Hello there!
I want to clear up a small misunderstanding from my last post.
One of the easiest ways to misunderstand AI is to only see the most visible version of it first.
That is what happened for a lot of small businesses.
For a lot of people, AI became a writing tool before it became anything else. It helped with emails, brainstorming, summaries, research, and all the communication work that tends to pile up during a normal week.
That is useful. I use it that way too.
But it also shaped how a lot of people think about AI in the first place.
So when someone says they are “using AI in the business,” they are often talking about one narrow slice of it.
That is part of what I was getting at in my last newsletter, Most People Are Using AI at the Wrong Level.
This one is a little different.
I want to make the picture more practical by walking through a few of the main categories of small business AI use the way I actually see them show up, because I think a lot of the confusion starts when very different kinds of use get lumped together.
If you want a good companion resource as you read, Matt Lietz from BotBuilders put together a free AI Starter Kit that is worth reading. It is one of the better “here’s what’s possible right now” resources I’ve seen if you are still trying to separate task level A.I. vs. business changing A.I.
It is one of the better “here’s what’s possible right now” resources I’ve seen if you are still trying to separate task level A.I. vs. business changing A.I.
Because the part of AI most small businesses are still missing is not more awareness.
It is a clearer view of where it actually fits.
Where AI starts to get more interesting is when it moves closer to the parts of the business where momentum is won or lost.
Not the parts where work gets a little faster.
The parts where delays compound.
A lot of small businesses do not actually have a content problem.
They have a lead handling problem.
A follow-up problem.
A support problem.
A handoff problem.
A consistency problem.
That is where the conversation shifts.
Because once AI moves into those parts of the business, you are no longer just talking about speed for the sake of speed. You are talking about response time, conversion support, customer experience, and operational drag.
That is a very different category of value.
I have seen businesses get excited about using AI to draft content while still taking too long to respond to leads.
I have seen teams use AI to summarize meetings while their follow-up process is still inconsistent.
I have seen people spend hours trying new prompt tricks while basic internal workflow is still being held together by memory, Slack messages, and whoever happens to be paying attention that day.
That is part of why AI can feel so uneven in practice.
The value is not distributed evenly across the business.
Some use cases save a little time.Some improve consistency.
And some sit much closer to the places where revenue, trust, and operational momentum are actually won or lost.
That is why it helps to separate the categories.
Not because every business needs all of them.
But because once you do, it gets easier to ask a much better question:
Where would AI actually remove friction in this business right now?
This is one of the first places where AI starts to matter in a more practical way.
I work with a lot of service businesses and consulting firms where leads are coming in just fine. The issue is what happens next.
A form gets filled out. Somebody reaches out through the website. A decent prospect replies to an email or asks a question. From there, too much depends on who saw it first, how busy the day is, and whether the person responding has enough context to say something useful.
That is where good opportunities start getting handled like generic ones.
The reply is slow. Or it is vague. Or it gets kicked around internally because nobody is fully sure who should own it. Sometimes the business has enough demand, but not enough structure around how inbound gets qualified, routed, and moved forward.
That is where AI can actually help.
Not by replacing sales. Not by pretending every lead should go through some robotic process. Just by helping the business respond faster, sort inquiries better, answer simple early questions, and give the right person a cleaner starting point.
That kind of use is a lot more meaningful than most of the public AI conversation.
Because in a lot of small businesses, the problem is not that interest is not there.
It is that the business is still handling too much of that early momentum manually, and not always especially well.
This is one of the more practical uses for AI, mostly because the problem it solves is so normal.
A friend of mine runs a large agency that manages paid advertising for clients, and this is a situation that shows up all the time.
The lead comes in. They have a good initial conversation. Maybe they send over a proposal or answer a few follow-up questions. Nothing is broken exactly, but the process is not especially tight either.
Some leads get a thoughtful follow-up right away because the conversation is still fresh. Others get a reply later when the day is already packed and the message is shorter, flatter, and missing some of the context that would have made it stronger.
Sometimes a proposal goes out and just sits there longer than it should because everybody is moving fast and nobody has a great system for what happens next.
That kind of slippage is pretty normal. Frustrating, but common.
It is also one of the reasons a business can look at the top of the funnel and think, we need more leads, when the real issue is that too much opportunity is getting lost in the middle.
This is where AI can help in a way that is actually useful.
Turning call notes into a sharper follow-up while the details are still fresh.
Helping draft stronger responses to common pre-sale questions.
Giving the team a cleaner starting point so every reply does not depend on somebody having a perfectly clear head at exactly the right moment.
That is the kind of support that actually matters.
Because a lot of deals do not die from one big mistake. They cool off because the middle of the process is uneven, and nobody notices how much that is costing them.
I spent years running the marketing department for a SaaS company in the prospecting and outreach space, so this is one I have seen from the inside.
When you are in that kind of business, customer questions pile up fast. Not because people are frustrated necessarily. Just because they are using the product, trying to get results, and they need help with normal things along the way.
How do I set this up?
Where do I find this?
Why is this not working the way I expected?
What is the right next step here?
None of those questions are unusual. But when enough of them stack up, they start putting real pressure on the team.
(it’s honestly maddening how often I’d get these questions)
And that is where customer experience starts to get shaped. Not by the big moments, but by how easy it is to get a clear answer, how consistent those answers are, and how much friction people feel when they need help.
I have seen this in my own experience, and I have seen it with clients too.
A lot of businesses do not have a support problem in the dramatic sense. They have a communication problem. Too many simple things still require too much manual effort. Too many repeat questions still get answered from scratch. Too much of the experience depends on who happens to reply.
That is where AI can help in a very practical way.
Not by removing the human side of support, but by making it easier to handle the repeatable parts well. Giving people faster answers to common questions. Pointing them to the right next step. Helping a team stay more consistent without having to recreate the same response over and over again.
Used well, that does not make a business feel more automated.
It makes it feel easier to work with.
This is probably the least flashy category, but in a lot of businesses it is where some of the most annoying breakdowns happen.
Here’s an example I recently ran into working with two different SaaS brands.
A testimonial comes in through customer service. A salesperson gets a great customer quote on a call. Somebody on the marketing team gets a strong piece of UGC. A long-form video gets recorded. A written asset gets created.
None of that is the problem.
The problem is that in a lot of businesses, those assets do not move cleanly from one part of the company to another.
They sit in inboxes. They stay in Slack threads. They live in one person’s folder. They get noticed by the team that captured them, but not by the people who could actually repurpose them, publish them, turn them into ad creative, plug them into sales collateral, or build them into follow-up.
That is an internal workflow issue.
And this is where AI can help in a very practical way.
Not by creating more stuff, but by helping the business move existing information better.
One piece of content gets broken into smaller usable assets.
A testimonial gets summarized, tagged, and routed to the right team.
A video gets turned into clips, snippets, and written copy.
The right people get notified that there is now a usable customer quote for a landing page.
A task gets created in the project management system.
The right people know it exists, know where it lives, and know what is supposed to happen next.
That is the kind of thing a lot of businesses are missing.
They already have useful material and valuable information. What they do not have is a clean enough system for making sure it keeps moving.
And when that gets fixed, the business starts feeling a lot less dependent on people remembering everything manually.
Each of these use cases can be valuable on its own.
But the bigger opportunity is when they stop being random isolated uses and start working together inside the business.
That is usually where the real friction is anyway. Not in one giant obvious problem, but in the gaps between steps. A lead comes in and context gets lost. A conversation happens and the next person does not have the full picture. A customer asks something simple and it still takes too much back-and-forth. People spend too much of the day tracking things down, repeating themselves, or holding loose processes together with memory.
That is the part AI can help clean up.
And when it does, the benefit is not just speed. It is that people get to spend less energy managing avoidable mess and more energy doing work that actually needs judgment.
If you want a good way to get caught up on what this actually looks like in practice, one thing I’d suggest is signing up for the webinar from Matt Lietz and BotBuilders.
It is a strong overview of what is possible with AI in a business, it is explained in a very easy-to-understand way, and it is probably one of the better entry points I’ve seen for people who want to level up their thinking here.
My work sits pretty close to this.
I am a marketing and growth systems consultant. A lot of what I do is help businesses think more clearly about how growth actually happens inside their company, then design better systems around it.
That is a big part of why AI stands out to me.
It is not because I think it replaces smart people.
It is because it helps a business rely a little less on every single person needing to remember everything, catch everything, and manually hold every part of the process together all the time.
That makes a real difference.
Because a lot of businesses are not struggling because their team does not care or is not capable. They are struggling because too much of the work still depends on people doing all the small tedious things perfectly, every time, while juggling everything else.
AI can help take some of that pressure off.
Used well, it gives people more room to focus on judgment, relationships, problem-solving, and the kind of work that actually moves things forward.
I’ve got a bunch more newsletters coming over the next week or two as I ramp things up here, and I’m excited to share more with you soon.
Onward,
Ryan
Owner | Poppin Consulting
Marketing Systems Consultant
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