Do I need to hire an AI expert, or can my team learn this?

You almost certainly don’t need to hire an AI expert. A full-time AI hire starts around $134,000, and with AI skills now the hardest to hire for globally, small businesses lose that bidding war anyway. The question also hides a false choice: the real decision has three paths — your team learns it themselves, someone sets it up and teaches your team to run it, or someone runs it for you. What separates them isn’t technical skill, because modern AI takes plain English. It’s founder time and who maintains the system afterward. List the tasks you want off your team’s plate; the right path falls out of the list.

Why does AI feel like it needs an expert?

I’m going to go back to something that I’ve mentioned before in some of my other posts and newsletters, and it’s that a lot of people hear the word Claude Code or Codex and get intimidated by the tools. Everyone’s happy to use ChatGPT in regular Claude because there’s nothing intimidating about it. It’s simply just a chat tool that you can use AI in, but once you start looking at the “Code” terms it’s immediately scary. And yes, previously creating code meant understanding software and being a real engineer. You really did need that technical knowledge of how to code, how to make integrations, and you had to speak the language essentially.

Nowadays, the industry is also keeping the intimidation alive. We’ve got a whole bunch of AI jargon that doesn’t really make any sense unless you look into it. Things like RAG, AI agent, orchestration, and a bunch of other terms that essentially fall into the “code” terminology.

And the evidence is there to support this. There’s around 76% of small businesses that now use AI. However, half of the firms using it have invested nothing in training or integration. Which essentially means they’re using only basic ChatGPT and calling it AI usage. Everyone’s happy to get in the shallow end of the pool, but to really benefit from AI, you need to learn how to swim to the deep end. That deep end is the intimidating part and it feels like the people who are expert swimmers are the only ones allowed to be there.

So this brings up the question of to truly benefit from the deep end, do I need to find somebody who is already an expert? Which I’m going to answer with no because you don’t. Everybody can learn how to swim, so why aren’t you learning it yourself? When you start learning AI, you’ll know exactly how much of it you can handle yourself, you get to decide how far you take it before you hand it off to somebody else, or even if you just want to do everything yourself.

Sources: Capsule — Small business AI adoption statistics 2026

What does hiring AI expertise actually cost?

You’re probably not going to hire somebody who is a full-time AI and machine learning engineer that costs $130k+/year. That is, unless your company is an AI lab. But if you’re here, you’re most likely a small business owner and you wouldn’t be hiring those anyway.

But for an AI consultant and builder, the prices do vary.

Specifically, an AI consultant will come in and tell you which AI solutions you should be building for your business. They’ll then hopefully give you a roadmap on which to build first and the order in which you’re building. And these consultants can cost anywhere from $150 to $350 per hour or anywhere from $1k-$20k+ per project.

For an AI builder, their job will be to come in and actually build and set up AI solutions for you. Very often, an AI consultant will have recommendations or also acts as an AI builder. They will take a consultant’s roadmap or whatever solution you’re coming to them with and do one-time fees as well as retainers for their projects. Again, this would cost in a similar range. $1k-$10k+ per project build and $500-$10k+/month retainers. The retainers, however, are services for them to continue to run it for you so you don’t have to.

So you can see that, yes, some of them are cheap, but it’s probably for more simple stuff, and they do range to pretty expensive on both ends. It’s expensive because it’s priced on the assumption that you or your team either can’t do any of this or not willing to do any of it. So they are pricing it at the value of the actual service and knowledge rather than what the actual cost of the AI takes. They are specifically using the fact that they are invested in AI and know a lot about it to create their prices, which is just a good general business practice on their end.

Sources: Qubit Labs — AI Engineer Salaries in 2026, Second Talent — Global AI Talent Shortage Statistics 2026, AI Essentials — AI Consultant Cost 2026

How much can a non-technical team actually learn?

The expert premium that I talked about in the last section basically makes you assume that the AI technology is the hard part. But it’s quite the contrary. I think anybody can learn how to leverage the more powerful AI solutions and build systems that run their business for them all by themselves. All of these modern AI tools take instructions in plain English. So the question isn’t, “can you code?”, It’s, “can you describe how your business works and how you want the automation to operate?”.

So a lot of the consultants are coming in and their main skill isn’t actually knowing AI, it’s more talking to the people doing the work to find the AI solutions. Which essentially means the person who can explain their work to AI is able to build a solution for themselves. If an employee of yours is able to fully explain in depth how they’re doing work that doesn’t require them, then you can essentially have them record themselves doing it and give that video to AI to turn it into an automation. Simple as that.

I was able to create a social media researcher by telling AI A version of the following words: “Every Monday morning, I go to YouTube, TikTok, Instagram, LinkedIn, and X to do research on viral posts. I generally try to find 10 to 20 posts that have an engagement count that is five times the follower count. I then take all of my posts across all of those platforms, put them into a single document, and then open a new document to write down all of the ideas that I have synthesized from all of the posts. I generally end up with 20 ideas for content that I adapt to each platform. So one idea can translate into a LinkedIn post, a TikTok post, and a Twitter post. I want you(claude) to research how to turn this into an automation for me and create a plan to build it.”

Everyone can simply do something with AI to build things like these for their workflows. And if you’re only ever working within Claude Code or Codex on your computer, most of these will work out fine. The issues arise when you start designing more complex systems that need to work 24-7.

My job as an AI builder, I need to know what connects to what, what runs when, what happens when it breaks, and all of these different items so that any of my clients who I run these for don’t have to worry about it breaking. This is exactly where self-taught teams stall because it’s the not invested in in-depth training or the not having a dedicated AI person on this. So the next question naturally becomes when to buy, build, or learn it yourself.

Sources: Careertrainer.ai — AI Corporate Training Statistics 2026

What actually decides it: build, buy, or learn?

There are three different paths for implementing AI into your business. Firstly, learning it yourself. This means you or your team decides somebody will be the person to learn everything about it, however, nobody is going to pay for anything. Everything will be self-taught and the person in charge of AI implementation needs to essentially make that their role.

Next up is learning it with a guide. So this is someone setting up a system for you and then teaching you how to run it day to day. But then it becomes your responsibility to maintain and upgrade it.

Finally is the most expensive option in somebody building it and running it for you. You’re hiring a builder to build the initial system and they set you up on a retainer to come back, maintain it, upgrade it, and keep it optimized.

Each of these has different expenses: The first variable for expenses is time. The first option is the cheapest in cash but the most expensive in hours and it flips when you go to the third option. If you go with option A, you need to research, build it yourself, debug it yourself, and these hours come directly from either the founder or the person the founder decides is in charge of AI.

The second variable is maintenance. Specific AI agents and automations are alive. This means tools changing, edge cases appearing, and business changes. So whoever owns maintenance owns the system. So the first and second options maintenance is up to you. The second option should train you on how to maintain the system, but it’s still up to you. Where Path C, it’s on the person you hire and you’ll never have to maintain it yourself because it’s handled for you.

What isn’t a variable is the intelligence or technical talent. There is no “we’re not technical enough.” because everything is available to build in plain words.

Here’s a mapping I think you can follow to really pick what you should be choosing for your business:

If you’re a drowning founder with no slack, always working, no leeway, you should probably go with hiring somebody.

If you do have some leeway and a real appetite to own the system and somebody on the team has weekly hours to invest, I would go with one of the first two options. Option B will get you further faster, simply because it’s an expert comes to give you that head start over self-taught teams.

And none of these stages are forever. You could start with a second or third option and move to A. You could start as a founder learning it themselves and move to hiring someone because you dont want to handle it anymore. All of the options make sense in their own way.

What does each path look like in practice?

Here’s how all of those paths look like in action.

The first path, learning it yourself. I’ve lived it personally. I’ve built my own AI operating system and I build every system that runs my business. I do have a computer science degree and a software engineering background. However, I haven’t touched a line of code in the entire time I’ve been building with AI. I learned a lot from YouTube videos where people teach it for free, and you can too. The only real caveat here is I have to build everything myself and sometimes when I’m building a more complicated system, it very often breaks because I’m not as good at the strategy around the business that the AI is taking over.

The second path is something that I teach and I’ve taught multiple people to do it and they have all seemed very happy with the results and have created very awesome solutions. I’ve streamlined how I’ve set it up for people, and now the non-technical clients that I do have have came in, we’ve got it set up for them, and in a matter of a couple days, they have taken the system and ran with it. I have been able to teach them that all they really need to do is explain what they want, and if anything comes back confusing, to simply ask AI what it means in simple terms. The questions my clients tend to have aren’t, how do I build this solution, they’re more, can AI do this solution? They have a slight hesitancy to think AI can take stuff over, and my answer every time is: “most likely yes it can. Go ahead and ask AI if it can”. Which results in them asking AI and them coming back to me saying, yep, I’m working on it now. Thank you.

Finally, having it ran for you. When you’re doing it yourself, there is a ceiling that you tend to hit in terms of managing the AI. There are some solutions that need to be on 24-7 to get their tasks done because they rely on triggers that could happen at any time. And these require ongoing maintenance, which is usually something that’s a little more technical. And yes, you can learn how to manage these yourself, but they are a lot more time intensive than anything you would build in the first or second path. This is exactly where hiring somebody to manage it for you really pays off. If managing your AI is an entire subset of the business and it takes way too much time, then hiring an expert to take care of it for you while you reap the benefit of AI working is definitely valuable.

You shouldn’t pick the first option if there is nobody on your team with three to four real hours per week to invest in learning.

You shouldn’t pick the second option if you really want zero involvement with AI because done with you means showing up to learn.

And you shouldn’t pick the done-for-you version if your processes are still undocumented chaos because nobody can run a system for a business that can’t describe it.

Where do you start?

No matter if you choose to learn it yourself or hire somebody to do it, your first place should be to start with the actual tasks that you want AI to handle. You can essentially skip the AI Consultant if you do this yourself.

There’s a free first step that you can follow:

Firstly, you need a task map. List out the recurring tasks, eating your team’s week. Make sure to write down the hours on each of them. Then, put a flag on them, asking, does this task require a human to do it?

Filter out all the tasks that REQUIRE a human to do them and look at the what remains. Then order it by pain point. The biggest pain points at the top and the things that are the least painful at the bottom. This will be your list for the automations that you should be building for your business.

The next question to ask yourself is, is there somebody on your team who has either the time to learn how to automate them with AI or if taught by somebody, the time to automate them and maintain them? If nobody has time to automate or maintain, then you probably have to hire somebody to do it for you.

And if you need help figuring out what these tasks actually are, I offer a free task audit quiz on my website where you can fill in answers for every section of your business and it will give you a list of possible AI automations to implement to your business for you to act on. Get your free Task Audit HERE

Frequently asked questions

Why does AI feel like it needs an expert? The intimidation kicks in at the “Code” tools. Everyone’s comfortable in ChatGPT and regular Claude, but Claude Code and Codex sound like engineer territory, and the industry’s jargon (RAG, agents, orchestration) keeps that feeling alive. Around 76% of small businesses now use AI, yet half have invested nothing in training or integration, so most stay in the shallow end when anyone can learn to swim to the deep end.

What does hiring AI expertise actually cost? A full-time AI and machine learning engineer runs $130k+ a year, which small businesses aren’t hiring anyway. An AI consultant, who tells you what to build and in what order, charges $150 to $350 per hour or $1k to $20k+ per project. An AI builder charges $1k to $10k+ per project build, with retainers from $500 to $10k+ a month to run it for you. The prices assume your team can’t or won’t do any of it.

How much can a non-technical team actually learn? Modern AI tools take instructions in plain English, so the question isn’t “can you code?” but “can you describe how your business works?”. An employee who can fully explain a task can record themselves doing it, hand that to AI, and turn it into an automation. Working inside Claude Code or Codex on your computer, most of these come out fine; the hard part starts with complex systems that need to run 24-7.

What actually decides it: build, buy, or learn? Two variables. Time: learning it yourself is the cheapest in cash and the most expensive in hours, and that flips when someone builds and runs it for you. Maintenance: automations are alive (tools change, edge cases appear), and whoever owns maintenance owns the system. Technical talent isn’t a variable, because everything can be built in plain words.

What does each path look like in practice? Learning it yourself works (Jonathan built his own AI operating system without touching a line of code), but you build and fix everything. Learning with a guide gets non-technical clients running their own system within a couple of days. Having it run for you pays off when solutions need to be on 24-7 and maintenance becomes its own job. Skip self-taught if nobody has 3 to 4 real hours a week, skip guided if you want zero involvement, and skip done-for-you if your processes are still undocumented.

Where do you start? Build a task map. List the recurring tasks eating your team’s week with the hours on each, filter out the ones that require a human, and order what remains by pain. Then ask whether anybody on the team has the time to automate and maintain them; if not, hire someone to do it for you. A free task audit quiz can build the list for you.

Featured image: Photo by Resume Genius on Unsplash