At some point, every business owner who takes AI seriously has the same thought: "We should probably just hire someone for this." A dedicated AI engineer or AI specialist. Someone in the building who owns it.
It is a reasonable instinct. It is also, for most businesses outside of tech, the most expensive way to get started, and often the slowest. This article walks through what hiring in-house actually involves, what working with a partner like us involves, and how to decide which fits your business. We will be upfront about where hiring is the right call, because sometimes it is.
What you are really buying when you hire an AI engineer
Start with the obvious cost: salary.
That is the median. It skews toward engineers at tech companies, and you may find someone for less in a smaller market. But AI talent is scarce, and anyone good enough to build reliable automations on their own has other offers.
Then add what sits on top of salary. According to the U.S. Bureau of Labor Statistics (2025), benefits account for roughly 30% of total compensation for private-sector employees. Health insurance, payroll taxes, retirement contributions, paid time off. A $150,000 salary is closer to a $200,000 annual commitment once those are in.
Then the hiring itself. SHRM's 2025 benchmarking puts the average cost per hire at about $5,475 and the average time to fill a role at 44 days. Technical roles run longer and cost more, because the candidate pool is smaller and the interviews are harder to run when nobody on your team can evaluate the technical answers.
And then the part nobody budgets for: you have to manage this person. Set their priorities. Review their work, which you cannot really do if you do not understand it. Keep them busy enough to justify the salary and interested enough to stay. If they leave, everything they built leaves with them, in the sense that nobody else knows how it works.
The hidden cost is not the salary. It is that a single AI hire makes you dependent on a single person, in a field moving faster than any one person can keep up with, doing work you have no way to check.
The one-person problem
Even if the budget works, there is a structural issue with a team of one.
Building useful AI for a business is not one skill. It is several. Someone has to understand your actual workflow and spot where the hours go. Someone has to design a tool your team will actually use. Someone has to connect it to your accounting software, your CRM, your inbox. Someone has to think about data security and what the AI is allowed to touch. Someone has to train your team. Someone has to be on call when a vendor changes their system and the automation stops working.
A senior AI engineer might be strong at two or three of those. Nobody is strong at all of them. So you either accept gaps, or you hire a second person, and now you are building a tech department instead of fixing your invoice process.
The other half of the one-person problem is continuity. AI tooling changes monthly. Model providers release new versions, prices shift, and integrations break. A lone engineer has to spend a real share of their week just keeping up, and when they take two weeks off, nobody is watching.
"We hired someone great. Then they left, and we had a system nobody could touch."
We hear a version of this from businesses more often than you would think. It is not a knock on the engineer. It is what happens when institutional knowledge lives in one head.
What working with an AI partner looks like
The alternative is to work with a team that does this for a living, for many businesses at once. Here is how it works with us, so you can compare like for like.
You get a team, not a person. When you work with Glant AI, you get people who do workflow discovery, people who build, people who handle integrations and security, and people who train your staff. Not because you are paying for all of them full-time, but because that is how the work gets done well. Each piece is handled by someone who does that piece every week.
You pay for outcomes, not hours in a chair. Every engagement is scoped before work starts: a fixed price for the build, and a fixed monthly fee for the platform that runs it, covering hosting, maintenance, and support. You know both numbers up front and can compare them directly against a salary and benefits. There is no payroll running in the background while a new hire ramps up, and no awkward conversation about what to do with them once the first project is finished.
Nothing to manage. You do not set priorities for an employee, run performance reviews, or figure out whether the work is any good. You get a scoped plan in week one, a walkthrough of the design in week two, and a working tool tested on your real documents by weeks three to five. Your side of it is about an hour a week.
It keeps working after launch. The tools we build are maintained by us. When something upstream changes, we fix it. When you want the next automation, we build it into the same workspace. You never end up with an orphaned system.
Security is built into the process, not bolted on. Before anything is connected, you get a written plan covering what we will access, how data flows, and what gets stored. Scope is agreed in writing, and anything that changes your records waits for a human on your team to approve it. You can read the full picture on our security page.
Your team gets trained. Tools only pay off if people use them. Training is part of every engagement, in plain language, run on your real work rather than generic slides.
Side by side
When hiring in-house is the right call
We would be doing you a disservice if we pretended the answer is always "work with us." There are situations where a dedicated hire makes more sense.
AI is your product. If you are building something to sell where AI is the core of what customers buy, you need that expertise inside the company. That is not an operations problem. That is your business.
You already have a technical team. If you have three or more engineers and the AI work is an extension of what they do, adding a specialist to that team is natural. They have peers to review their work, systems to plug into, and a manager who understands the domain.
The volume genuinely justifies a full-time person, every week, indefinitely. Some large operations have enough ongoing AI work to keep someone busy year-round. If you are honestly at that scale, hire. Many businesses think they are and find that the real workload is a handful of projects a year, with maintenance in between.
If none of those describe you, you are the business a partner model was designed for: you run on expertise rather than engineering, you have a few processes eating your team's week, and you want them fixed without building a tech department to do it.
A third option worth naming
Some businesses try to split the difference: hire a generalist, buy some AI software, and hope it comes together. In practice this usually produces an expensive subscription that nobody set up properly, plus a frustrated employee who was hired for something else.
If you are leaning this way, read our guide on what AI actually looks like for non-technical businesses first. The gap is rarely software. It is knowing which process to start with and having someone who can connect the pieces.
Frequently asked questions
How much does it cost to hire an AI engineer?
Glassdoor's 2026 data puts the median U.S. AI engineer salary at roughly $173,000, with total compensation often higher once bonuses or equity are included. Add about 30% for benefits (BLS, 2025), plus hiring costs averaging $5,475 per hire (SHRM, 2025). For most small and mid-sized businesses, the first year lands well over $200,000 before the first tool ships.
Is an AI partner cheaper than hiring an AI specialist?
For businesses that need a few well-built automations and ongoing support, yes, and usually by a wide margin. You pay a scoped price for the build and a fixed monthly platform fee that covers hosting, maintenance, and support. Both are agreed up front, so you can put them side by side with a salary, benefits, and hiring costs and see the difference for yourself. The exception is businesses with enough continuous AI work to keep a full-time engineer busy indefinitely, which is rarer than it sounds.
What happens if we work with a partner and later build an internal team?
The two are not mutually exclusive. The platform stays with us, and we keep maintaining it, but we are also a consultancy, so if you grow to the point where you hire your own technical people, we work alongside them. That can mean handing over documentation and context, advising on what to build next, or supporting their work on top of what already runs. Many businesses find they never get there, because the ongoing support already covers what an in-house person would have done.
Do we need any technical staff to work with Glant AI?
No. We handle the technical side from start to finish and train your team on what we build. If your team can use email and a spreadsheet, they can use what we deliver.
Figuring out which path fits
The honest test is this: do you need an AI department, or do you need a few specific problems solved and kept solved? Or, as is often the case, do you just want to be sure you are using AI well enough to stay competitive and not miss what it could do for your business? If it is either of the last two, hiring is a lot of overhead for the outcome you actually want. A partner who sees what is working across many businesses can tell you where the opportunities are, which is not something one new hire, however good, can see from inside a single company.
Not sure where you land? Our 10-question AI readiness assessment will give you a clearer picture of where your business stands, and our guide to writing an AI use policy covers the governance side either path will need.
If you would rather just talk it through, a 30-minute call is enough to map where your team's hours are going and what it would take to get them back. Book a call and we will take it from there.