You’d think by now we’d have figured out how to implement AI successfully. The technology has matured. Best practices are documented. Case studies abound.

And yet, AI projects keep failing at roughly the same rate they did three years ago. Why? Because the failures aren’t about technology. They’re about how organisations approach AI – and those patterns haven’t changed.

Here are the seven mistakes I keep seeing, along with what actually works.

Mistake 1: Starting With Technology, Not Problems

The conversation goes like this: “We need to implement AI. What should we do?” This question has it backwards.

The right question is: “What business problems could AI help solve?” Starting with technology leads to solutions looking for problems. Starting with problems leads to appropriate solutions – which may or may not involve AI.

What to do instead: Identify specific, measurable business problems. Evaluate whether AI is the right approach compared to alternatives. Only proceed with AI when there’s a clear problem-solution fit.

I’ve seen organisations spend hundreds of thousands on AI projects that could have been solved with basic automation or process improvement. Technology enthusiasm isn’t a strategy.

Mistake 2: Underinvesting in Data Work

Every AI project plan I review allocates about 20% of budget and timeline to data preparation. Every completed project reports data work consumed 50-60% of actual effort.

This isn’t a minor estimation error. It’s a systematic failure to understand what AI projects require.

What to do instead: Audit your data before committing to AI. Understand quality, accessibility, and integration requirements. Budget realistically – at least 50% of effort for data work on any project involving enterprise data.

The organisations that get this right have already invested in data foundations before their AI initiatives start. Those that haven’t are paying the data tax on every project.

Mistake 3: Treating Pilots as Production

Pilot projects are designed to demonstrate possibility. They typically use small datasets, enthusiastic users, and simplified scenarios. Success in pilots doesn’t indicate success at scale.

Yet organisations routinely approve full rollouts based on pilot results, then are surprised when scaled deployment underperforms.

What to do instead: Design pilots to test scalability risks, not just functionality. Include realistic data volumes, average users (not just enthusiasts), and production-like conditions. Define explicit criteria for pilot-to-production graduation.

A pilot should answer “can this work at scale?” not just “can this work at all?”

Mistake 4: Ignoring Change Management Until Too Late

I covered this topic in detail recently, but it’s worth repeating: AI projects fail more often due to adoption problems than technical problems.

The pattern: technical team builds AI solution, deploys it, and expects users to embrace it. Users don’t. Adoption stalls. Project is declared a failure despite technically functional AI.

What to do instead: Start change management when you start the project, not when you finish it. Involve end users in design. Build training and support into the timeline. Measure adoption, not just deployment.

Every AI project is a change management project. If your budget doesn’t reflect that, revise it.

Mistake 5: No Clear Ownership

“The AI project” belongs to everyone and therefore to no one. IT owns the infrastructure. Data science owns the models. Business owns the use case. Nobody owns the outcome.

This fragmented ownership leads to finger-pointing when things go wrong and paralysis when decisions are needed.

What to do instead: Assign clear ownership of AI outcomes to a business leader with authority and accountability. Technical teams support; business owns. This person is responsible for value delivery, not just project completion.

If you can’t identify who would be fired if the AI project fails, you don’t have adequate ownership.

Mistake 6: Expecting Immediate Results

AI capabilities take time to deliver value. Models need training and refinement. Users need to develop skills. Processes need adjustment. Data quality improves through use.

But executive sponsors often expect transformational results in the first quarter. When those results don’t materialise, support evaporates.

What to do instead: Set realistic timelines. Most enterprise AI projects take 12-18 months to show significant results. Early milestones should be adoption and learning, not business impact. Educate sponsors about the maturation curve.

Patient investment in proven approaches beats impatient investment in silver bullets.

Mistake 7: Building Instead of Buying

Custom AI development is expensive, slow, and requires ongoing investment. Platform capabilities have improved dramatically. For many use cases, off-the-shelf solutions now outperform what organisations can build internally.

Yet many enterprises default to custom development, often because technical teams prefer building to integrating.

What to do instead: Start with platform capabilities. AI consultants Sydney consistently recommend evaluating build-vs-buy before committing to custom development. Build custom only when you have a genuine differentiator that platforms can’t address.

The organisations getting the best AI ROI are often those using Microsoft Copilot, Azure AI, or Bedrock – not those with custom models.

The Meta-Pattern

These seven mistakes share a common thread: they’re all about how organisations think about AI, not about AI technology itself.

The technology works. Models are sophisticated. Platforms are mature. Infrastructure is available. The constraints on enterprise AI success are organisational, not technical.

Fixing these mistakes requires changing how organisations approach AI:

• From technology-first to problem-first
• From project to operating capability
• From IT ownership to business ownership
• From short-term expectations to patient investment
• From build to buy where appropriate

These shifts are harder than upgrading software. They require changing habits, structures, and mindsets. That’s why the failure rate hasn’t improved despite better technology.

The Optimistic Note

The organisations that do change their approach are getting substantial value from AI. They’re the minority, but they prove it’s possible.

What distinguishes them:

• Ruthless focus on business problems
• Realistic expectations about timelines and effort
• Strong business ownership of AI outcomes
• Investment in data foundations before AI initiatives
• Commitment to change management throughout

None of this is mysterious. It’s just disciplined execution of known principles. The competitive advantage goes to those who actually do it rather than just knowing about it.

Which will your organisation be?