10/20/2026
Why Most Businesses Using AI Aren't Actually Growing Faster.
And Why The Difference Between Using AI To Produce More Work & Using AI To Build A Better Business Is Bigger Than Most People Realize.
There's a question I think more online business owners should be asking.
Not:
"How can I use AI to create more content?"
Or:
"What's the best AI tool for marketing?"
Or even:
"How much of my business can I automate?"
The better question is:
What actually prevents my business from growing, and which parts of that constraint can AI help remove?
Because those are two completely different conversations.
One is about finding things AI can do.
The other is about understanding how a business grows.
And that distinction matters a lot more than people think.
Let's get into it.
AI Doesn't Automatically Create Growth. It Creates Capacity.
This is probably the biggest misunderstanding I see around using AI in business.
People assume that if they can produce ten times the work, they should be able to produce ten times the results.
Ten times the content.
Ten times the emails.
Ten times the landing pages.
Ten times the outreach.
But production and growth aren't the same thing.
Imagine a business generating 100 leads per month.
Twenty of those leads become sales conversations.
Four become customers.
Now imagine using AI to generate twice as many leads.
200 leads.
Sounds great.
Except the business still takes too long to follow up, doesn't qualify prospects properly, and has no consistent process for converting conversations into customers.
More leads don't solve those problems.
They expose them.
AI can increase the volume passing through a business without improving the business's ability to convert that volume into revenue.
Which brings us to the first mental model.
Lens #1: Find The Constraint Before Adding Capacity.
Every business has something limiting its growth.
Sometimes it's traffic.
Sometimes it's conversions.
Sometimes it's sales capacity.
Sometimes it's fulfillment.
Sometimes it's simply the owner's ability to make decisions and execute.
The mistake is assuming AI should be applied wherever work is easiest to automate.
That's not necessarily where it creates the most value.
Think about three businesses.
Business A: The Traffic Problem.
They have a proven offer.
Their landing page converts.
Their fulfillment is solid.
Customers are happy.
But they aren't reaching enough qualified people.
For this business, AI-assisted audience research, creative testing, outreach personalization, and campaign development could create meaningful leverage.
Because they're expanding the input to a system that's already working.
Business B: The Conversion Problem.
They're getting thousands of visitors.
People are interested.
But few become customers.
Generating another thousand visitors might not be the right priority.
Instead, AI could help analyze objections, review customer conversations, identify messaging gaps, and develop new positioning hypotheses to test.
Same technology.
Completely different application.
Business C: The Delivery Problem.
They're closing customers consistently.
But onboarding takes forever.
Every deliverable is built manually.
And the founder is personally involved in every customer experience.
More leads could make this business worse.
AI-assisted onboarding, knowledge retrieval, standardized workflows, and agent-supported delivery might be far more valuable.
Here's the point.
The best AI implementation depends on what's preventing the business from making more money, not which AI tool has the most impressive demo.
Lens #2: Separate Activities From Outcomes.
This is where things get interesting.
Most people organize their AI usage around activities.
Write an email.
Create an image.
Summarize a meeting.
Generate a sales script.
Research competitors.
And there's nothing wrong with any of these.
But they're individual pieces of a larger process.
Let's use email marketing.
The activity is writing emails.
The outcome is generating profitable customer relationships.
Those aren't the same thing.
A business could use AI to generate 100 emails and still have a terrible email marketing operation.
Why?
Because writing is only one part of the process.
You still need to understand:
Who is receiving the message?
What do they already believe?
What problem are they trying to solve?
What stage of the buying process are they in?
What would make them take the next step?
What happened after previous emails were sent?
Now imagine building an AI-supported process around those questions.
The system organizes customer research.
Segments the audience.
Identifies relevant messages.
Drafts emails based on the customer's situation.
Tracks performance.
And helps determine what should be tested next.
You've gone from automating an activity to supporting an entire business outcome.
The distinction is simple:
Task automation reduces the work required to perform an activity.
Process automation reduces the work required to achieve a result.
They can both be useful.
But they're not equally valuable in every situation.
Lens #3: Context Determines The Quality Of AI Output.
Here's something worth understanding.
Two businesses can use the exact same AI model and get wildly different results.
Same model.
Same general task.
Different outcomes.
Why?
Because the model isn't the only variable.
The information available to it matters.
Imagine asking AI:
"Write a Facebook ad for my coaching business."
It has to make assumptions.
About the audience.
The problem.
The offer.
The positioning.
The objections.
The tone.
The desired outcome.
Now imagine giving it access to actual information from the business.
Customer interviews.
Winning advertisements.
Sales objections.
Product details.
Founder stories.
Previous campaign results.
Audience research.
Suddenly, it has something real to work from.
This leads to a principle I think will become increasingly important.
The value of AI in business isn't determined only by model intelligence. It's also determined by how much relevant business intelligence the model can use.
This is why context should be treated as infrastructure.
Not something you type from scratch every time you open a chatbot.
Lens #4: Understand The Difference Between Speed & Leverage.
People use these words interchangeably.
But they mean different things.
Speed means completing a task faster.
Leverage means getting more output from a given amount of resources.
Suppose writing a sales page normally takes eight hours.
AI helps reduce that to one hour.
You've gained speed.
Now imagine that the research, messaging, offer information, and customer insights developed during that process can also be reused for:
Advertisements.
Email sequences.
Webinars.
Organic content.
Sales scripts.
Retargeting.
Follow-up campaigns.
That's a different kind of advantage.
The value isn't just that the sales page was created faster.
It's that the work that went into understanding the customer no longer has to be repeated for every new asset.
You've created reusable knowledge.
And reusable knowledge can create compounding operational leverage.
The more processes that can use it, the more useful it becomes.
Lens #5: AI Makes Ex*****on Cheaper. It Doesn't Make Every Business Strategy Better.
This is where I think a lot of online entrepreneurs are going to get themselves into trouble.
AI is making it easier to create businesses.
Landing pages.
Courses.
Software.
Digital products.
Automations.
Sales funnels.
All the things that used to require more time, money, or specialized skills.
But here's the catch.
Making something easier to create doesn't automatically make it more valuable to buy.
A course nobody needs is still a course nobody needs.
A funnel built around a weak offer is still a weak funnel.
Software that doesn't solve an important problem is still software nobody wants.
The reduction in production cost changes the economics of experimentation.
You can test ideas faster.
Create prototypes at lower cost.
Explore positioning without rebuilding everything manually.
But the underlying requirements of business don't disappear.
People still need reasons to buy.
Offers still need to solve problems.
Customer acquisition still needs to make economic sense.
And fulfillment still needs to deliver what was promised.
AI reduces the cost of trying.
It doesn't remove the need to figure out what works.
Lens #6: Build A Business That Learns.
This is probably the most important concept in the entire discussion.
Think about how a typical online business operates.
Someone launches an ad.
It doesn't work.
They try another one.
Someone writes an email.
It performs poorly.
They write another one.
A salesperson hears an objection.
They answer it.
Another salesperson hears the same objection the next day.
Over time, the business accumulates experience.
But much of that experience remains scattered across people, files, platforms, and conversations.
Now imagine a business with processes designed to capture and reuse what it learns.
Sales objections become inputs for new marketing angles.
Customer feedback informs product improvements.
Winning messages become references for future campaigns.
Campaign performance influences the next creative test.
Delivery problems identify opportunities for better onboarding.
AI can help organize, retrieve, analyze, and apply those lessons.
But only if the business actually captures the information and connects it to decisions.
That's the difference between using AI to generate work and building an AI-supported learning system.
One produces outputs.
The other can improve the process used to produce future outputs.
So Where Should An Online Business Actually Start?
I wouldn't start by asking which AI platform to buy.
I'd start by mapping the business.
Five areas.
1. Acquisition.
How do people discover the business?
Where do qualified leads come from?
What limits the volume of opportunities?
2. Conversion.
What happens between initial interest and purchase?
Where do prospects drop off?
What objections consistently appear?
3. Delivery.
What must happen to fulfill the promise?
Which steps require human judgment?
Which repeatable steps could be assisted or automated?
4. Retention.
Why do customers stay, return, or buy again?
What causes them to leave?
What signals suggest they need additional help?
5. Learning.
Where does the business store what it discovers?
How does customer feedback influence future decisions?
How much knowledge gets lost between departments, tools, or team members?
Once you've mapped those five areas, look for three things.
High-frequency work: Tasks repeated constantly.
High-friction work: Processes that delay customers, revenue, or delivery.
High-value decisions: Choices where better information could materially improve business performance.
Those are useful starting points for AI.
Not because everything should be automated.
But because they help identify where greater speed, consistency, or decision quality might actually matter.
The Bigger Opportunity.
I think we're going to see a significant divide between businesses that adopt AI superficially and businesses that redesign how they operate around it.
Some will use AI to make more things.
More posts.
More ads.
More emails.
More products.
Others will use it to build better processes for understanding
customers, acquiring revenue, delivering results, and learning from experience.
Both groups are using AI.
But they're building very different businesses.
And I suspect the second group will have the more durable advantage.
Because tools change.
Models improve.
Features become commodities.
But a business that understands its customers, knows where growth is constrained, and continuously improves how it operates has something more valuable than access to the latest model.
It has a system for turning information into better decisions and better ex*****on.
That's where I think the real opportunity is.
Not replacing human judgment with AI.
Building a business where human judgment, business knowledge, and AI ex*****on reinforce each other.
TL;DR
AI creates capacity.
Capacity doesn't automatically create growth.
Find the business constraint before adding more automation.
Automating an activity isn't the same as improving a business outcome.
Context is a major part of what makes AI useful.
Speed saves time. Leverage lets knowledge and processes be reused.
Cheaper ex*****on doesn't fix bad offers or weak customer understanding.
The biggest opportunity is building businesses that capture what they learn and use that knowledge to operate better.
The goal isn't to do more work with AI. It's to build a business that gets better at producing results.
