The most effective way to use AI is to focus on three areas: replacing expensive vendors with AI workflows, compressing your team's execution time on high-value work, and building operational dashboards that were previously too expensive. Get those three right and the rest compounds. Founders who experiment broadly with AI without depth end up nowhere. Founders who go deep in the right areas change their cost structure permanently.
The most effective way to use AI to grow my business, and what I've found works across the twelve companies I run, is to focus on three areas first: replacing the vendors charging a premium for work AI now does better, compressing your team's execution time on high-value tasks, and building the operational dashboards you've never had because they were too expensive to build. Get those three right and the rest compounds naturally. Founders who spend a year experimenting broadly with AI end up in roughly the same place they started. Founders who go deep in the right areas early change their cost structure permanently.
What Does It Actually Mean to Use AI to Grow Your Business?
Growing with AI doesn't mean having the newest tools or running impressive demos. It means your cost per output drops, your team ships faster, and decisions are grounded in real data rather than gut instinct. Three things shift materially: cost of execution drops, speed of execution compresses significantly, and decisions get grounded in data that didn't exist before because the infrastructure to collect it was too expensive. Treat AI like a senior team member who never sleeps and has read everything in your industry.
Growing your business with AI doesn't mean having the newest tools or running the most impressive demos. It means your cost per output drops, your team ships faster, and the decisions you make are grounded in real data rather than gut instinct and reports that arrive two weeks after the fact.
The founders who get this right treat AI like a senior team member who never sleeps, never gets distracted, and has read everything published in your industry since the internet began. They give it clear assignments with clear outputs. They use it to replace the things that were slowing them down and eroding their margins, not to play around.
Three things shift materially when a business truly starts using AI to grow. The cost of execution drops. Things that required three people or a costly vendor now require none of those things. The speed of execution compresses significantly: research that took a week, communication that took a day, reporting that took a department. And decisions get grounded in data that didn't exist before, because the infrastructure to collect and surface it was previously too expensive to build.
Where Do Most Founders Go Wrong With AI?
The most common mistake is using AI for the wrong things first. Founders who fail with AI in year one almost always use it primarily for social media posts (lowest leverage, most visible, feels productive), explore without deploying (months in ChatGPT asking questions, never shipping), or delegate the decision to someone not running a P&L (your marketing coordinator decides AI strategy instead of revenue leaders). The people closest to your revenue and margin need to drive AI adoption.
The most common mistake is using AI for the wrong things first. Founders who fail with AI in year one almost always do one of three things.
They use it primarily to write social media posts. This is the lowest-leverage use of AI in any business. It's visible, it feels productive, and it generates almost zero actual business growth on its own.
They explore without deploying. They spend months in ChatGPT asking questions, watching demos, and building an elaborate mental model of what AI can do, without ever shipping anything that changes their cost structure or their output.
They delegate the decision to someone who isn't running a P&L. If your marketing coordinator is deciding where AI goes in your business, the results will reflect that. The people closest to your revenue and margin need to be driving AI adoption, not because the technical decisions require it, but because the prioritization decisions do.
Related Insights The Business Mistake That Stops Growth Cold →How Do You Actually Replace Vendors With AI?
Before any vendor renewal, ask whether that vendor does something involving research, writing, analysis, or synthesis. If yes, test an AI replacement before renewing. I've replaced multiple five-figure vendor contracts with AI tools - output quality went up, not down. The cost savings are real, but the bigger shift is operational: when a process no longer requires a vendor or headcount, it becomes something you can iterate on freely, and that speed of iteration is a compounding advantage.
I have replaced multiple five-figure annual vendor contracts in the last eighteen months using AI tools, specifically a code assistant running in a terminal, and the output quality went up, not down.
A competitive intelligence vendor charged $2,400 per month to monitor our category and deliver a weekly summary. It's been replaced by a script that pulls daily, summarizes with AI, and drops a report into our team's Slack before anyone arrives in the morning. A copywriter on retainer produced first drafts of email sequences. Those drafts now come from a detailed brief, in my voice, because I trained the workflow on two years of my own writing. A web development vendor charged per-page for custom work. I now work directly in code with AI: I describe what I want, it builds, I review and refine.
The principle is simple: before any vendor renewal, ask whether that vendor is doing something that involves research, writing, analysis, or synthesis. If yes, test an AI replacement before renewing. In most cases, you won't need to renew.
The cost savings are real. But the more important shift is operational: when a process no longer requires a vendor or a headcount, it becomes something you can iterate on freely, and that speed of iteration is its own compounding advantage.
How Do You Multiply Your Team With AI?
The highest-leverage thing is multiplying your team, not replacing them. When you give your team the right AI tools and train them for their specific roles, each person's effective output increases substantially. Every team member identifies their three most time-consuming recurring tasks. You build an AI workflow for each one - not generic ChatGPT instructions, but a specific workflow with prompt templates, output format, and clear standards for good. That specificity separates real AI adoption from the subscription-nobody-uses version most companies have.
The highest-leverage thing you can do for your team with AI isn't replacing them. It's multiplying them.
When you give your existing team the right AI tools and train them on how to use those tools for their specific roles, each person's effective output increases substantially. Your marketing person can produce more content, more thoroughly researched, faster than before. Your operations lead can analyze data and draft SOPs in a fraction of the previous time. Your sales team can research prospects, personalize outreach, and prepare for calls in a way that used to take an hour and now takes five minutes.
The structure I use: every team member identifies their three most time-consuming recurring tasks. We build an AI workflow for each one. Not a generic "use ChatGPT" instruction. A specific workflow with a specific prompt template, a specific output format, and a clear standard for what good looks like. That specificity is what separates real team AI adoption from the subscription-to-ChatGPT-nobody-uses version that most companies have.
This also changes who you hire over time. When your team operates at a higher output level with AI, you need fewer generalists handling repetitive work and more people who can direct AI, review its output critically, and make the judgment calls that AI can't make yet.
Related Insights How to Build a Leadership Team as a Founder → Related Insights How to Implement AI in Your Business Without Getting It Wrong → Related Insights The AI Tools I Actually Use in My Business →How Do You Build Data Visibility You Never Had Before?
Most businesses have never had a complete customer journey picture, not because data doesn't exist, but because assembling it was too expensive. You had different tools showing different funnel stages, and getting them to tell one story required analysts, integrators, and months of work. AI changes that equation entirely. I built a customer journey dashboard using a code assistant in the terminal. It pulls from four data sources, normalizes the data, and generates a weekly report showing where prospects enter, convert, drop, and which segments have highest value. The analysis that used to cost a consulting engagement now runs automatically every week.
Most businesses have never had a complete picture of their customer journey, not because the data didn't exist, but because assembling it and making sense of it was too expensive. You had a marketing tool showing top-of-funnel, a CRM showing mid-funnel, a payment processor showing conversions, and a support tool showing post-purchase behavior. Getting those four to tell one coherent story required an analyst, a systems integrator, and months of work.
AI changes that equation entirely. I built a customer journey dashboard for one of my companies using a code assistant in the terminal. It pulls from four different data sources, normalizes the data, and generates a weekly report showing exactly where prospects enter, where they convert, where they drop, and what the highest-value customer segments look like. The analysis that used to cost a consulting engagement to set up now runs automatically every week.
The mindset shift is this: data infrastructure used to be a capital expense for big companies. AI has turned it into a weekend project for any operator who can describe clearly what they want to see. If you're running a business and making decisions without this kind of visibility, you're flying with instruments you built in the dark.
Which AI Tools Should You Actually Use?
I won't rank tools because the landscape changes too fast. My team evaluates our AI stack every week. We swap tools when something better comes along. We have loyalty to results, not platforms. For strategy and reasoning: top-tier large language models. For building and automating: a code assistant running directly in a terminal - it's replaced meaningful portions of what I used to need technical contractors for. For day-to-day team workflows: role-configured AI tools, not generic subscriptions everyone uses differently, but tools set up for a specific role with prompt templates and output quality standards. Go deeper on what's working before chasing what's new.
I won't give you a definitive ranking, because the landscape changes fast enough that it would be outdated before you finish reading it. My team and I evaluate our AI stack every week. We swap tools when something better comes along. We have no loyalty to any platform. We have loyalty to results.
That said, here is what is producing results right now. For strategy, research, and complex reasoning tasks: the current top-tier large language models. The specific tool matters less than the quality of the assignment you give it. A sharp, well-structured brief to any top-tier model produces dramatically better output than a vague one to the "best" model on the market.
For building and automating: a code assistant running directly in a terminal. As of the writing of this article, Claude Code Terminal is what I use most for this category. It has replaced a meaningful portion of what I used to need technical contractors for. I describe what I want built, it builds, I review and refine. But I want to be clear about how I frame that: my team evaluates this category constantly. What we're using today is what's producing the best results today. Next quarter may look different. We track what works, not what has a strong brand name in the market.
For day-to-day team workflows: role-configured AI tools, not a generic subscription everyone uses differently, but tools set up for a specific role with a prompt template, a voice guide, and a clear standard for output quality. The difference in adoption and actual output between a generic deployment and a role-specific one is not small.
The principle that doesn't change: go deeper on what's working before chasing what's new. The marginal value of adding another AI tool to your stack is close to zero. The marginal value of using your current tools at eighty percent of their capability instead of ten percent is enormous.