Why More Than 85% of AI Projects Fail—and It’s Not About the Data

For years, we’ve been told that “data is the new oil,” and businesses have raced to collect as much of it as possible. But here’s the shocking truth: data alone won’t save your AI project. In fact, the real secret to AI success isn’t about how much data you have—it’s about the expertise driving the project.

The numbers don’t lie: more than 85% of AI projects fail, according to Gartner. And the leading cause isn’t bad data. It’s the lack of experienced professionals who know how to leverage it effectively.

Let’s break down why expertise is the #1 factor for AI success—and why so many businesses get it wrong.

The Reality of AI Project Failure

AI initiatives are notorious for failing to deliver results. Despite massive investments, companies often end up with incomplete, ineffective, or abandoned projects.

The reasons behind these failures reveal a common pattern:

1. Undefined Goals: Companies don’t know what problem they’re solving.

2. Skill Gaps: Teams lack the expertise to turn data into actionable insights.

3. Overreliance on Data: Organizations prioritize data collection over strategy and execution.

4. Poor Integration: AI systems fail because they don’t fit into existing workflows.

This overemphasis on data blinds companies to the real key to AI success: having the right expertise at every stage of the project.

Why Expertise Matters More Than Data

1. Defining the Right Problem to Solve

AI isn’t magic—it needs clear objectives. Experts with both technical and domain knowledge can frame problems in ways that make AI solutions meaningful and achievable.

For example, a manufacturing company might collect production line data but lacks experts to pinpoint the real bottlenecks AI can address. Without this clarity, the project fails before it even begins.

2. Building and Optimizing Models

AI models don’t build themselves. They require skilled professionals who know:

• How to select the right algorithms,
• How to handle bias in datasets, and
• How to fine-tune models for real-world performance.

Example: Companies often assume more data equals better results. But an expert can use techniques like transfer learning to achieve high accuracy with minimal data—something an inexperienced team might overlook.

3. Operationalizing AI Solutions

Deployment isn’t just a technical challenge—it’s an organizational one. Experts understand how to integrate AI tools into workflows, making them practical and scalable.

Without expertise, companies risk spending millions on AI that no one uses because it doesn’t fit the business’s day-to-day operations.

4. Mitigating Risks and Ensuring Compliance

From ethical concerns to regulatory requirements, AI projects come with risks that only experienced professionals can anticipate and navigate.

Consider the backlash against AI systems accused of bias. These issues often stem from poor oversight—not bad data. Experienced teams know how to mitigate these risks before they become PR disasters.

The Myth of Data Supremacy

While data is critical, it’s only as valuable as the team interpreting and using it. Many companies waste resources chasing massive datasets when they should focus on hiring and retaining AI talent.

In fact, a McKinsey report reveals that companies with strong AI expertise are 2.5 times more likely to see significant ROI compared to those focusing solely on data.

The Key Insight:

Data is abundant, but expertise is rare. And that makes expertise far more valuable than any dataset.

The Takeaway: Expertise Drives AI Success

If you’re investing in AI, prioritize expertise over everything else. Start with these steps:

1. Hire Strategically & Start by Outsourcing Experts: Build a team of experienced data scientists and have them work directly with proven experts in this area allowing your internal team to upskill at the same time.

2. Focus on Use Cases: Define clear, achievable objectives before diving into data collection.

3. Invest in Training: Upskill your team to stay ahead in the rapidly evolving AI landscape.

The next time someone tells you “data is the new oil,” remember this: Without expertise, all the data in the world is just wasted potential.

Don’t let your AI project become another statistic. Focus on expertise, and success will follow.