deeper investment in people, culture, and capability.

For leaders, the challenge is not just how to deploy AI — but how to integrate it meaningfully and responsibly into the fabric of their organisations.

The good news? We don’t need to reinvent how we lead change.
Many of the same principles that guide successful transformation still apply — they just need to be adapted for a world where the technology is exponential, and the human response to it is emotional, ethical, and evolving.

Here are five practical foundations to guide the journey:

1. Lead with Curiosity, Courage, and Clarity

AI adoption starts at the top.

Leaders must set the tone — with curiosity about the possibilities, courage to move early, and clarity about the purpose behind it.

A clear, human-centred vision for AI is essential — one that positions AI as a tool to amplify human strengths, not replace them. Following John Kotter’s principle of building a compelling case for change, the “why now” and “why this matters” must feel urgent, meaningful, and aligned with broader strategic goals.

A strong narrative around responsible, purposeful AI integration helps overcome resistance and builds trust from the outset.

Kotter’s 8 Steps offer a timeless reminder: create urgency, empower champions, celebrate wins, and anchor the change in culture. In real-world terms, this shows us, change succeeds when it builds momentum — not just compliance. A sense of urgency, empowered champions, visible wins, and strong cultural anchors all help move transformation from intent to action.

  • What it is:
    A step-by-step roadmap for leading organisational change, developed by John Kotter. The eight steps are:

    1. Create urgency

    2. Build a guiding coalition

    3. Form a strategic vision

    4. Enlist a volunteer army

    5. Enable action by removing barriers

    6. Generate short-term wins

    7. Sustain acceleration

    8. Institute the change

    Why it’s useful:
    It’s a practical checklist for creating momentum, aligning teams, and embedding new behaviours — especially valuable when scaling AI from pilot to enterprise level.

2. Invest in People, Not just tools

AI won’t transform your organisation.
Your people using AI well will.

The ultimate value of AI is realised only when people feel confident using it. That means investing real energy in capability-building — not just training programs, but safe spaces to learn, experiment, and grow.

Not everyone needs to become a prompt engineer, but most people will need:

  • Basic AI literacy

  • Confidence with new tools

  • Permission to explore, ask, and try

Leading organisations are building this through:

  • AI academies and communities of practice

  • Peer-led “AI champions” who share knowledge and support colleagues

  • Integration of AI literacy into onboarding and leadership development

Prosci’s ADKAR model is a useful guide: Awareness, Desire, Knowledge, Ability, and Reinforcement. It’s not about ticking boxes — it’s about helping people shift their mindset and behaviour at a human level.

Give people time and encouragement to explore. Treat early efforts as learning, not evaluation. And remember: the biggest barrier to adoption is often emotional, not technical.

Address fear. Celebrate experimentation. Build confidence. That’s how you build momentum that lasts.

  • What it is:
    A people-focused model that helps individuals navigate change. ADKAR stands for:

    • Awareness – understanding the need for change

    • Desire – choosing to engage with the change

    • Knowledge – knowing how to change

    • Ability – turning knowledge into action

    • Reinforcement – sustaining the change over time

    Why it’s useful:
    It helps change leaders ensure individuals are not just informed, but empowered to change. It’s especially useful in guiding training, engagement, and adoption efforts for AI and other large-scale transformations.

3. Move Beyond Pilots to Systemic Integration

The best AI transformations rarely begin with grand declarations.
They start small — with a real use case, a meaningful problem, and a mindset of learning.

Choose one or two high-impact areas where AI can create real value:

  • Reduce admin time

  • Improve customer service

  • Speed up internal decision-making

Run a thoughtful pilot. Track the impact. Capture lessons.
But then — scale with intention.

The risk is that AI stays stuck on the side: a flashy demo, a siloed proof of concept. True transformation means integrating AI into the flow of work — not as an add-on, but as a better way of operating.

This might include:

  • Embedding AI into business processes and decision points

  • Updating job descriptions, roles, and KPIs

  • Clarifying decision rights between humans and AI systems

  • Refreshing governance, risk, and compliance frameworks

This is where models like Kotter’s 8 Steps and ADKAR continue to serve us — not as rigid templates, but as reminders to build awareness, generate early wins, empower champions, and reinforce success.

In other words: build momentum, not mandates.

4. Set Guardrails — Build Trust by Design, Govern ethically, and lead humanly

AI brings immense opportunity — but also new risks around bias, privacy, transparency, and accountability.
This makes governance not just a legal requirement, but a leadership responsibility.

Trust can’t be assumed. It must be designed.

Practical ways to build this trust:

  • Create internal AI ethics committees

  • Define clear guidelines for responsible data use and model oversight

  • Involve diverse voices in design and decision-making

  • Be transparent with employees and customers about how AI is being used — and why

Ethical governance shouldn’t be an afterthought. It should be built into your AI strategy from day one.

Ethics isn’t just a policy — it’s a practice. And in this moment, leadership means asking:
Are we lifting people, or replacing them?
Are we designing systems that reflect our values?
Are we building a future that is inclusive, fair, and worth working toward?

5. Build Change Resilience — Not Just Change Readiness

AI isn’t a one-off shift – it signals that the very nature of change itself is changing.
Faster. More frequent. More complex.

The organisations that thrive will be those that don’t just brace for change — they build the muscle to respond to it, continuously and confidently.

How to start:

  • Invest in a culture of continuous learning

  • Enable local leaders to drive transformation at team level

  • Normalise experimentation and feedback loops

The McKinsey 7-S Framework reminds us: sustainable change happens when strategy, structure, systems, skills, staff, style, and shared values all work in harmony. AI success depends on this full organisational alignment.

  • What it is:
    A model for aligning all parts of an organisation during times of change. The seven interdependent elements are:

    • Strategy – the plan for achieving goals

    • Structure – organisational roles and hierarchy

    • Systems – processes and tools used

    • Skills – capabilities of the workforce

    • Staff – people and talent strategies

    • Style – leadership and culture

    • Shared values – guiding principles and culture

    Why it’s useful:
    It helps leaders diagnose gaps, align strategy with operations, and ensure AI adoption isn’t just a tech layer — but part of how the business works and grows.

In Short

Navigating AI isn’t about the tech.

It’s about people.
It’s about clarity, care, and courage.
It’s about embedding change into the work — not as a side project, but as a new way of operating.

Leaders who embrace this won’t just weather the disruption — they’ll shape what comes next.
Not just more efficient. But more human, more resilient, and more ready for whatever comes after AI.

Closing thoughts

The pace of AI change can feel both exciting and overwhelming — full of possibility, yet loaded with complexity. But one thing is clear: this isn’t a trend to observe from the sidelines. It’s a transformation to lead.

The real challenge — and opportunity — isn’t about the technology itself.
It’s about how we show up as leaders, decision-makers, and changemakers during this moment.

Whether you're deep in the work of enabling change or just beginning to explore how AI might impact your team or organisation, the path forward is the same: start where you are. Stay curious. Engage your people. Lead with purpose.

This is not just a time for implementation — it’s a time for intention.
The organisations that navigate this moment well will be those that stay human, even as they become more digital. Those who move early, learn fast, and centre people in the process will shape not only their own future, but the one we all share.

The question isn’t whether AI will change things – it already is.

The real question is: whether we will lead that change with care, courage, and clarity?




Written by Rebecca Agent, with credit to the following AI tools for assistance in producing this content:

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