Why 90% of AI Projects Fail (And How to Know When Yours Is at Risk)

Artificial Intelligence (AI) is often hailed as the ultimate game changer for businesses. Yet, despite the hype, 90% of AI projects fail to deliver a return on investment (ROI), and 60% fail outright [1, 2]. For brands investing heavily in AI, this isn’t just a financial risk, it’s a reputational one.

At The Brand Auditors, we’ve seen firsthand how AI initiatives can go sideways. The good news? Most failures are preventable. The key is recognizing the warning signs early.

In this post, we’ll break down why AI projects fail and how you can diagnose whether your project is at risk before it’s too late.

The foundation of failure: Why AI projects go sideways

AI projects don’t fail overnight. They crumble due to systemic oversights, misaligned strategies, and unchecked risks. Here are the most common pitfalls:

1. “Chasing the Hype” Syndrome: Lack of a clear business problem

Many organizations jump into AI and machine learning because it’s the “next big thing,” not because they’ve identified a specific, measurable business problem to solve.

Symptoms to watch for:

• Your AI project was greenlit because “everyone else is doing it.”

• You’re struggling to articulate how AI will directly impact revenue, efficiency, or customer experience.

• The project scope keeps expanding because there’s no clear objective.

The risk:

Without a defined problem, you’re building a solution in search of a problem and one that’s destined for obscurity.

2. The “Garbage In, Garbage Out” Trap: Poor data quality

AI models are only as good as the data they’re trained on. If your data is incomplete, biased, outdated, or poorly structured, your AI will produce flawed, unreliable, or even harmful outputs.

Symptoms to watch for:

• Your data is siloed across departments, with no single source of truth.

• You haven’t audited your data for accuracy, relevance, or bias.

• Your team assumes the data is “good enough” without verification.

The risk:

Poor data quality increases failure rate. You can also expect costly mistakes, lost trust, and wasted resources.

3. The Blind Leap: Inadequate risk assessment

AI is not just a technical challenge. It’s also a strategic, ethical, and operational one. Skipping a thorough risk assessment is like sailing into uncharted waters without a map.

Symptoms to watch for:

• No one has assessed technical risks (e.g., model drift, integration issues).

• Ethical risks (e.g., bias, privacy concerns) haven’t been addressed.

• There’s no plan for unexpected costs (e.g., cloud compute, third-party services).

The risk:

Unchecked risks lead to project delays, budget overruns, and even potential PR disasters.

Beyond the basics: Are you truly “AI ready”?

Even if you avoid the potential pitfalls above, systemic challenges can still derail your AI project. Here’s what most organizations overlook:

4. Organizational Readiness: More than just data scientists

AI success isn’t just about hiring a team of data scientists. It requires holistic organizational readiness.

Ask yourself:

• Cultural buy-in: Does leadership endorse AI systems, or are are they just an IT experiment? Are employees willing (and trained) to adapt to AI driven processes?

• Data infrastructure: Is your data accessible, well-organized, and governed by clear policies? Or is it scattered across silos?

• Talent  and skills: Do you have the expertise to build, maintain, interpret, and deploy AI tools effectively?

The risk:

Without organizational readiness, even the best AI models will fail to deliver results.

5. The ROI Disconnect: Vague goals, vague returns

Many AI projects technically “succeed” but still fail to deliver ROI. Why? Because no one defined what “success” looks like.

Symptoms to watch for:

• Your AI project’s goals are vague (e.g., “improve efficiency”).

• You haven’t tied AI outcomes to specific business KPIs (e.g., revenue growth, cost reduction).

• No one is tracking hidden costs (e.g., cloud services, maintenance, retraining).

The risk:

Without clear KPIs, you’ll never know if your AI project is actually delivering value.

How to diagnose your AI project’s health

Now that you know the warning signs, here’s how to assess whether your AI project is at risk:

Step 1: Audit your business case

• Question: “What specific business problem are we solving with AI?”

• Red flag: If the answer is vague (e.g., “We want to be more innovative”), your project lacks direction.

Step 2: Assess your data quality

• Question: “Is our data accurate, complete, and unbiased?”

• Red flag: If you haven’t audited your data, you’re flying blind.

Step 3: Evaluate organizational readiness

• Question: “Do we have the culture, infrastructure, and skills to support AI?”

• Red flag: If leadership isn’t aligned or employees resist change, your project will struggle.

Step 4: Review your risk management plan

• Question: “Have we identified and mitigated technical, ethical, and operational risks?”

• Red flag: If risks haven’t been documented, they’ll likely derail your project.

Step 5: Define your ROI metrics

• Question: “How will we measure success, and what’s our threshold for ROI?”

• Red flag: If you can’t answer this, your project is set up for disappointment.

What’s next? Diagnose your AI project

If any of the symptoms or red flags above resonate, your AI project may be at risk. The good news? Most failures are preventable with the right approach.

At The Brand Auditors, we specialize in auditing AI projects to ensure they deliver tangible, measurable results. Our framework helps you:

• Clarify your business case (so you’re solving the right problem).

• Assess data quality and readiness (so your AI has a solid foundation).

• Evaluate organizational readiness (so your team is prepared for success).

• Identify and mitigate risks (so nothing derails your project).

• Define and track ROI (so you know exactly what success looks like).

Ready to diagnose your AI project?

[Download our free “AI Project Risk Checklist“] to identify potential pitfalls and learn how to fix them.

References
[1] MIT Sloan Management Review (2020). “Only 10% of organizations achieve significant financial benefits with AI.”
[2] Gartner (2018). “Up to 85% of AI projects fail to deliver on their intended promises.”