Should I Hire an AI Engineer or Bring In a Consultant?

You know your company needs AI expertise. Maybe a competitor just shipped something that makes you nervous. Maybe your team is drowning in manual processes that feel like they should have been automated two years ago. Maybe your board is asking questions you do not have answers to yet.

So you do the obvious thing: you open LinkedIn, search "AI engineer," and start drafting a job description. And that is where the problems begin. You are about to enter a hiring process where you cannot evaluate the candidates, you cannot afford the good ones, and even if you land someone, the odds of the hire succeeding are lower than you think.

This is not a pitch to hire a consultant instead. Sometimes a full-time AI engineer is exactly the right move. But the decision deserves more data than most companies give it, and there is a third option that most mid-market leaders do not even know exists.

The Real Cost of a Full-Time AI Hire

Let us start with the numbers, because they are worse than most job descriptions suggest.

According to Levels.fyi data from over 9,500 verified profiles, the average total compensation for an ML/AI software engineer in the United States is $244,747.¹ Glassdoor places the typical range between $143,520 at the 25th percentile and $218,057 at the 75th percentile.² Senior AI engineers, the people who can actually architect and ship production systems, command $195,000 to $350,000 or more in total compensation, with staff-level roles reaching upward of $500,000 at major tech companies.¹

But salary is only the beginning. Factor in benefits, equipment, cloud computing costs ($20,000-$100,000 annually for GPU access and infrastructure), recruiting fees (8-25% of annual salary), and onboarding time, and you are looking at a first-year total investment of $300,000 to $450,000 for a single senior AI engineer.³

Now here is the part that keeps CFOs up at night: the time to actually get someone in the seat. The average hiring process for AI roles takes 58 days. But companies that cannot meet the $200,000 base salary floor for senior talent face an average time-to-fill of 114 days, nearly four months of an empty seat while your AI initiative stalls.⁴

The Evaluation Problem Nobody Talks About

Here is the paradox at the heart of AI hiring for mid-market companies: you need AI expertise to evaluate AI expertise.

When you hire a sales director, your VP of Sales can assess the candidates. When you hire a senior developer, your engineering lead can evaluate their code. But when you hire your first AI engineer, who conducts the technical assessment? Who knows whether their approach to model training is sound? Who can tell whether they are overengineering a solution or choosing the right architecture?

This is not a theoretical concern. Research shows that 76% of senior managers admit to having made a bad hire, and the U.S. Department of Labor estimates a bad hire costs at least 30% of the employee's first-year earnings.⁵ For executive and specialized technical roles, that figure can reach 213% of the position's salary.⁵ On a $250,000 AI hire, a bad decision can cost your company over $500,000 in direct and indirect losses.

The problem is compounded by how fast the AI field moves. A candidate who was cutting-edge 18 months ago may have skills that are already outdated. The difference between someone who can fine-tune a language model and someone who can build an end-to-end AI system that integrates with your existing infrastructure is enormous, and invisible to a non-technical evaluator.

The Talent Gap Is Not Closing

Even if you have the budget and evaluation capacity, finding the right person is getting harder, not easier.

The Nash Squared Digital Leadership Report, the world's largest survey of senior technology leaders covering 2,015 respondents across 62 countries, found that AI has jumped from the sixth most scarce technology skill to number one in just 18 months. The demand nearly doubled from 28% to 51%, the steepest increase in any technology skill shortage recorded in over 15 years.⁶

IDC projects that skills shortages will cost the global economy up to $5.5 trillion by 2026 in product delays, quality issues, and missed revenue, with over 90% of organizations facing critical gaps.⁷ AI proficiencies are now the most highly prized and difficult-to-source IT skill set, cited by 45% of respondents in IDC's survey.⁷

For a 200-person company competing against Google, Meta, and well-funded startups for the same talent pool, these numbers are not abstract. AI-related roles command 67% higher salaries on average compared to traditional software engineering positions.⁸ You are outbid not only on compensation but on research budgets, GPU access, publication opportunities, and the gravitational pull of working alongside other world-class AI practitioners.

Tech turnover rates in 2025 hovered between 20% and 25%, driven largely by the intense competition for specialized AI talent.⁹ Even if you manage to hire well, retention is a separate battle, one that mid-market companies tend to lose.

When Hiring Full-Time Is the Right Call

Despite these challenges, there are clear situations where building an internal AI team is the correct strategic decision. Be honest about whether your situation matches:

• AI is your core product. If your company's primary value proposition involves AI (you are building an AI-native product, not just applying AI to existing operations) you need in-house talent. Full stop.
• You have continuous, high-volume AI workloads. If you need ML models retrained weekly, real-time inference pipelines monitored daily, and a steady stream of new AI features, the math favors full-time hires over project-based engagements.
• You already have technical leadership that can evaluate and manage AI talent. A strong VP of Engineering or CTO with AI experience can recruit, assess, and retain AI engineers. Without this, you are hiring blind.
• You can offer competitive compensation. If your budget genuinely supports $200K+ total comp with interesting problems and growth opportunities, you can compete. If not, you will cycle through B-players wondering why nothing ships.

If all four conditions are true, hire. But for most mid-market companies in the early stages of AI adoption, at most one or two of these conditions hold.

The Consulting Option (and Its Limitations)

The natural alternative is to bring in outside help. But "consulting" spans an enormous range of price points and value delivery.

At the top end, traditional management consultancies charge $711-$1,200 per hour for senior partners. A typical 8-week engagement at BCG runs approximately $1.78 million.¹⁰ These firms deliver strategy (market analysis, competitive assessments, transformation roadmaps) but they rarely write code or build production systems. For a $50 million revenue company, that is a staggering investment for a PowerPoint deck, no matter how well-researched.

At the other end, freelance AI consultants on platforms charge $100-$300 per hour, with project fees ranging from $10,000 to $150,000 depending on scope.¹¹ The pricing is accessible, but the risks mirror those of hiring: how do you evaluate whether a freelancer can deliver production-quality AI systems? Who manages them? Who ensures the work integrates with your existing infrastructure?

The data shows that purely internal AI builds succeed roughly 22% of the time, while partnerships with specialized firms succeed about 67% of the time, roughly three times the success rate.¹² But a "partnership" is not the same as a traditional consulting engagement. The distinction matters enormously.

The Third Option: Build With, Then Hand Off

There is a model that most mid-market companies do not consider because it does not have a well-known category name. Call it a practitioner consultancy, a fractional AI team, or an implementation partner. The defining characteristics are:

• They build working systems, not strategy decks. The deliverable is production software, integrated workflows, and deployed AI, not a recommendation to go do those things.
• They work alongside your team, not above them. Your developers, analysts, and operations people are part of the engagement. They learn by building alongside practitioners who have done this before.
• They transfer knowledge deliberately. The engagement is structured so that by the end, your team can maintain, extend, and iterate on what was built. The goal is bootstrapping capability, not creating a dependency.
• They exit. A good implementation partner works themselves out of a job. The engagement has a defined endpoint, measurable outcomes, and a handoff plan.

Fractional AI leadership, essentially a part-time senior AI executive who guides your strategy and oversees implementation, has emerged as a growing model within this space. AI/ML-specialized fractional CTOs typically cost $6,500 to $16,000 per month, representing 60-70% savings compared to a full-time equivalent.¹³ For a mid-market company, that is $78,000 to $192,000 annually for senior AI leadership, compared to $300,000 or more for a full-time hire you may not be able to evaluate or retain.

The Decision Matrix

Rather than prescribing a single answer, here is a framework for making the decision based on your specific situation.

Factor	Hire Full-Time	Implementation Partner
AI is your core product	Yes, build the team	To supplement, not replace
First AI initiative	Risky without internal AI leadership	Ideal: get results while learning
Budget under $200K/year	Cannot compete for senior talent	Scoped engagements fit this range
Need results in 90 days	Hiring alone takes 58-114 days	Practitioners start building week one
Ongoing daily AI operations	Yes, you need dedicated staff	Build it, then hand off to your team
No internal AI evaluator	High risk of bad hire ($500K+ cost)	Partner evaluates and fills the gap
Multiple AI use cases planned	Makes sense at scale (3+ engineers)	Start with one, prove value, then staff

The Sequencing That Actually Works

The data points to a pattern that goes beyond a binary "hire" or "outsource" decision. The most successful mid-market companies follow a sequence:

Phase 1: Partner to prove value (months 1-3)

Engage an implementation partner to build your first AI system on a defined use case with measurable success criteria. This does three things simultaneously: you get a working system, your team develops hands-on AI experience, and you generate concrete ROI data. The RAND Corporation's research shows that the primary cause of AI failure is misunderstanding the problem to be solved. A practitioner partner helps you avoid that fatal mistake from the start.¹⁴

Phase 2: Build internal muscle (months 3-6)

With a working system in production and your team trained on maintaining it, you now have something no job posting can give you: the ability to evaluate AI talent from a position of knowledge. You know what good AI implementation looks like because you have been part of building one. You can write a job description based on real needs, not guesses. You can assess candidates against actual technical requirements.

Phase 3: Hire strategically (months 6-12)

Now you hire. Not because a board member told you to, but because you have proven demand for ongoing AI work. You can offer candidates something compelling: a production AI system to extend, a team that understands the basics, and clear, well-defined projects. You have also de-risked the hire because your partner can help you evaluate candidates and onboard them against working code, not abstract requirements.

McKinsey's research reinforces this approach: organizations that succeed with AI pursue fewer opportunities but focus on the most promising initiatives, expecting more than twice the ROI of organizations that spread themselves thin.¹⁵ Starting with a partner gives you that focus.

What to Look For in an Implementation Partner

Not all consultants are implementation partners. Here is how to tell the difference:

• Ask what they deliver. If the answer is "a strategy" or "a roadmap," keep looking. You want to hear "a working system in production" or "an automated workflow your team can maintain."
• Ask who does the work. At large consultancies, the partner who sells the work is not the person who does the work. You want senior practitioners, people who have built and shipped AI systems, writing the code and making the architecture decisions.
• Ask about knowledge transfer. A good partner will describe specifically how your team will be brought along. Pair programming sessions, documentation, training workshops, and a defined handoff process are all signs you are talking to the right kind of firm.
• Ask about their exit plan. If they cannot describe how the engagement ends and what your team will be able to do independently, the model is dependency, not partnership.
• Ask for measurable outcomes. "We will implement AI" is not an outcome. "We will reduce invoice processing time by 60% within 90 days" is. Insist on specificity.

Making the Call

The AI talent market is brutally competitive. IDC estimates that over 90% of organizations will face critical AI skills shortages by 2026.⁷ For mid-market companies without existing AI leadership, external help is a given. The real question is what kind.

Hiring a $250,000 AI engineer you cannot evaluate, into a team that cannot support them, to solve a problem you have not yet validated, is the most expensive way to learn what you actually need. A scoped engagement with practitioners who build real systems, transfer real knowledge, and set you up to hire strategically is both cheaper and statistically three times more likely to succeed.¹²

The companies that get AI right sequence their investments intelligently: proving value first, building capability second, and staffing permanently only when the demand is proven and the team is ready to evaluate, onboard, and retain the talent they bring in.

The goal is to be ready to hire well. That readiness almost always comes from doing the work first.