The Gap That AI Tools Don't Close

There’s something nobody warns you about when AI actually works well. Something that catches you off guard the first few times it happens.

You build something genuinely useful. Surface data that was buried, create reports that reveal real insights, automate processes that used to eat up hours. The client sees it working, understands the value, starts using it to make better decisions. Exactly what everyone wanted.

And then a few months later, it’s just part of the furniture. Gartner calls this the “trough of disillusionment” but I think it’s something different. It’s not disillusionment. It’s integration. The technology has become so embedded that it’s invisible. The brilliant piece of work you delivered has become unremarkable. Not ignored, exactly. More like absorbed. It’s just how things work now. Nobody thinks about it much.

The first time this happens it’s a bit deflating. You pour effort into something genuinely transformative and then watch it disappear into normalcy. Where’s the ongoing appreciation? Where’s the recognition of how things used to be versus how they are now?

The brilliant piece of work you delivered has become unremarkable. Not ignored, exactly. More like absorbed. It’s just how things work now.

The right kind of invisible

But I’ve come to think this is actually the right outcome. Maybe the only good outcome.

If people are still impressed by your AI implementation months later, it usually means they’re still thinking of it as separate. A thing apart from how they work. Something to be marvelled at rather than used. That’s not adoption. That’s a novelty.

The work that really lands, the work that actually changes how a business operates, becomes invisible precisely because it worked. It got integrated so thoroughly that people forget there was ever another way. They just do their jobs, and the AI is part of how they do them, and nobody thinks about it because why would you think about something that just works?

It’s like electricity. Nobody marvels at the lights coming on. The expectation of function is so complete that the technology has disappeared entirely.

The appetite for more

I’ve seen this with simpler tools too. When ChatGPT and Claude became widely available, the first response was amazement. Look what this can do. Writing that used to take hours now takes minutes. Research that was tedious is suddenly easy.

That amazement lasted maybe six months for most people. Now it’s just how things are. People use AI to write and research and analyse without really thinking about it. The capability that seemed magical has become mundane. And already they’re asking what else it can do, what comes next, what the next thing is.

There’s a restlessness to it that I find interesting. Each capability that gets absorbed creates appetite for the next one. The more AI can do, the more people want from it. Not because they’re ungrateful but because that’s how expectations work. They adjust to what’s normal and then look beyond.

Measuring differently

I’ve made my peace with this. The best work disappears. The reward isn’t ongoing amazement. It’s watching people move on to the next problem because the old problem is solved. It’s the freedom that comes from having something handled.

If you’re measuring success by how much people appreciate what you built, you’ll be disappointed. But if you’re measuring it by how much capacity people have to think about other things, the picture looks different.

The goal was never to build something impressive. The goal was to make something useful enough that people stop noticing it.

When that happens, you’ve succeeded. Even if it doesn’t feel like celebration.

Looking for AI that becomes part of how your business works? Our AI Clarity Session focuses on building solutions that your team will actually adopt, not just admire. Or explore our real-world AI use cases to see the kind of work that disappears into normalcy because it just works.

There’s something about AI that’s different from other technology we’ve worked with, and I’ve been turning it over in my mind for a while now.

With traditional software, when something changes, you know about it. There’s a release, a changelog, maybe a migration guide if it’s significant. Someone on your team made a decision to update, and if the update breaks something, you can trace back to that moment and figure out what happened.

AI doesn’t work like that. In 2024 alone, OpenAI released over a dozen model updates, Google pushed five major Gemini versions, and Anthropic shipped three Claude generations. Each one subtly changed how existing integrations behaved.

The models we build on top of are maintained by someone else entirely. They get updated on schedules we don’t control, for reasons that aren’t always explained, and the behaviour shifts in ways that might be subtle enough that you don’t notice immediately. Your prompt that worked beautifully for six months starts producing slightly different outputs. Your automation that handled a particular task reliably now misses edge cases it used to catch.

Nothing changed on your end. The foundation just moved a bit.

Your prompt that worked beautifully for six months starts producing slightly different outputs. Nothing changed on your end. The foundation just moved.

Building on shifting ground

I find this fascinating rather than alarming, because it means we need to think about AI systems differently from how we’ve thought about software before. It’s less like building on solid ground and more like building on something that’s slowly shifting. Not unstable exactly, but not static either.

The Gemini models have been an interesting example of this. Google iterates fast, new versions coming out regularly, each one slightly different from the last. If you built something tuned to the behaviour of one version, the next version might handle things differently. Usually better overall, but “better overall” doesn’t always mean “better for your specific use case.”

We’ve started thinking about this in terms of baselines. Before putting something into production, getting really clear on what good looks like. What outputs do we expect? What’s the acceptable range of variation? How would we know if something had drifted outside that range? It’s the kind of discipline that feels obvious once you’re doing it, but it took us a while to realise it was necessary.

Nobody else is watching this for you

The vendors aren’t going to tell you this stuff, not because they’re hiding anything but because they’re focused on capability. They want you excited about what the new version can do, not thinking about how it might affect what you’ve already built. That’s not unreasonable from their perspective. They’re building general-purpose tools for millions of users. Your specific implementation isn’t their concern.

Which means it has to be yours.

I’ve come to see this as just part of working with AI at this stage. The technology is moving fast, the models are improving constantly, and that improvement comes with change. Fighting that seems pointless. Better to build with the expectation that things will shift and have ways of noticing when they do.

A relationship, not an installation

It’s actually quite an interesting problem when you frame it that way. How do you build reliable systems on top of foundations that evolve? How do you maintain quality when the underlying capabilities keep changing? These aren’t solved problems. We’re all figuring it out as we go.

The people who’ll do well with AI in the long run, I suspect, are the ones who get comfortable with that. Not treating AI as a fixed tool that you configure once and forget about, but as something that needs ongoing attention. A relationship more than an installation.

Not treating AI as a fixed tool that you configure once and forget about, but as something that needs ongoing attention. A relationship more than an installation.

That might sound like more work, and it is. But it’s also more interesting. The tools keep getting better, which means the possibilities keep expanding. You just have to stay engaged with it.

Want to build AI systems that stay reliable as models evolve? Our AI Clarity Session helps you establish the baselines and monitoring that keep your AI working as intended. Or explore our real-world AI use cases to see how businesses are building with change in mind.

When businesses think about AI, they tend to think about the future state. What things will look like when it’s all working. The efficiency gains, the time saved, the insights revealed. It’s natural to focus there because that’s where the value is.

The bit that gets less attention is the journey from where people are now to where you want them to be. And that’s usually where things get complicated. McKinsey research consistently shows that around 70% of digital transformation efforts fail, and the primary reason is people, not technology.

I’ve watched projects that had everything going for them on paper struggle to get traction. Good technology, clear use case, leadership support, budget. All the ingredients for success. But the people who were supposed to use the thing just… didn’t. Or did reluctantly, badly, and then stopped.

Understanding the starting point

The people who were supposed to use the thing just… didn’t. Or did reluctantly, badly, and then stopped.

Usually when this happens it’s because nobody spent enough time understanding the starting point. What are people actually doing today? How do they think about their work? What are they worried about? What have their experiences been with previous technology changes?

That last one matters more than you might think. A lot of people have been burned before. New systems that made their jobs harder, not easier. Rollouts that promised efficiency but delivered frustration. Changes that felt like they were being done to them rather than with them.

So when AI comes along, there’s wariness. Another transformation. Another promise that things will be better. They’ve heard this before.

Sceptics become advocates

The interesting thing is that the people who are most cautious about new technology are often the most process-driven and detail-oriented people in the organisation. They’re not resistant because they’re stuck in their ways. They’re resistant because they’ve seen what happens when change is handled badly and they don’t want to go through it again.

Once you understand that, you can work with it rather than against it. Those same people, the ones who ask lots of questions and raise concerns and want to understand exactly how this is going to work, often become the strongest advocates once they’re on board. They bring rigour to the implementation. They think about edge cases that nobody else considered. They make sure things actually work properly rather than just looking good in a demo.

Getting there takes patience. It means explaining not just what the AI does but why it helps with the specific things they care about. It means showing rather than telling, letting people see it work in their context rather than generic examples. It means creating space for scepticism rather than dismissing it.

A human pace

AI needs to understand how work actually happens before it can improve it. And that understanding has to include the people doing the work. Their knowledge, their concerns, their context. Skip that and you end up with technically impressive solutions that nobody uses.

The businesses that handle this well don’t just deploy AI. They bring people along. They treat adoption as something that happens at a human pace, not a technology pace. They understand that the dream state is only valuable if you can actually get there, and getting there requires meeting people where they are.

The dream state is only valuable if you can actually get there. And getting there requires meeting people where they are.

It takes longer. It’s messier than a clean technology rollout. But it’s the difference between a tool that gets used and a tool that gathers dust.

Want to bring your team along on the AI journey? Our AI Clarity Session starts with understanding where your people are now, not just where you want them to be. Or explore our real-world AI use cases to see how other businesses have navigated the human side of AI adoption.

When I mention “governance,” most people immediately think: bureaucracy. Slow. Red tape. “We can’t do this without filling out Form 47 and waiting for committee approval.”

That’s governance done wrong.

Good governance doesn’t slow you down. It accelerates you.

Let me explain.

The Governance Problem (Done Wrong)

Bad governance looks like: “No one can use AI without executive sign-off. All AI decisions go through a committee. All prompts must be reviewed. All outputs must be audited.”

This stops experimentation. It kills momentum. It makes your organisation move like a tank through mud.

So teams bypass it. They use ChatGPT in ways you don’t know about. They build AI systems outside your frameworks. They hide things because the official process is too slow.

That’s not safety. That’s the opposite. That’s fragmentation and risk. Deloitte’s research highlights exactly this problem. Unsanctioned AI deployments by individual teams create governance blind spots that are far more dangerous than the risks governance was meant to prevent.

And the numbers back this up: while 75% of organisations have established AI usage policies, only 36% have adopted a formal governance framework, according to the 2025 AI Governance Benchmark Report. Having a policy without a framework means no consistent roles, controls, monitoring, or enforcement. It’s governance theatre.

The Governance Insight

Here’s what I’ve noticed in organisations that are winning:

They don’t have less governance. They have smarter governance.

They ask: What’s actually risky? And what’s not?

Using ChatGPT to brainstorm marketing copy? Low risk. Let the team do it. No governance needed.

Using an AI model to flag fraudulent transactions? High risk. That needs governance. That needs an audit trail, oversight, validation.

Storing customer data in a third-party AI system? High risk. Governance required.

Processing internal meeting notes? Low risk.

The organisations that move fast don’t have no governance. They have risk-based governance. They apply strict controls where it matters and trust teams where it doesn’t.

What Risk-Based Governance Looks Like

Instead of one process for everything, you have levels:

Green (Low Risk): Team uses AI tools, minimal controls needed. Completion timestamp. That’s it.

Yellow (Medium Risk): Team uses AI, but there’s documentation of the prompt, the output is reviewed by a second person, there’s an audit trail. Takes an extra 30 minutes per project.

Red (High Risk): Governance committee review. Full audit trail. Compliance checklist. Formal sign-off.

What determines the level? Risk factors:

• Is personal data involved?
• Is this a legal or regulatory decision?
• Could bad output cause real harm?
• Are we trusting AI to make a decision, or just to inform one?
• How transparent is the AI’s reasoning?

Most projects are green or yellow. A few are red.

And here’s the key: Teams know upfront which level they’re in. They’re not surprised halfway through. They know what controls they need and plan accordingly.

This is why the AI Native Platform includes a built-in governance framework — so your team can classify projects by risk level and apply the right controls without slowing down the low-risk work. Gartner’s research found that organisations with AI governance platforms are 3.4 times more likely to achieve high effectiveness in AI governance than those without.

How This Enables Speed

Let me give you an example.

Tender analysis: Is it high risk?

Some organisations say yes: “We’re using AI to evaluate proposals. That’s a decision that could be wrong. Governance required.”

Other organisations think differently: “The AI reads the tender, extracts information, and makes a recommendation. A human reads the AI output and makes the decision. The human is responsible. The AI is a tool.”

Different risk assessment. Yellow governance, not red.

With yellow governance, you can:

• Launch projects in 4 weeks, not 12 weeks
• Scale across teams, not wait for approval
• Iterate and improve, not freeze specs
• Trust teams, not create bottlenecks

And you’re still safe. You still have documentation. You still have an audit trail. You still have review. You just don’t have bureaucracy.

The data supports this approach. The Cloud Security Alliance found that organisations with comprehensive AI governance are nearly twice as likely to successfully adopt advanced AI, with 46% adoption versus just 25% for those with only partial guidelines. Good governance doesn’t slow adoption. It enables it.

The Confidence Part

Here’s why this matters for your board and executives:

Bad governance makes it seem like you’re being careful, but you’re creating risk.

Good governance means your leadership can move fast with confidence.

“We evaluated tenders with AI support. The AI recommendation was reviewed by our procurement lead. Here’s the audit trail of the AI analysis. Here’s the decision. Here’s the reasoning.”

That’s auditable. That’s defensible. That’s confidence.

Compare to: “We used ChatGPT to help. I think it worked well. Here’s the outcome.”

That second one is risky. Not because you used AI. Because you can’t explain your process.

This is where structure matters, and it’s one of the reasons the AI Native Programme includes governance setup as part of the monthly partnership. You don’t need to build a framework from scratch. You need one that fits your risk profile and enables your team to move.

What This Means for Your Organisation

If you’re worried about AI risk, good governance is the answer. Not “no AI.” Not “secret AI.” Structured, risk-based governance.

It lets you move confidently. Your team knows what’s expected. Your executives know what’s auditable. Your customers (if relevant) know you’re being responsible.

It’s not bureaucracy. It’s confidence.

And it’s not “no governance vs. full governance.” It’s “smart governance that enables speed where it’s safe and controls where it matters.”

That’s how you actually win with AI.

Keen to talk about what a governance framework might look like for your organisation? Get in touch. It’s usually a shorter conversation than you’d expect.

You could hand the world’s best AI tools to your organisation and you’d still fail 95% of the time.

I know that’s a bold claim. But after working with dozens of teams on AI adoption, I’ve watched the pattern repeat: The technology works fine. The failure isn’t technical.

Rand Group research puts the figure at 87% of AI projects never making it to production. Gartner estimates that through 2025, at least 30% of generative AI projects will be abandoned after proof of concept. Whether the number is 87% or 95%, the root cause is the same: it’s almost never the technology.

The Real Problem

Here’s what actually happens:

A leader reads about ChatGPT or Claude and thinks, “We need this.” A team gets excited. Someone sets up an account. They run a few experiments. And then… silence. The project lives in one person’s head. There’s no measurement of what worked. No training for the wider team. No governance framework. No documentation of what was built. When that person leaves or moves to something else, the whole thing collapses.

Or they implement something bigger: a workflow automation or document processing system. It works brilliantly for three weeks. Then nobody maintains it. The prompts drift. The outputs degrade. No one notices because there’s no monitoring. Three months later, someone asks, “Whatever happened to that AI project?” and nobody remembers.

The pattern is consistent: Technology success + organisational chaos = project failure.

Why This Happens

It’s not stupidity. It’s that AI is new enough that most organisations haven’t figured out the discipline required to make it sustainable.

When you implement a new CRM system, you have processes. A CRM vendor gives you implementation guides. Your team gets trained. There’s a change management plan. There’s governance about who can access what. There are quarterly reviews of adoption metrics.

With AI, we’re still in the Wild West. People experiment in isolation. They build something, it works, and then they expect it to just… keep working. Without maintenance, measurement, or documentation.

MIT Sloan Management Review found that 91% of top data managers cited team challenges and change management, not technology limitations, as the main barriers holding them back with AI. McKinsey’s research reinforces this: companies with a formal AI strategy report an 80% success rate in AI adoption, compared to just 37% for those without one.

The constraint isn’t whether ChatGPT can analyse documents. It’s:

• Does your team have a framework for finding AI opportunities systematically?
• Once you build something, who owns maintaining it?
• How do you measure whether it actually delivered value?
• How do you scale from one team experimenting to the entire organisation moving systematically?
• What happens when someone leaves and nobody knows how the system works?

These aren’t technology questions. They’re discipline questions.

What Actually Works

The organisations that successfully adopt AI do something different:

They treat it like any other critical business capability: discovery, planning, execution, measurement, governance, training.

They don’t ask, “Can we use AI?” They ask, “Where will systematic AI use save the most time, reduce the most risk, or improve the most?” They map opportunities. They prioritise. They run pilots with proper measurement. They scale only what works.

They build documentation as they go. They train their teams. They establish governance that enables fast, safe decision-making rather than blocking it.

They measure ROI clearly. Not to prove AI works, but to understand where it’s creating value and where it’s not.

They treat the first person who builds something as a proof point, not the final solution. They systematically spread that knowledge across the team so it survives when people move on.

This takes discipline. But it’s the difference between a single successful AI project and systematic AI adoption that compounds over time. It’s also why we built the AI Native Programme as a structured monthly partnership rather than a one-off engagement. Sustainable AI adoption requires ongoing structure.

How the Platform Helps

The AI Native Platform is built around this exact insight. It’s not trying to replace ChatGPT or Claude (those are tools you’ll use within the platform). It’s infrastructure for the discipline part.

• Discovery Canvas: systematically find where AI matters most in your business, rather than guessing
• Roadmap & Prioritisation: rank opportunities by impact and effort so you invest in the right projects first
• Task Management: keep AI project execution visible across your team
• ROI Measurement: track actual returns, not assumptions, so you know what’s working
• Governance Framework: move fast without fear of chaos, with built-in risk assessments and policy guardrails
• Training Modules: build team capability so knowledge doesn’t walk out the door

Essentially, the platform exists because we realised that the bottleneck for most organisations isn’t the AI tools. It’s the discipline, measurement, and structure around them.

If you want to see what this looks like in practice, the AI Readiness Assessment is a good place to start. It helps you understand how AI-literate your team is, what tools they’ve already used, and how they feel about using AI in your organisation.

What This Means for Your Organisation

If you’re experimenting with AI today, you’ve probably got ChatGPT, Claude, maybe Copilot. Different teams using different tools. No shared framework for what’s working. No measurement of impact. No governance. You’re probably part of the 95%.

The path forward isn’t more tools. It’s systematic adoption. Discovery, measurement, training, governance, scaling.

That’s not sexy. But it’s what actually works.

Interested in understanding where AI could actually matter in your organisation? Get in touch. I’m happy to chat through your biggest opportunities and where the real bottlenecks are.

I’ll be honest with you - I’m living this contradiction every single day.

In my role as a business owner, I’m constantly looking for ways to cut costs and improve efficiency. When I see a process that could be streamlined or a role that might be automated, there’s this immediate mental calculation: “What would this save us monthly? Could AI handle this?” It’s almost automatic now, this lens of optimisation and cost reduction.

But here’s the thing - I’m obviously a huge AI user. And when I use AI tools, I’m not trying to replace myself. I’m trying to get rid of the tedious stuff so I can focus on what actually matters. I want AI to handle my scheduling, draft my initial emails, and organise my notes so I can spend more time on strategy, relationships, and the work that genuinely energises me.

The contradiction hit me recently when I realised I had been looking at my team’s roles through that “efficiency lens” while protecting my own work from the same scrutiny. I wanted AI to free me up to do more meaningful work, but I was unconsciously viewing their roles as potentially replaceable.

Last week, I stumbled across this Stanford research that perfectly captures what I call the ‘AI Cake Fallacy.’ Everyone wants their slice of the AI productivity gains, but nobody wants to give up the parts that matter most to them. And the data proves this disconnect is real - and it’s killing AI implementations.

The Problem We’re All Dancing Around

The Stanford study surveyed 1,500 workers across 104 occupations and found something that should make every business owner pause. Yes, 46% of workers want AI automation, but only for specific tasks. They want AI to handle scheduling, data entry, and file management. You know, the stuff that’s genuinely dull. They want to focus on strategy, creativity, and meaningful work.

Makes perfect sense when you think about it. Why would anyone want to spend their workday doing stuff they don’t enjoy?

But here’s where it gets interesting. The research shows that 41% of current AI investments are going to what they call the “wrong places” - either low-priority areas or “Red Light Zones” where workers actually resist automation.

We’re literally investing in the stuff people don’t want automated while missing the opportunities where they’d welcome it with open arms.

The Four Zones That Change Everything

The researchers identified four distinct zones, and understanding these may shifted how you think about AI implementation.

There’s the “Green Light Zone” where workers actually want automation and AI can deliver. Think automated scheduling, basic data processing, routine admin work. Companies operating here see both productivity gains and employee satisfaction. It’s the sweet spot we should all be aiming for.

Then there’s the “Red Light Zone” - high AI capability but low worker desire. This is where we often get seduced by what’s technically possible rather than what’s actually wanted. Creative tasks, strategic planning, client relationship management. Force AI into these areas and you’ll create resistance and disengagement faster than you can say “digital transformation.”

The study found that Arts and Design workers only wanted 17% of their tasks automated. Editorial roles consistently rated as requiring essential human involvement. The pattern is clear - workers fiercely protect tasks involving creative expression, human relationships, strategic thinking, and work they find genuinely enjoyable.

The Reality Check I Needed

There’s a cycle that is all too familiar. We saw it with failed digital transformation projects as well. Initial excitement about AI capabilities, followed by the slow realisation that implementation isn’t working as expected, then confusion about what went wrong, followed by the frustrated “just make it work” directive that leads nowhere.

We skip the critical step of understanding what our people actually value about their work before trying to automate it away. We assume that because AI can technically do something, it should do something.

What Workers Actually Want

The most telling finding from the Stanford research is that workers prefer equal partnership with AI, not replacement. They want collaboration, not elimination. This isn’t about being resistant to change - it’s about being protective of meaning.

When you dig into what creates the strongest resistance, it’s the human-centred stuff. Customer service conversations where relationship building matters. Sales interactions that require reading between the lines. Training and mentoring colleagues where empathy makes the difference. Any work requiring genuine human judgment or emotional intelligence.

Workers will gladly hand over the scheduling, the data entry, the routine administrative tasks. But ask them to surrender the creative problem-solving, the strategic thinking, the work that makes them feel human, and you’ll hit a wall.

The Skills Revolution We’re Missing

The research reveals something important about where we’re heading. There’s a clear shift happening from information-processing skills toward interpersonal and organisational competencies. The traditionally high-wage work like data analysis is becoming less valuable, while relationship building, creative thinking, and strategic judgment are becoming more important.

This isn’t just about redistribution - it’s about fundamental redefinition of value. The most valuable human skills are becoming the ones that are most distinctly human.

Getting This Right

The companies that succeed with AI won’t be the ones who automate the most. They’ll be the ones who understand that AI works best when it amplifies human capability rather than replacing it.

This means having real conversations with your team about which tasks they’d actually want automated. It means understanding what they find meaningful about their work before you try to optimise it away. It means designing AI implementation with them, not to them.

The Bigger Picture

The Stanford research proves what many of us have suspected - the real opportunity lies in that collaboration zone where AI handles the tedious while humans focus on what they do best: thinking, creating, relating, and innovating.

As business owners, we need to resist the temptation to see AI as primarily a cost-cutting tool. Yes, it can reduce costs, but its real value lies in freeing our people to do more valuable work. When we get this right, we don’t just save money - we create more value.

The question isn’t whether your business can have its AI cake and eat it too. It’s whether you’re smart enough to share the cake in a way that everyone wins. Because the research is clear - when you get this alignment right, both productivity and satisfaction go up.

And honestly, isn’t that the kind of transformation we actually want to be part of?

Ready to align your AI investment with what your team actually wants? Our AI Clarity Session helps you identify the green-light opportunities where AI delivers both productivity gains and employee satisfaction. Or explore our real-world AI use cases to see practical examples of people-first automation in action.

The biggest hurdles when adopting AI aren’t about the tech. They’re actually about you - how will you lead AI adoption in your organisation?

The biggest hurdles when adopting AI aren’t about the tech. They’re actually about you.

A report from McKinsey said, “The biggest hurdle to success [with AI] is leadership.” And it’s a common theme in other findings. MIT Sloan Management Review (April 2025), found that 91% of top data managers said that “team challenges and managing change” were the main problems holding them back with AI.

For Kiwi businesses, getting AI working well isn’t just about buying new software or hiring a tech guru. It’s about how you, as the leader, tell the story of AI, get your people ready, and weave it into your unique business.

Your role in leading AI adoption

Sharing the vision: If you don’t have a clear idea of why you’re bringing in AI, or what you want it to achieve, your efforts can end up all over the place. As Codewave pointed out (May 2025), “AI actually helps the business when the top leader leads the way.” Your team needs to understand why and how AI is coming into your company.

Turning worries into wins: It’s fair enough for people to worry about AI taking their jobs. But staff are often less scared of AI itself, and more worried about how it will change their daily work, especially when things aren’t clear. This is where your leadership is crucial. When you show that AI is here to help people and not replace them - it changes everything. For example, Microsoft’s own studies (April 2025) found that staff using an AI Copilot, saw a 10-20% boost in getting things done and 68% felt happier at work. It helped free them up for more interesting, important (and valuable) work, and boosted their confidence in their future.

Leading the journey: Bringing in AI means some changes in the workplace. You need to be the kind of leader who can guide your team through this. You can do this by setting clear expectations, letting them try out new things, and building trust. When you, as the leader, actively share exploration of AI and its benefits, you create a workplace where your team sees it as a helpful partner, not something to be afraid of.

Adopting AI is less about a tech race and more about a thoughtful, people-focused journey. For Kiwi businesses, this means looking beyond the gadgets and truly focusing on your team and your style of leadership. When you, the leader, are strong and understanding, your AI plans will have a clear purpose, get results, and grow right alongside your business.

What’s your biggest question or fear about leading AI adoption within your business? We’d really love to hear from you.

Want to develop your AI leadership skills with expert guidance? Our AI Clarity Session gives you a structured 2-hour workshop to build your AI vision and roadmap. For ongoing support, the AI Native Programme helps you lead your team through the full adoption journey. You might also enjoy our related article on leading AI adoption with a clear vision.