How Much Does an AI Consultant Cost?

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The short answer

AI consulting in 2026 typically runs from about $150 to $500 an hour, or roughly $2,000 to $10,000 for a defined project, with larger transformations going well past that. Most independent consultants and boutique firms price one of three ways: by the hour, by the project, or by a monthly retainer. The honest answer is that the hourly number matters less than what you walk away with. A cheap engagement that leaves you with a tool nobody uses costs more than a focused one that changes how your team actually works.

Let me break down what you are really paying for, because the price tag without the context is useless.

The three ways AI consultants price

Hourly. Common for advisory work, audits, and short engagements. Independent consultants usually land between $150 and $350 an hour. Senior specialists and firms with a track record charge $350 to $500 and up. Hourly is honest for open-ended discovery, but it can punish you if the scope drifts, so I rarely recommend it for a full build.

Project-based. A fixed price for a defined outcome. A focused workflow audit and a starter plan might be $2,000 to $5,000. A built-and-trained AI workflow for one part of your business often sits in the $5,000 to $15,000 range. A full operating-system build across multiple functions goes higher. You are buying a result, not a clock, which is why I prefer it for most small businesses.

Retainer. A monthly fee for ongoing strategy, building, and support, often $2,000 to $8,000 a month. This fits businesses that want a partner in the chair every month, not a one-time project. It is the model that makes the most sense once AI becomes part of how you run, rather than a thing you tried once.

Why the range is so wide

Three things move the number more than anything else.

  1. Scope. A one-hour second opinion and a six-week build are not the same purchase. Get clear on whether you are buying advice, a plan, or a built thing before you compare quotes.
  2. Seniority and track record. Someone who has put AI to work inside real organizations and can show you the results will cost more than someone who learned it last quarter. That gap is usually worth it, because most of the cost of AI is not the tool. It is the time you lose getting it wrong.
  3. What gets handed off. A slide deck of recommendations is cheap to produce and easy to ignore. A working system your team is trained on and actually uses is harder, and it is the only version that pays for itself.

That last point is the one I care about most. You can read more about how I think about putting people, not tools, at the center of this in my approach to AI implementation.

What you are actually paying for

The mistake I see most often is treating an AI consultant like a software purchase. You are not buying software. The model is the cheap part now, and it is getting cheaper every month. What you are paying a good consultant for is judgment: knowing where AI belongs in your specific business, where it does not, and how to put it in without breaking the way your people work.

In practice, a strong engagement buys you four things:

  • A clear map of where AI saves you real time, not where it looks impressive in a demo.
  • The right sequence, so you build the foundation before you chase the shiny use case.
  • A built workflow your team is trained on and trusts.
  • A plan that survives past the first excited week.

That is the work. The hourly rate is just how it gets billed. If you want the longer version of how I structure a build so it does not collapse after launch, that lives in the Mission-Driven AI Stack.

How to know if it is worth it for you

Here is the simple test. Add up the hours your team loses every week to repetitive, low-judgment work. Put a real dollar figure on it. If a $5,000 project buys back even a few hours a week, permanently, the math is not close. The reason AI projects fail is almost never that they were too expensive. It is that they were aimed at the wrong thing, or nobody owned them after launch.

So do not start by asking what an AI consultant costs. Start by asking what your time is currently costing you. Then find someone who will tell you honestly where AI helps and where it does not.

If you want to figure out what the right starting point is for your business, that is exactly what an AI Strategy Session is for. We map your work, find the highest-leverage place to begin, and put a real plan and a real number in front of you, with no pressure to buy more than you need.

The model is the cheap part now. What you are paying a good consultant for is judgment.

FAQ

How much does an AI consultant cost per hour?

Most independent AI consultants charge between $150 and $350 an hour in 2026. Senior specialists and established firms charge $350 to $500 or more. Hourly rates suit advisory work and audits more than full builds.

How much does an AI consulting project cost?

A defined AI project commonly runs $2,000 to $15,000 depending on scope. A short workflow audit and starter plan sits at the lower end. A built and trained workflow for part of your business sits at the higher end, and a full multi-function build costs more.

Is hiring an AI consultant worth it?

It is worth it when the consultant aims AI at real, repetitive work your team loses hours to and hands off a system your people are trained on and actually use. The biggest cost of AI is usually not the fee. It is the time lost getting it wrong without guidance.

Should I pay hourly, by project, or on retainer?

Pay hourly for open-ended advice, by project for a defined build with a clear outcome, and on retainer when AI becomes an ongoing part of how you run. For most small businesses, project-based pricing gives the clearest value because you are buying a result, not a clock.

Want help pricing your first AI project?
We map your work, find the highest-leverage place to start, and put a real plan and a real number in front of you.

Book a 30-Min Strategy Session

What Is the 10-20-70 Rule for AI?

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The short answer

The 10-20-70 rule says the success of an AI project comes down to three parts. Roughly 10 percent is the algorithm or model. About 20 percent is the technology and the data it runs on. And 70 percent is the people and the process around it. The model everyone obsesses over is the smallest slice. The work that actually decides whether AI sticks is the human work, and it is the part most teams try to skip.

I learned this the slow way, inside real businesses, not on a slide.

Where the rule comes from

The 10-20-70 breakdown was popularized by BCG’s research on AI transformations. Once you have done a few of these, it stops feeling like a statistic and starts feeling like a law of nature. The math is not precise to the decimal. The point is the proportion. The intelligence is cheap and getting cheaper. The plumbing is solvable. The people are everything.

Why the 70 percent is the whole game

When an AI project fails, the post-mortem almost never says “the model was not smart enough.” In three years of putting AI to work inside real organizations, I have not seen that happen once. What I see instead:

  • Nobody redesigned the workflow, so the AI got bolted onto a broken process and made it faster and more broken.
  • The team did not trust it, so they quietly kept doing the old thing on the side.
  • No one owned it after launch, so it drifted, went stale, and got abandoned.
  • Leadership wanted the output without changing how anyone actually works.

That is all 70 percent work. It is change management. It is training. It is trust. It is redesigning the way a day flows so the tool has somewhere to live. None of it shows up in a demo, which is exactly why it keeps getting underfunded.

Why this is good news

Here is the part I love. If 70 percent of AI success is human, then your advantage is human too. You do not need the biggest model or the deepest pockets. You need to understand your own work well enough to put AI in the right place. That is a level playing field, and it favors the people who actually know how the work gets done.

This is the core of how I think about implementation. The gap most organizations have is not a lack of technology. It is sequence. They reach for the 10 percent first because it is exciting, and they save the 70 percent for last, if they get to it at all. Flip that order and everything changes. That is the whole idea behind the Sequence Model.

What a small business should actually do with this

If you run a small business and you are staring down AI, do not start by shopping for tools. Start with the 70 percent.

  1. Map how the work really happens now. Not the org chart. The actual flow of a day.
  2. Find the noise. The repetitive, draining, low-judgment tasks that eat your people’s hours.
  3. Put AI there first, where it buys back time without asking anyone to trust it with the hard calls.
  4. Train and document, so it survives past the first excited week.
  5. Then, and only then, worry about which model.

That is foundation before growth, which is the spine of the Mission-Driven AI Stack. Build the base, then climb.

The deeper point

The 10-20-70 rule is usually taught as a project-management warning. I think it is something bigger. It is proof, sitting right there in a consulting firm’s data, of the thing I keep saying. AI does not make the human less important. It makes the human the entire point. The technology is the cheap part now. Judgment, context, trust, and care are the expensive part, and they always will be.

So when someone tells you AI is going to replace your people, you can hand them the math. Ninety percent of the value was never in the machine.

If you want help finding your own 70 percent, that is exactly what an AI Strategy Session is for. We map your work, find the right place to start, and build a plan that puts the human back at the center.

AI does not make the human less important. It makes the human the entire point.

FAQ

What does the 10-20-70 rule mean for AI?

It means roughly 10 percent of AI success is the model, 20 percent is the technology and data, and 70 percent is people and process. The human and organizational work is the largest and most decisive part.

Who created the 10-20-70 rule?

It was popularized by BCG’s research on enterprise AI transformations and has become a common rule of thumb for why AI projects succeed or fail.

Why do most AI projects fail?

Because teams overspend on the 10 percent (the model) and underinvest in the 70 percent: workflow redesign, training, trust, and change management. The fix is sequence, not a better algorithm.

Want help finding your own 70 percent?
We map your work, find the right place to start, and build a plan that puts the human back at the center.

Book a 30-Min Strategy Session

How Can AI Be Used in Small Businesses?

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The short answer

Small businesses can use AI to handle the repetitive work that eats their week: answering common customer questions, drafting emails and content, summarizing meetings, cleaning up data, scheduling, bookkeeping prep, and first-draft marketing. The point is not to replace your people. It is to give a small team the output of a much larger one by taking the low-judgment tasks off their plate so they can spend their hours on the work that actually needs a human.

The trick is not the tool. It is knowing where to start. Here is how I think about it.

Start with where the time goes, not with the tool

Most small businesses get this backwards. They pick a shiny AI tool, then go hunting for a problem it might solve. That is how you end up paying for software nobody opens.

Do the opposite. Look at your week and find the tasks that are repetitive, rule-based, and low on judgment. Those are the ones AI is genuinely good at right now. The work that needs your taste, your relationships, or your hard-won read on a situation stays with you. AI clears the runway so you have more room for that.

The places AI earns its keep first

These are the use cases I see pay off fastest for a small team.

Customer support and FAQs. A well-set-up assistant can answer your most common questions instantly, draft replies for you to approve, and hand the hard ones to a person. You stop losing evenings to the same five questions.

Marketing and content. First drafts of emails, social posts, product descriptions, and newsletters. You are not publishing the raw output. You are starting from a draft instead of a blank page, which is where most of the time goes anyway.

Operations and admin. Meeting summaries with action items, turning messy notes into a clean doc, sorting and tagging incoming requests, prepping data before it goes to your bookkeeper. The quiet back-office work that never makes the to-do list but always takes the time.

Sales support. Research on a prospect before a call, drafting follow-ups, keeping your CRM notes current. The stuff that slips when you are busy and costs you deals later.

Personal leverage for the owner. If you are the founder, AI can act like a chief of staff: triaging your inbox into drafts, prepping you for what is ahead, and turning a scattered brain-dump into a plan. For a solo operator that is often the highest-value use of all.

You do not do all of these at once. You pick one. You can read more about why the order matters so much in the Sequence Model.

Why most small-business AI projects fail (and how to not)

The failure is almost never the technology. It is one of three things.

  1. No clear job. “We should use AI” is not a plan. “We want to cut the time we spend answering the same support emails in half” is. Pick a specific, painful, repetitive task and aim at it.
  2. Wrong order. People chase the impressive use case before they have the basics in place. Build the foundation first, then add the flashy part. Sequence beats ambition every time.
  3. Nobody owns it after launch. A tool with no owner gets abandoned by week three. Someone on your team has to own the workflow and keep it alive.

That third one is the quiet killer. The way I structure a build so it survives past the first excited week is the whole point of the Mission-Driven AI Stack. The system is built to be run by your actual people, not to impress them once and gather dust.

You do not need a big budget. You need the right first move.

Here is the part that should be a relief. You do not need an enterprise budget or a data team to start. Most of the tools a small business needs are inexpensive or already sitting inside software you pay for. What you need is the right first move: one well-chosen workflow, set up properly, owned by someone, that buys back real hours every week.

Get that one win, and the next one is obvious. The team trusts the approach because they felt the time come back. That is how AI actually takes root in a small business. Not with a big launch, but with one task that stops stealing your week.

If you want help figuring out which task to start with for your specific business, that is exactly what an AI Strategy Session is for. We map your week, find the highest-leverage place to begin, and leave you with a real plan you can act on, with no pressure to buy more than you need.

That is how AI actually takes root in a small business. Not with a big launch, but with one task that stops stealing your week.

FAQ

What is the easiest way for a small business to start using AI?

Pick one repetitive, low-judgment task that eats your time, like answering common customer questions or drafting routine emails, and set up AI to handle the first draft. Start with a single workflow, get the win, then expand. Do not start by buying a tool and hunting for a use for it.

Do small businesses need a big budget to use AI?

No. Many useful AI tools are inexpensive or already built into software you pay for. The cost that matters is not the tool, it is the time lost aiming AI at the wrong thing. The right first move matters far more than the size of the budget.

Will AI replace employees in a small business?

For most small businesses the goal is leverage, not replacement. AI takes the repetitive, low-judgment work off your team so a small staff can produce like a larger one and spend their hours on the work that genuinely needs a human.

What tasks should a small business NOT use AI for?

Keep the work that needs human judgment, real relationships, and your specific read on a situation. AI is for the repetitive and rule-based tasks. Your taste, your client relationships, and your high-stakes decisions stay with you.

Want help finding your first AI win?
We map your week, find the highest-leverage place to start, and leave you with a real plan you can act on.

Book a 30-Min Strategy Session