There Is No Universal AI SKU: What It Costs a Midsize Company to Get Started

For most small and midsize businesses, getting started with AI consulting costs between $5,000 and $35,000. Where a company lands in that range depends on the first step it takes. Choosing and rolling out the right frontier AI plan sits near the low end. Building one specific use case, or a roadmap with ongoing advisory support, sits in the middle. Special cases such as a sovereign AI setup sit at the high end. These are typical ranges from Proxigee's own engagements as of October 2026, not quotes.

Nearly every first conversation I have with a business owner reaches the same question within a few minutes. What is this going to cost?

It is a fair question, and the range above is a fair start. The range alone tells you less than you would hope, though, because there is no universal AI SKU. You cannot pull "AI" off a shelf, compare two price tags, and pick the cheaper box. What you are buying is a step, and the step that is right for your company depends on where your business is stuck or where it is ready to grow.

Proxigee Services is a Pittsburgh-area AI consulting firm that helps small and midsize businesses choose, build, and adopt AI that pays for itself. This piece lays out what the money buys at each point in the range, why published prices disagree so widely, and how to pick a first program that earns its cost back quickly.

How much does AI consulting cost for a small or midsize business?

A first AI consulting engagement for a small or midsize business typically costs $5,000 to $35,000, and the scope of the first step sets the price.

Four things move a project up or down within that range. The first is how many tasks the AI takes on. A focused agent with a short list of responsibilities is a smaller build than one that touches a dozen workflows. The second is how ready your data and systems are. A system with a clean way to read and write data is a very different project from one where the information lives in spreadsheets and inboxes.

The third is where the AI sits relative to the systems your people already use. Working behind an existing system of record is usually the fastest route to adoption, a choice I wrote about in my piece on integrating AI with existing business systems. The fourth is how much of the capability needs to stay under your own control, which is what pushes a project toward the top of the range.

What does $5,000 buy, and what does $35,000 buy?

At the low end of the range, $5,000 buys a governed start: help choosing the right plan from the frontier AI providers and getting your team using it safely and effectively.

That sounds simple, and it is more consequential than it looks. The business plans from OpenAI, Anthropic, and Google differ in what they retain, who can review your data, and which controls you get, and the right choice depends on what your team will actually put into them. I covered those differences in detail in my piece on whether AI trains on your company's data.

A low-end engagement typically ends with a short list of things in hand: a plan matched to the data your team will use, a clear line on what information can and cannot go into AI tools, one acceptable-use rule everyone understands, a named owner for AI decisions, and a team that has practiced on its own real work. Those are the same few decisions I recommend in my piece on starting with goals and governance, made concrete. Anyone can buy a login for the price of a monthly subscription. The value is in everything around it.

The middle of the range covers one of two things. One is exploring and building a specific use case, usually an agent or workflow that works behind a system your company already runs. The other is a roadmap with an ongoing advisory cadence, where we meet on a regular schedule to review what has happened, what could be better, and what opportunities are emerging. That third question tends to surface the next initiative before anyone has planned for it, which is why I believe AI roadmaps should be built to change.

The high end of the range covers special cases. The most common one today is sovereign AI, keeping your most proprietary and critical AI capabilities on infrastructure your company controls. That work involves choosing the models, setting up the environment, and deciding which work stays in-house and which is safe to outsource. The hardware itself now costs a few thousand dollars and is billed separately from the consulting. My piece on what sovereign AI means for a business covers that decision in full.

Where does your company fall in the range?

A company can usually place itself in the range by answering four questions.

Is your team already using AI tools on its own, without an agreed plan or rules? Then the first step is usually a governed start at the low end of the range.

Is there one bottleneck the leadership team agrees on, sitting inside a system you already run? That points to a single use case in the middle of the range.

Does leadership agree AI matters but disagree on where to begin? A roadmap with an advisory cadence, also in the middle, settles that question first.

Does the work involve information that cannot leave the company, such as product formulas, pricing logic, or confidential customer records? That is where sovereign AI comes in, at the high end.

Many companies answer yes to more than one. The right first step is the one that clears the most pressing constraint with the fastest payback, and sorting that out is what a first conversation is for.

Why do published AI consulting prices disagree so much?

Published AI consulting prices disagree because the same label is attached to very different amounts of work.

Two current pricing guides show the spread. One firm's 2026 guide puts small AI strategy assessments at $5,000 to $25,000 and monthly retainers anywhere from $2,000 to more than $15,000.² Another puts a readiness audit or one simple automation under $5,000, defined-scope projects at $5,000 to $25,000, and retainers at $2,000 to $8,000 a month.³ Both are consulting firms describing their own market, and both are reasonable. They are describing different scopes under similar names.

That is the practical problem with shopping on price. An "AI assessment" from one firm may be a two-hour call and a slide deck. From another, it may be several weeks of interviews, a data review, and a ranked list of use cases with expected returns. Before comparing two numbers, put the two scopes side by side and confirm you are comparing the same work.

How do you choose the right first program?

The right first program is the next logical step for your company, the one that clears the bottleneck holding the business back or releases growth it is ready for.

Two Proxigee engagements show how different that step can look, and how each one paid for itself. A regional convenience store operator could not hire fast enough to staff its stores, and it was paying an outside firm to do much of its recruiting. The first program put an agent behind the HR system the company already used, which let the company's own team bring recruiting back in-house. Recruiters who had been running fewer than 15 interviews a week moved to more than 50. Avoiding outsourced recruiting fees paid for the work in under four months.

Another client wanted growth, so its first program expanded the company's reach into its market without adding people. It worked well enough that a hiring manager fielded more than fifty calls on a single job posting, the first time in a twenty year career. For that business, filling billable roles sooner and closing new sales leads have put the program on track to pay for itself within six months.

Hiring was the first bottleneck for one company and a result of growth for the other. Same function, different starting points, different first programs. That is why there is no universal AI SKU, and why program selection matters more than comparing price tags.

There is no universal AI SKU. The price of the first step matters. Which step you take matters more.

How fast should a first AI program pay for itself?

A first AI program should be one that can pay for itself within about six months, and a candidate use case that cannot meet that test should wait for a later phase.

This is guidance on where to start, and applying it is part of the work we do with clients. A use case with a fast, visible return builds confidence, frees budget, and gives the team a working example to build on. A use case that is generic in nature, or one with a long, uncertain payback, asks a smaller company to carry a large bet before it sees any value.

The broader market looks very different. Deloitte surveyed 1,854 senior executives and found most achieving satisfactory return on a typical AI use case within two to four years, against the seven to twelve months expected of other technology investments. Only 6 percent reported payback in under a year.¹

The survey does not break respondents down by company size, but in my observation Deloitte's work centers on large enterprises. Most of the small and midsize companies I work with could not afford to even consider an initiative with a two to four year payback. In my view, that model is not serving most large enterprises well either.

I have watched this pattern before at enterprise scale. Large ERP and advanced supply chain planning implementations ran for years, with budgets that grew and value that arrived late. In my experience, the companies that came out ahead built roadmaps that put the capabilities of most value to the business first, and planned for governance, process, change management, and data alongside the technology. AI programs should heed those lessons, and they have the chance to be more nimble and targeted. That is especially true for small and midsize businesses, where the first useful step can now be small, specific, and fast.

A smaller company has the option to choose differently. Pick the step that can pay back fast, prove it, and let it fund the next one. If you want to structure the fees themselves around results, my piece on making an AI program pay for itself covers how.

What costs sit outside the consulting invoice?

The consulting fee is one part of the cost of getting started with AI, and a realistic budget also covers software, usage, hardware where needed, and your own team's time.

Plan seats and usage fees for agents are ongoing costs. A sovereign setup adds hardware. And someone on your team has to own the decisions, test the first release, and teach colleagues what works. Plan for that time the way you plan for the fee.

What should a company expect after the first engagement?

A company should expect the first engagement to be the first step in a five year commitment, and to deliver real value on its own along the way. AI is changing how business gets done, and each round of advances opens options that were not available the year before.

The first program is a beginning. Once one constraint is cleared, the next one becomes visible, and the work moves to it. Over five years, those steps accumulate into a capability the company owns and keeps improving.

That asks two things of a client. The first is to expect real business results from each step, measured in the terms the business already uses. The second is to stay open to new ways of working. The companies that get the most from AI tend to be the ones willing to change how a job gets done, beyond adding a new tool to the old process. They also tend to aim it at growth, the larger half of the prize I described in my piece on aiming AI at growth.

The price of the first step matters. Which step you take matters more. Choose the one that pays for itself, and let it pay for the next.