Your VP of Engineering just told you they need to hire three full-time AI engineers. You're looking at $500K+ in annual salaries, plus recruiting costs, equity, benefits, and management overhead.
Six months later, one engineer quits. Another is stuck waiting on data pipeline work. The third just spent two weeks researching vector databases when a simple Postgres solution would have worked.
You're now $250K in the hole with minimal progress.
This isn't a failure of hiring—it's a failure of the full-time AI team model itself.
After working with a multitude of companies across the heartland, we've observed a pattern: Most companies don't have enough focused AI work to justify full-time AI engineers. The work is bursty, experimental, and requires deep specialization that changes project-to-project.
Meanwhile, mass tech layoffs in 2023-2024 revealed what many suspected: companies were over-hired, with specialized teams sitting idle or inventing work to stay relevant.
This post will show you:
- Why the full-time AI team model fails for most companies
- The real utilization rates of specialized AI talent
- How embedded team models solve the overhiring problem
- What to look for when evaluating AI engineering partners
- When you actually need full-time AI engineers
Let's start with why your expensive AI team is probably underutilized.
The Utilization Problem Nobody Talks About
Here's what nobody tells you about hiring full-time AI engineers:
Week 1-4: They're ramping up, learning your codebase, understanding your data Week 5-8: High productivity, shipping features, running experiments Week 9-12: Waiting for data pipeline fixes, blocked on infrastructure Week 13-16: "Improving the model"—but is anyone measuring if it matters? Week 17-20: Asked to help with non-AI work because there's not enough AI work
Real utilization on actual AI work? Maybe 40-50%.
The rest is overhead, blocked time, invented work, or helping with general engineering.
At $200K+ salary, you're paying $2,000-$3,000 per productive day. That's not their fault—it's structural.
Why AI Work Doesn't Fit the Full-Time Model
AI engineering work has unique characteristics that make it incompatible with traditional full-time roles:
1. Highly Bursty Work Patterns
AI projects have intense periods followed by quiet stretches:
- Initial implementation: 4-6 weeks of intense work
- Evaluation phase: 2-3 weeks of measurement and iteration
- Maintenance mode: 2-4 hours per week
After the initial implementation, ongoing AI work is often just monitoring metrics and occasional tuning. But you're still paying a full salary.
2. Requires Constantly Shifting Specializations
One month you need RAG expertise. Next month you need fine-tuning. Then evaluation frameworks. Then production MLOps.
No single engineer has deep expertise across all these domains. So you either:
- Hire multiple specialists (expensive, even more underutilized)
- Have generalists who are "good enough" at everything but expert at nothing
3. Unpredictable Experimentation Cycles
Unlike traditional software (where you scope a feature and build it), AI work is:
- 60% experiments that fail
- 30% experiments that work but aren't worth the cost
- 10% experiments that ship
You can't plan this work in sprints. You can't predict utilization. Your "AI team" might spend three weeks proving an idea won't work—valuable learning, but zero shipped features.
The Post-Layoff Reality
The 2023-2025 tech layoffs exposed a truth many executives suspected: companies were dramatically over-hired, especially in specialized roles.
Why? Because in a zero-interest-rate environment, companies optimized for speed over efficiency. They hired full-time specialists for work that could be done fractionally or episodically.
The result:
- Engineers invented projects to stay relevant
- Teams expanded their scope beyond core competencies
- Utilization was low, but headcount was a status symbol
- Real productivity per dollar spent was abysmal
Why Embedded Teams Work Better
The solution isn't to hire cheaper engineers or work them harder—it's to fundamentally rethink the engagement model.
This is where embedded teams shine. Instead of hiring full-time AI specialists, companies work with specialized firms that provide:
1. On-Demand Expertise Across Multiple Domains
Need RAG implementation this month? You get a RAG specialist. Need evaluation frameworks next month? You get an evaluation expert. Need production MLOps after that? You get someone who's done it 50 times.
2. High Utilization Through Portfolio Distribution
A firm can keep AI specialists fully utilized by distributing their time across multiple clients:
- Company A: 15 hours/week during implementation phase
- Company B: 10 hours/week during evaluation phase
- Company C: 5 hours/week in maintenance mode
The specialists stay engaged and sharp. Companies get exactly what they need, when they need it.
3. Built-In Knowledge Transfer and Best Practices
Specialized firms see patterns across dozens of companies. They know:
- What experiments usually fail (so you don't waste time)
- What evaluation frameworks actually work in production
- How to structure AI work for maximum velocity
- Which tools are hype vs. production-ready
This accumulated wisdom compounds. Your full-time hire is learning from their mistakes. An embedded team learned from everyone else's mistakes.
4. No Overhead, No Retention Risk
With embedded teams:
- No recruiting costs
- No benefits, equity, or management overhead
- No retention risk when someone gets a better offer
- No deadweight when projects wind down
You pay for results, not seats filled.
The Three Capabilities You Actually Need
Whether you hire full-time or work with an embedded team, AI engineering success comes down to three core capabilities:
1. Experimentation Velocity
Can they run experiments quickly and learn from failures?
The best AI engineers don't optimize for elegant solutions—they optimize for learning speed. They'd rather run ten messy experiments in a week than spend a month architecting the "perfect" system.
What this looks like:
- Examining actual failure logs before proposing solutions
- Building quick evaluation tools to test hypotheses
- Running 5-10 prompt variations in a day
- Prioritizing speed of iteration over architectural elegance
2. Measurement Discipline
Do they instinctively measure, log, and analyze everything?
AI systems are probabilistic—a "bug" might be a 5% regression in a metric you're not tracking. Teams that succeed have measurement built into their DNA.
What this looks like:
- Logging every AI interaction with full context
- Flagging and categorizing failed outputs immediately
- Defining success metrics before implementing changes
- Comparing before/after distributions after deploying changes
3. Product Intuition
Can they connect technical work to real user value?
AI systems exist in a probabilistic space with infinite possible improvements. The question isn't "can we make this better?"—it's "does this improvement actually matter to users?"
What this looks like:
- Translating vague user complaints into specific technical experiments
- Knowing when "good enough" is actually good enough
- Prioritizing improvements based on user impact, not technical interest
- Communicating trade-offs clearly to non-technical stakeholders
How to Evaluate AI Engineering Partners (or Hires)
Whether you're evaluating a potential embedded team partner or deciding if you need to hire full-time, the evaluation criteria are the same.
Questions to Ask Embedded Team Partners
For Experimentation Velocity:
- "Show me a case study of a failed experiment and what you learned"
- "Walk me through your typical evaluation framework"
- "How many iterations do you typically run before shipping?"
For Measurement Discipline:
- "What metrics do you track by default on every AI project?"
- "How do you handle evaluation drift over time?"
- "Show me how you log and analyze failed outputs"
For Product Intuition:
- "Describe a time you pushed back on a feature request because the data didn't support it"
- "How do you decide when an improvement is 'good enough' to ship?"
- "What's your process for translating user feedback into technical work?"
Questions for Full-Time Hire Candidates
Scenario-Based Question:
"You just deployed a feature that generates product descriptions using GPT-4. Users are complaining that 30% of descriptions are 'off-brand.' You have one week to improve this. Walk me through your first 72 hours."
What you're listening for:
- Do they immediately ask to see actual examples?
- Do they propose running small, quick experiments first?
- Do they talk about measurement before solutions?
- Do they mention building tooling to accelerate iteration?
Red Flag Response:
"I'd research state-of-the-art prompt engineering techniques, read papers on brand alignment, and design a comprehensive evaluation framework..."
Green Flag Response:
"First, I'd look at 20-30 examples of 'off-brand' descriptions to understand what users mean. I'd categorize the failure modes—maybe it's tone, length, or specific word choices. Then I'd add those examples to a test set, write a simple pass/fail eval, and iterate on the prompt with immediate feedback. I'd probably run 5-10 variations in the first day."
The "Show Your Work" Exercise
For full-time hires, give them this scenario:
Prompt: "Here's a RAG system that's returning irrelevant chunks 40% of the time. You have access to the codebase, query logs, and a staging environment. What do you do first?"
Give candidates 10 minutes to outline their approach. Then have them screenshare and actually start implementing—accessing logs, writing scripts, building quick evaluation tools.
What this reveals:
- How quickly they orient to a new codebase
- Whether they build tooling or just read code
- If they look at data before theorizing
- How they handle ambiguity and time constraints
The best candidates start writing Python scripts to analyze logs within 5 minutes. Weaker candidates spend the entire time asking clarifying questions or whiteboarding architectures.
Red Flags That Look Like Green Flags
The AI engineering market is full of misleading signals. Here's what to watch for:
In Partners or Hires
"We/I published 3 papers at NeurIPS"
- What it signals: Strong research ability
- What it doesn't signal: Ability to ship production features quickly
- Reality: Research and production AI require completely different skill sets
"We/I built this with the latest framework (LangChain/LlamaIndex/etc.)"
- What it signals: Keeps up with trends
- What it doesn't signal: Understands fundamentals
- Reality: The best AI engineers often avoid heavy frameworks in favor of direct API calls and custom tooling
"We/I optimized the model to achieve 99.5% accuracy"
- What it signals: Perfectionism, attention to detail
- What it doesn't signal: Product judgment
- Reality: Going from 95% to 99.5% often costs 10x development time for negligible user impact
"We/I built 50+ AI applications"
- What it signals: Prolific builder
- What it doesn't signal: Depth of experience
- Reality: Building lots of demos is different from iterating on production systems with real users
Green Flags That Look Like Red Flags
"Most of our/my experiments failed"
- What it looks like: Incompetence
- What it actually signals: High experimentation velocity
- Reality: The best AI engineers run lots of experiments; most fail
"We/I spent two weeks just looking at data"
- What it looks like: Analysis paralysis
- What it actually signals: Measurement discipline
- Reality: Understanding the problem deeply before building is high-leverage
"I don't have a CS degree"
- What it looks like: Lack of fundamentals
- What it actually signals: Self-directed learning
- Reality: Many of the best AI engineers are self-taught; they had to learn by doing
"We're/I'm not sure what the best approach is yet"
- What it looks like: Uncertainty, lack of confidence
- What it actually signals: Intellectual honesty
- Reality: AI engineering is uncertain by nature; people who admit this tend to iterate faster
When You Actually Need Full-Time AI Engineers
Embedded teams aren't always the answer. You should hire full-time AI engineers when:
1. You Have Sustained, High-Volume AI Work
If you're shipping multiple AI features every week and have a roadmap for the next 12 months, full-time makes sense.
Examples:
- You're building an AI-first product (like an AI coding assistant)
- You have dozens of models in production requiring constant iteration
- Your core product is AI-powered (not just AI-enhanced)
2. You Need Deep Domain-Specific Knowledge
If your AI work requires deep expertise in a specialized domain that's hard to find, hiring someone full-time to build that expertise internally makes sense.
Examples:
- Medical AI requiring clinical knowledge
- Legal AI requiring understanding of case law
- Financial AI requiring regulatory compliance expertise
3. You're Pre-Product and Need to Move Fast
Early-stage startups with significant funding often benefit from full-time AI engineers who can move quickly without coordination overhead.
Reality check: This only works if you have:
- Clear product direction (not just "let's use AI")
- Sufficient runway (18+ months)
- Ability to attract top talent (equity story, mission, location flexibility)
4. Your AI Work Requires Security/IP Protection
If you're working with highly sensitive data or building proprietary models that represent core IP, embedded teams may not be appropriate.
Examples:
- Defense contractors
- Healthcare companies with strict HIPAA requirements
- Companies building proprietary foundation models
The Grey Haven Embedded Team Model
At Grey Haven, we work with companies across the heartland—Oklahoma City, Dallas, Tulsa, Nashville, Kansas City, Little Rock—who need AI expertise but can't justify (or don't want) full-time AI teams.
How It Works
1. We embed with your existing team
- Work directly in your Slack, GitHub, codebase
- Attend standups and planning meetings
- Collaborate with your engineers daily
2. We scale with your needs
- 10 hours/week during planning and evaluation phases
- 30 hours/week during implementation sprints
- 5 hours/week during maintenance mode
3. We transfer knowledge, not dependencies
- Document everything we build
- Train your team on evaluation frameworks
- Leave you with the tools to maintain and improve
4. We bring cross-company insights
- Patterns from dozens of AI implementations
- Best practices that actually work in production
- Honest assessments of what's hype vs. reality
Who This Works For
Embedded teams work best for:
- Series A-C companies adding AI features to existing products
- Established companies in traditional industries (manufacturing, agriculture, energy, logistics)
- Companies with strong engineering teams who need AI expertise, not general engineering help
- Organizations building 2-5 AI features per quarter (not 20)
Who Should Hire Full-Time Instead
You should hire full-time if you:
- Are an AI-first product company
- Have sustained, high-volume AI work
- Need someone in-office every day for team cohesion
- Are building core AI IP that requires full-time focus
Building AI Capability in the Heartland
For companies in Oklahoma City, Dallas, Tulsa, Nashville, Kansas City, Little Rock, and other heartland markets, the embedded team model offers unique advantages:
1. Access to Top Talent Without Competing on Salary
You're not competing with SF and NYC salaries for full-time hires. You're paying for results, not seats.
2. Leverage Domain Expertise
Your team already understands manufacturing, agriculture, energy, or logistics. You just need AI expertise to augment that domain knowledge—not replace it.
3. Avoid the "AI Team Sitting Idle" Problem
When your AI work slows down, you're not paying for idle capacity. The embedded team scales down or shifts to other clients.
4. Learn While Building
Embedded teams transfer knowledge as they work. Your engineers learn evaluation frameworks, prompt engineering, and production AI practices—skills they keep even after the engagement ends.
Conclusion: Rethink the Model, Not Just the Hire
The question isn't "how do we hire better AI engineers?"—it's "do we need to hire AI engineers at all?"
For most companies:
- AI work is bursty and unpredictable
- Specialization requirements shift constantly
- Utilization rates are 40-50% at best
- Embedded teams provide better ROI
The tech layoffs of 2023-2024 proved what many suspected: overhiring is expensive and inefficient. Knowledge-specific firms (including AI engineering firms, development shops, and specialized consultancies) have thrived because they solve the utilization problem—they keep specialists engaged across multiple clients.
Before you post that "AI Engineer" job listing, ask yourself:
- Do we have 40+ hours per week of focused AI work?
- Does this work require deep institutional knowledge?
- Are we building AI-first products or adding AI features?
- Can we afford to pay full salary during slow periods?
If the answer to these questions is "no" or "not sure," an embedded team model is probably better.
What's Next?
If you're evaluating whether to hire full-time or work with an embedded team:
For Companies:
- Assess your AI workload: Do you have sustained, high-volume work?
- Evaluate your team: Do you have strong engineers who need AI expertise?
- Consider your timeline: Can you afford 3-6 months to hire and ramp up full-time engineers?
For Grey Haven Partnerships: We work with companies who need:
- Rapid AI implementation (without the hiring overhead)
- Evaluation frameworks and measurement discipline
- Knowledge transfer to existing engineering teams
- Honest guidance on what's worth building vs. what's hype
Reach out at Grey Haven to discuss your AI strategy.
Read Next:
- Your RAG System Needs Evals – How to systematically improve AI products
- AI Engineering Flywheel – How production data compounds value
- Build Your AI Company in the Heartland – Why the best AI companies are being built outside Silicon Valley
