5 AI Adoption Mistakes (and How to Avoid Them)

Author: Dr. Sarah Mitchell, Founder & CEO Anadyne IQ

---

AI can make work smoother, decisions sharper, and admin lighter. But all too often, promising AI projects fall flat. And it’s not because the tech isn’t ready, or good. It’s because the right support isn’t in place - that’s where most teams go wrong.

In my work with organisations across Aotearoa, I’ve seen five common AI adoption mistakes that quietly slow things down or introduce unnecessary risk. Below, I break down what they are, why they happen, and how to avoid them. Plus, I’ve also included some practical tools to help you build momentum without the missteps.

AI Adoption Mistake 1: No Clear Ownership

AI often starts informally. Someone explores an AI tool. A pilot gets underway. A few results look promising. But no one actually owns it. Legal thinks it’s IT’s job. IT assumes strategy will lead. Privacy isn’t consulted. Risk isn’t looped in. Things move forward, until they don’t.

Without clear roles, things stall. Tools are used without proper review. Decisions get delayed. Questions go unanswered. People aren’t sure what they’re allowed to do or who to ask. That’s when unnecessary risk creeps in - risk that could have been avoided with just a bit more clarity upfront.

This doesn’t need to be complicated, but it does need to be clear. Who approves new tools? Who keeps the policy up to date? Who supports teams day to day? Who is responsible for the tech? Who looks at privacy, compliance, and risk?

✅ One practical fix

Use an AI Governance Roles Map to define responsibilities across leadership, IT, tech, legal, risk, privacy, and operations. Even a simple roles register helps keep decisions moving and keeps risk in check.

AI Adoption Mistake 2: Solving the Wrong Problem

It’s easy to start an AI project by trying to solve the problem. I know, that sounds like a decent first step. But it's actually the wrong approach. A better first step is figuring out which problem you actually need to solve. Consider this common scenario: an admin process appears inefficient, so a team jumps ahead to fixing it with automation, or with the latest fandangled AI tool. But perhaps the real issue might not be the process at all. The root cause could sit somewhere else entirely. So, what happens? You end up streamlining the wrong step, solving a surface symptom, or skipping past the actual problem altogether.

When this happens, the tech can work perfectly well and still deliver no real value. Time and budget are used up. The team feels like they missed something. And future projects face more resistance, because the last one didn’t land.

The key is to slow down and check you’re solving the right thing. It may sound counterintuitive, but moving fast later depends on slowing down at the start. Ask the people closest to the work. Don’t stop at the first answer, ask “why” a few times. For example, a consistent delay in loan approvals might not be about slow decision-making. It could be about missing data, poor handovers, or unclear responsibility upstream.

If you're not sure where to start, working with an experienced AI consultant can help identify blind spots and keep things moving.

✅ One practical fix

Use the AI Use Case Lean Canvas to map out the problem, the people it affects, the outcome you want, and what success actually looks like. It gives teams a shared starting point and a reality check before jumping to solutions. As the project evolves, keep circling back to that original question — are we still solving the right problem?

AI Adoption Mistake 3: Choosing AI When It's Not the Best Solution

Once you know what problem you need to solve, the next step is choosing how to solve it. That’s also where things can easily go sideways. With all the current hype, AI is top of mind for many teams which means it can often become the default approach. A task feels repetitive? Let’s find an AI tool. A process seems clunky? Maybe an AI assistant will fix it.

But not every problem needs AI. And not every process is improved by adding another layer of technology. Sometimes a better workflow, a dashboard tweak, or clearer responsibilities will get you further, faster.

It’s completely normal to want to use AI to improve efficiency, demonstrate innovation, or stay ahead of competitors. But when you apply it to the wrong kind of problem, it adds cost and complexity without real benefit. You end up with more confusion and less impact, and you'll miss the quick wins already within reach.

Before you choose AI, pause and ask: what’s really needed here? Is the challenge about decisions, data, or something else entirely? Are there tools or fixes you already have that could work?

✅ One practical fix

Use the AI Fit-for-Purpose Questionnaire to help your team assess whether AI is the best option, or whether the solution might already be in front of you.

AI Adoption Mistake 4: Trying to Do Too Much, Too Soon

AI creates a lot of excitement. One successful pilot can quickly turn into a list of ten new ideas. Suddenly there’s a roadmap with multiple tools, multiple teams, and ambitious goals. All happening at once. This is something I like to call decision spaghetti.

So, what happens to that early momentum you’ve built? It doesn't last. It gets lost in the new tangle of priorities, platforms, approvals, and expectations. Projects stall. Teams get overwhelmed. Data isn’t where it needs to be. Everything that seemed achievable starts to feel messy. And difficult.

What’s missing isn’t necessarily capability. It’s focus. And clarity. Trying to roll out AI across too many areas at once makes it hard to deliver anything meaningful. You miss the chance to learn, refine, and build confidence as you go.

Start smaller than you think. Much smaller. Choose one clear problem, one tool, and one team. Deliver something useful. Share what worked. Then build from there. Using experience, not guesswork.

✅ One practical fix

Use the Use Case Prioritisation Matrix to sort potential projects based on impact, effort, and feasibility. It helps teams pick something small, specific, and achievable. You’re looking for the kind of win that delivers value, builds momentum, and earns trust.

AI Adoption Mistake 5: Letting Shadow AI Spread Without Oversight

While you’re planning how to get started with AI, your team may already be using it. Quietly, and without permission. One person is testing ChatGPT to draft content. Another is running prompts through Copilot to speed up admin. A developer is coding with Cursor. Maybe it started as curiosity or a quick fix. But now it’s part of how daily work gets done.

This is what’s often called Shadow AI. It’s the use of AI tools without formal approval, oversight, or clear guidance. And it’s more common than most organisations realise. According to Microsoft’s 2024 Work Trend Index, seventy-eight percent of AI users are bringing their own tools to work. That might sound high, but it closely matches what I’m seeing in practice across teams.

Experimentation isn’t the problem. That’s where many great ideas and innovation starts. But without clear support in place, that curiosity can introduce avoidable risk. Teams may not know what’s safe to use, what’s expected, or who to ask. Data can be exposed. Tools may be used in ways that don’t align with your values, compliance needs, or privacy obligations.

If you want teams to use AI well, they need structure. Not red tape for the sake of it, but practical guidance that helps people make good decisions and reduces risk. AI policies and supporting AI literacy is a big part of this. People need more than permission, they need context, clarity, and confidence in how to use these tools well.

✅ One practical fix

Put in place a clear, accessible AI Use Policy, supported by a shared AI Tool Register and practical staff training that brings your policy to life. This will give your team the confidence to use AI responsibly and help your organisation stay safe, consistent, and on track as usage grows.

Bringing It All Together

AI doesn’t fall short because the technology isn’t ready. It falls short when the foundations aren’t in place. Clear ownership, focused projects, the right tools for the job, realistic scope, and a bit of structure to support teams - these are the things that make adoption successful. And they’re all things you can put in place today.

To make that easier, I’ve created the AI Adoption Toolkit. It's practical set of templates and tools designed to help you avoid the most common missteps and build momentum the right way. It includes:

• An AI Governance Roles Map to define ownership and responsibilities

• A Lean Use Case Canvas to clarify the problem and what success looks like

• A Fit-for-Purpose Questionnaire to check if AI is the right solution

• A Use Case Prioritisation Matrix to help teams start small and stay focused

• An AI Use Policy and Tool Register to support safe, consistent use
  

Each resource is practical, flexible, and ready to use, whether you’re just starting out or need to bring more structure to existing work. If you’re interested in purchasing the toolkit or want to talk through what’s included, get in touch. I’d be happy to walk you through it.

Until next time,

Sarah

Founder, Anadyne IQ