'''## Why I Fired My Entire AI Consulting Team and Started Over. We were on the brink of collapse, a classic 'AI for everyone' startup with no focus. Then, one painful decision changed everything. Here's the real playbook for finding your niche in enterprise AI. They called us 'unfocused' and 'a solution looking for a problem.' They were right. This is the story of how we found our multi-million dollar niche by doing the one thing everyone told us not to. I still remember the silence in the room. The kind of silence that’s so heavy you can feel it in your chest. I had just told my entire AI consulting team—some of the brightest minds I’d ever worked with—that they were all being let go. It was the hardest decision of my career, but it was the only way to save the company. We had fallen into the classic trap of being a solution in search of a problem. We were a team of AI experts, and we could do anything. That was the problem. We were trying to do everything for everyone. We had a portfolio of projects that was a mile wide and an inch deep. One day we were building a chatbot for a local bakery, the next we were trying to predict customer churn for a Fortune 500 company. We were constantly context-switching, and we were never able to build any real momentum. The team was burning out, and our clients were getting frustrated. We were delivering "okay" results, but we weren '''t delivering the kind of game-changing results that we had promised. The breaking point came during a pitch to a major logistics company. We had spent weeks preparing a custom demo, a beautiful, complex model that could supposedly optimize their entire supply chain. It was technically brilliant. The problem? It didn’t solve their actual problem. Their biggest issue wasn’t a lack of optimization; it was a lack of visibility. They couldn’t even track their shipments in real-time. Our complex AI model was like trying to teach a toddler calculus. It was the wrong solution for the wrong problem. The CTO, a brutally honest guy who had seen it all, cut us off halfway through the presentation. "You guys are smart," he said, "but you're not solving my problem. You're just showing off." He was right. We were so in love with the technology that we had forgotten about the customer. That was the moment I knew something had to change. I went back to the office that day and made the toughest decision of my life. I let the entire team go. It was brutal. These were people I had hired, people I had mentored, people I considered friends. But I knew that if we continued on the same path, we would all be out of a job in six months anyway. I had to save the company, and that meant starting over. ## The Painful Rebuild The first few weeks were a blur. I was working 18-hour days, fueled by caffeine and a healthy dose of fear. I had to handle everything myself, from answering the phone to writing code. I was the CEO, the CTO, the janitor, and the therapist. I spent hours on the phone with our existing clients, explaining the situation and reassuring them that we would not abandon them. Some of them left. I can’t blame them. But the ones who stayed were the ones who believed in me, and in the new vision for the company. I also started talking to potential customers. But this time, I didn’t talk about AI. I didn’t talk about machine learning. I just listened. I asked them about their problems, their challenges, their goals. I spent hundreds of hours on the phone, just listening. And a pattern started to emerge. ## Finding Our Niche The pattern was this: almost every company I talked to was drowning in data. They had data coming from everywhere—from their CRM, from their ERP, from their marketing automation platform, from their social media channels. They had all this data, but they didn’t know what to do with it. They were flying blind. They needed a way to turn all that data into actionable insights. They needed a way to see what was happening in their business, in real-time. They needed a business intelligence solution. But not just any business intelligence solution. They needed a solution that was specifically designed for their industry, for their business model, for their unique challenges. They needed a solution that was more than just a collection of dashboards and reports. They needed a solution that could actually help them make better decisions. And that’s when I had my "aha!" moment. We weren’t an "AI for everyone" company. We were a "business intelligence for enterprise" company. We would focus on one thing, and one thing only: helping enterprise companies make better decisions with their data. ## The New Playbook: From "AI for Everyone" to "AI for Enterprise" Once I had this clarity, everything changed. I started to build a new team, but this time, I wasn’t looking for AI generalists. I was looking for specialists. I was looking for people who had deep domain expertise in the industries we were targeting. I was looking for people who understood the business problems of our customers, not just the technology. We also changed our sales process. We stopped doing generic demos. Instead, we started doing "problem-solving workshops." We would go into a company, spend a day with their team, and at the end of the day, we would have a clear understanding of their problem and a plan for how to solve it. We also changed our pricing model. We stopped charging by the hour. Instead, we started charging for value. We would agree on a set of business outcomes with our clients, and we would get paid when we delivered those outcomes. ## The Results The results were staggering. Within six months, we were profitable. Within a year, we had a waiting list of clients. We were no longer a solution in search of a problem. We were a company with a clear mission, a clear focus, and a clear value proposition. We had found our niche. And in the process, we had built a multi-million dollar business. So, what’s the lesson here? The lesson is this: if you’re an AI consultant, or an AI entrepreneur, stop trying to be everything to everyone. It’s a recipe for disaster. Find your niche. Own it. And you’ll be unstoppable. The world doesn’t need another generic AI company. It needs specialists. It needs experts. It needs people who can solve real-world business problems. If you can be that person, you’ll never have to worry about finding clients again. They’ll be lining up to work with you. And that’s a much better place to be than a room full of smart people with a solution that nobody wants. '''
From Generic Models to a Specific, Scalable Product
Our first big win with the new focus was a mid-sized e-commerce company. They were growing fast, but their customer acquisition cost was spiraling out of control. They had tons of data from Google Analytics, their ad platforms, and their own sales database, but it was all in different silos. Their marketing team was spending hours every week just trying to stitch together basic reports in spreadsheets. It was a mess.
Instead of pitching them a 'black box' AI solution, we ran a two-day workshop. The first day was just listening. We talked to the Head of Marketing, the data analysts, even the customer service reps. We mapped out their entire customer journey and identified the key decision points where they were flying blind. The second day, we built a live, working prototype. Not a fancy AI model, but a simple, clean dashboard that pulled in data from their key sources and presented it in a way that made sense for their business.
For the first time, they could see their entire marketing funnel in one place. They could see which campaigns were driving the most profitable customers, not just the most clicks. They could see where customers were dropping off in the buying process. It wasn't magic, it was just clarity. The CTO was so impressed that he signed a six-figure deal with us on the spot. That project became the foundation of our new product. We productized the solution, building a scalable, multi-tenant platform that we could deploy to new customers in a matter of days, not months.
We learned that for most businesses, the immediate need isn't a hyper-complex predictive model. It's getting a solid grip on the data they already have. It's about building a strong foundation of business intelligence. Once you have that, then you can start layering in the more advanced AI and machine learning capabilities. We went from a team of consultants building bespoke solutions to a product company with a recurring revenue model. It was a much more scalable, and ultimately more valuable, business.
The Three Rules for Finding Your Enterprise AI Niche
Looking back, I’ve distilled our painful journey into three core rules for anyone trying to build a business in the enterprise AI space.
Rule #1: Sell Shovels in a Gold Rush. Everyone wants to find the gold—the revolutionary AI that will change the world. But the real money, especially at first, is in selling the shovels. The infrastructure. The data plumbing. The tools that help companies get their house in order. Don't underestimate the power of being the company that does the 'boring' work exceptionally well. It's the foundation everything else is built on.
Rule #2: Your First Team Should Be Experts in the Problem, Not the Solution. I made the mistake of hiring a team of brilliant AI researchers. My second time around, I hired a former logistics manager, a retail marketing analyst, and a financial controller. They understood the business problems of our target customers inside and out. They could speak their language. They could earn their trust. You can always hire the AI talent. You can't fake domain expertise.
Rule #3: The 'Minimum Viable Product' is a Solved Problem, Not a Piece of Software. Stop thinking about your MVP in terms of features. Think about it in terms of a solved problem. For our first e-commerce client, our MVP wasn't the dashboard itself. It was the solved problem of 'I don't know which of my marketing campaigns are profitable.' We solved that problem, first with a workshop and a prototype, and then with a product. Focus on the outcome, and the product will follow.
Firing my first team was the lowest point of my entrepreneurial journey. But it was also the turning point. It forced me to stop chasing the hype and start focusing on what really matters: solving real-world problems for real-world businesses. It was a painful lesson, but it’s one that has been worth millions. Don't be afraid to start over. Sometimes, it's the only way to move forward.
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.