Most business owners are implementing AI completely backwards. And I say that as somebody who made the exact same mistake. I spent months trying to figure out why AI wasn't doing what I needed it to do inside my companies - and the problem wasn't the AI. The problem was that I was using it the way an employee uses it, and the way a vendor sells it, instead of the way an entrepreneur actually runs it. Those three things are completely different.
Why Most AI Implementation Fails Before It Starts
An employee uses AI to get their individual tasks done faster. A vendor uses AI to sell you something. An entrepreneur uses AI to build leverage across an entire company - to compress timeframes, eliminate the drag that slows growth, and free up the mental bandwidth of the team so they can focus on the work that actually moves the needle. That's a fundamentally different use case, right? And most of what gets written about AI implementation is written from the employee or vendor perspective, not from the perspective of somebody building and investing in real companies.
I've got a portfolio of twelve companies across multiple industries. When I started bringing AI into those businesses, I made mistakes. I automated things that weren't working. I bought tools my vendors told me I needed. I let my team direct how we were using it. What I ended up with was more complexity, more cost, and the same results. What I needed to do - and what I eventually figured out - was ask the same question I ask before I make any major decision inside any of my companies: does this solve a real problem, and is the process I'm adding it to actually working first?
Fix the Process Before You Automate It
This is the one thing I wish I'd understood at the start. If a process in your company is broken, and you layer AI on top of it, you don't fix it. You make it break faster and cost more money to operate. I've watched this happen inside my own companies and I've watched it happen with the founders and CEOs I work with directly inside Club 28.
The businesses that are getting real results from AI right now all did the same thing. They identified a process that already worked when a human ran it. They confirmed it happened often enough to be worth automating. And then they brought AI in to handle the repetitive parts so their team could focus on the work that requires judgment, relationships, and real operator thinking. That's the whole framework, right?
Now, that sounds simple. And it is. But simple doesn't mean easy. Most entrepreneurs want to implement everything at once because the opportunities are genuinely enormous and it feels like you're falling behind. I get that. But trying to implement AI across your entire operation at once is the fastest way to end up with nothing that actually works.
The Three Questions I Ask Before Adding AI to Anything
Before I add AI to any process in any of my companies, I run it through three questions.
First: does this process already work when a human does it? If the answer is no, I fix the process first. AI is an accelerant. It makes what's already working scale faster. It doesn't make broken things work.
Second: does this happen often enough that automating it actually matters? A process that runs twice a month - the time you spend building and maintaining an AI workflow around it will almost always cost more than just doing it manually. The sweet spot is anything happening daily or weekly at real volume.
Third: what does my team do with the time this frees up? This is the question most entrepreneurs skip. AI that saves ten hours a week only helps your business if those ten hours get redirected toward work that actually grows the company. If your team fills that space with lower-priority tasks, the efficiency gain disappears completely.
Related Insights How to Stay Focused on What Really Drives Growth →Where AI Creates the Most Leverage in a Growing Company
I look at all of my companies through four pillars: Revenue, Reach, Relationships, and Organization. And when I think about where AI creates the most leverage the fastest, it's almost always Organization first - the systems, processes, and internal operations that run the business day to day.
This is where most entrepreneurs have the most room to grow, and it's where AI is genuinely excellent. Research, drafting, summarizing, analyzing patterns in data, building first versions of documents that your team then refines - these are organization-layer tasks, and they're the fastest path to a real return. Some of the functions I was paying serious money for on a monthly basis, I can now handle internally through AI workflows. That's not theoretical. That's running live across my companies right now.
Reach is the second area. If you're creating content - articles, emails, scripts, social media - AI can compress your production timeline significantly. But your voice, your stories, and your credibility cannot come from AI. Those have to come from you. Your core competency as an entrepreneur is what you've built, what you've lived through, what you've figured out in the trenches. AI handles the structure, the research, the first draft, the editing pass. The substance that makes your content worth reading is still yours.
Revenue is where I see the most hype and the fewest real results. People think AI is going to close their sales or run their funnels. The parts of your sales process that involve genuine human judgment and relationship-building are not good candidates for replacement. The parts that involve research, follow-up sequencing, and content creation around the sales process - those are excellent candidates.
Relationships - the most important pillar - is where AI has the least to offer. Your clients and team members trust you because of the human interaction you've built with them. AI can help you prepare, follow up faster, and show up more consistently. But the relationship itself stays human. And if you are an entrepreneur who builds companies, your relationships are your single most valuable asset. That doesn't get outsourced to a tool.
Related Insights How to Use AI to Grow Your Business →The Difference Between the 1% and Everyone Else
I have a friend I've known for over twenty-five years. He's watched me build multiple companies across multiple industries. A while back he reached out because he'd heard what was happening inside my companies and couldn't understand how we were getting so much more accomplished than before. He called me the next day and asked if I could help him set up his laptop so he could start using AI the way I was using it.
So I invited him to my house. We spent the day going through everything - setting up the workflows, configuring everything to his business and his industry. By the time he left, his laptop was completely built for his company, and he understood how to use AI as an entrepreneur, not the way his employees were using it and not the way his vendors had been telling him to use it. Since then, he's texted me almost every day. He just finished his third record week in a row.
The difference wasn't the tools. He had access to the same tools before. The difference was understanding how to use them from the owner's seat. And that's what separates the entrepreneurs who are getting real results from AI from the ones who are still describing it as promising. The 1% who are winning with AI right now are winning with fewer people, fewer expenses, and more margin than they've had in years - because they're using it like entrepreneurs, not like employees or vendors.
The most dangerous person in business right now is not the one who doesn't know anything about AI. It's the one who thinks they know a lot about it but is still using it the way their employees use it - and wondering why it isn't working.
How to Start Without Getting It Wrong
Pick one process. Not your most important one. Not the most painful one. Pick something that happens regularly, that you understand well, that has a clear output, and that is not mission-critical if something goes sideways.
Run it manually first. Document exactly what a good result looks like. Experiment with using AI to handle pieces of it. Measure the output against your standard. Iterate. Once it works consistently, lock it in. Then pick the next one.
I've watched companies try to implement AI across their entire operation at once and end up with nothing that works. And I've watched companies pick one process, get it running right, and then build from that foundation one piece at a time until AI is genuinely handling a meaningful portion of their operations. The second group almost always gets further faster, because momentum builds when you have a win to point to and a working process to replicate.
This is not different from how I've thought about scaling for thirty years. You build what gets you to the next level. You run it until it needs an upgrade. Then you upgrade. Same principle applies here.