Why 95% of AI Projects Fail (And It's Not the Technology)

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.

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?

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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.

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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.