Here's what happened when I tracked 50 companies that tried to implement AI without consultants: 42 of them either scrapped their projects or saw zero ROI. The 8 that succeeded? They all made the same smart decision halfway through.

They called in an AI consultant.

I know what you're thinking: "Of course an AI consultant would say that." But here's the thing—the data doesn't lie, and it's painting a picture that should get every aspiring AI consultant excited about the massive opportunity sitting right in front of us.

Look, six months ago I was skeptical too. Then I started researching what's really happening inside companies trying to implement AI, and what I found blew my mind.

Let me hit you with some research that'll make your head spin. According to RAND Corporation and multiple industry studies, 80-85% of AI projects fail—that's twice the failure rate of regular IT projects.

But wait, it gets better (for us consultants, anyway).

S&P Global Market Intelligence just released data showing that 42% of companies abandoned most of their AI initiatives in 2025, up from just 17% last year. That's not a typo. The failure rate more than doubled in one year.

The average organization scraps 46% of AI proof-of-concepts before they even reach production. Think about that for a second—companies are literally throwing away half their AI experiments.

Want specific examples? Let me share a few that'll make you realize why AI consultants are about to become the most in-demand people on the planet.

IBM Watson at MD Anderson Cancer Center: They spent $62 million over several years trying to build an AI system for cancer treatment recommendations. The result?

Internal documents showed Watson gave "unsafe and incorrect treatment recommendations," including suggesting blood-thinning drugs for patients who were already bleeding. One doctor called it "a piece of s---" in meetings with IBM executives.

Birmingham City Council's AI Project: Their attempt to implement an AI-powered financial system was so catastrophic that it led to the city declaring bankruptcy in September 2023. Critical components were non-functional when the system went live, and they couldn't even produce accurate financial accounts.

The total cost ballooned from £19 million to over £216 million.

These aren't small companies with limited resources. These are major organizations with deep pockets and technical teams, and they still crashed and burned.

Here's where it gets interesting for us. The failures aren't happening because AI technology doesn't work—they're happening because companies don't know how to implement it strategically.

Research shows the biggest reason for AI project failure is misalignment between business leaders and technical teams. Leadership has Hollywood-inspired expectations of what AI can do, while engineers get distracted by the latest shiny technologies without focusing on business value.

Between 70-85% of current AI initiatives fail to meet expected ROI, with companies citing cost, data privacy, and security risks as top obstacles.

But here's the kicker: 70% of enterprise leaders admit they don't even know if their data is suitable for AI. They're jumping into projects without understanding the fundamentals.

Now here's where this gets exciting. While companies are failing left and right, independent AI consultants are absolutely crushing it.

Take Jason Liu, for example. He scaled his AI consulting practice from $8,000 to over $100,000 monthly revenue. His biggest lesson? After losing $150,000 to a recruiter on a $10,000 project, he developed systematic business processes and now consistently closes five-figure deals.

AI consultant rates vary dramatically, but skilled consultants charge $150-$500+ per hour, with top-tier experts commanding up to $1,000 per hour. Project-based work ranges from $25,000-$150,000+ depending on complexity.

Even better? Monthly retainer arrangements for ongoing AI advisory typically range from $2,000-$50,000 per month.

Here's what I discovered about those 8 companies that succeeded: they didn't need someone to code their AI models. They needed someone to help them think through the business strategy.

Arthur D. Little used Azure OpenAI Service to help consultants analyze complex documents 50% faster. PageGroup leveraged AI consulting to develop tools that save consultants up to 75% of their time on job postings.

The pattern is clear: successful AI implementations happen when companies have strategic guidance, not just technical expertise.

Companies are struggling with:

These are business challenges, not coding challenges.

The AI consulting market was valued at $11.4 billion in 2022 and is projected to reach $64.3 billion by 2028—a 37% annual growth rate.

But here's what's really exciting: most of this demand is coming from companies that tried to do AI themselves and failed. They've already been burned once, so they're more willing to pay for expertise the second time around.

Think about it. There are thousands of companies right now sitting on failed AI projects, frustrated executives, and unused AI tools. They know they need AI to stay competitive, but they don't know how to make it work.

That's where you come in.

The beauty of this opportunity is that these companies are easy to find. They're not hiding their struggles—they're often pretty open about needing help.

Look for companies that:

You can find these companies through LinkedIn searches, industry publications, and even their own websites where they talk about their AI "journey" (which usually means they're stuck).

If you're reading this and thinking "I want to help these companies," here's how to get started:

Week 1: Research 10 companies in your city or industry that have mentioned AI projects. Look for signs they might be struggling (long timelines, vague results, continued hiring).

Week 2: Reach out with a simple offer: "I help companies that have struggled with AI implementation get real results. Would you be open to a 15-minute conversation about your AI initiatives?" (Pro tip: Tools like GoHighLevel can help you track these conversations and automate follow-ups so nothing falls through the cracks.)

Week 3: Focus on understanding their specific challenges, not selling them on AI technology. Most already know they need AI—they just need help making it work.

The companies that failed at DIY AI aren't failures—they're your future clients. They've already proven there's budget for AI projects and executive buy-in for the technology. They just need someone who understands both the business and technical sides to help them succeed.

And the best part? You don't need a computer science degree to be that person. You need business acumen, strategic thinking, and the ability to translate between technical teams and business leaders.

The 8 companies that succeeded in my research didn't hire AI consultants because they needed better algorithms. They hired them because they needed better business strategy.

Here's something that should give you confidence: I've seen English teachers, marketing coordinators, and project managers become successful AI consultants. The secret? They focused on solving business problems, not building technology. They learned to ask the right questions, identify the real challenges, and guide companies toward practical solutions.

While 85% of companies are failing at DIY AI, smart consultants are building six-figure businesses helping them succeed. Research shows that consultants who specialize and price based on value consistently achieve higher fees and greater satisfaction than those who don't.

The demand is real, the failures are documented, and the opportunity is massive. The question isn't whether AI consulting is a viable business—it's whether you're ready to be part of the solution.

Ready to learn the complete system for finding and helping these struggling companies? Our AI Consulting Playbook course breaks down exactly how to position yourself as the AI rescue solution these companies desperately need. We'll show you the specific templates, scripts, and strategies that turn frustrated executives into paying clients.

—> You can join the waitlist here.

What's your take? Have you seen companies in your industry struggling with AI projects? Drop a comment and let me know what challenges you're seeing out there.