Stop looking for unicorns - "AI talent" isn't one role but multiple specialized skills (data engineering, workflow design, evaluation, infrastructure). Hire for distinct capabilities, not all-in-one experts.
Focus on workflow, not models - You don't need people to build LLMs from scratch. You need engineers who can orchestrate existing models and integrate them into real business processes.
Leverage internal knowledge - Your existing staff already understand your data, exceptions, and workflows. Train them in AI rather than relying solely on external hires who lack domain context.
Fix the environment first - No amount of talent can overcome siloed data, unclear ownership, missing evaluation frameworks, or inaccessible infrastructure. Establish foundations before hiring.
Build balanced teams - Don't just hire senior experts. You need mid-level builders for execution and junior staff for routine work. Isolated experts can't do everything.
The article argues that the "AI talent shortage" is largely a myth created by companies misunderstanding what they actually need. The real problem isn't a lack of skilled people but rather poor organizational foundations, unclear roles, and misguided hiring strategies. Most companies don't need PhD researchers building models from scratch; they need people who can design workflows, integrate AI into existing processes, and work cross-functionally. Success comes from building AI fluency across teams, fixing data and infrastructure issues, and hiring for specific problems rather than following trends.
You've certainly heard companies complaining that “We can't find AI talent.” It sounds plausible, but it's actually one of the most persistent misunderstandings in today’s technology landscape. The problem is not a lack of skilled people. The problem is a mismatch between what companies think they need — and what they actually need to make AI work.
The AI talent shortage is real only in the same way the “cloud architect shortage” was real ten years ago. For most companies, it's a signal that you've misdiagnosed the problem. "Headcount Arithmetic" is fast, simple, and wrong.
Obviously, we run into this a lot here at CloudGeometry, and we've done a lot of thinking about it. Here are ten of the places where we most often see companies we talk to getting it wrong, and experience has taught us to recommend doing instead.
Mistake #1: Treating “AI talent” as a single role
Often we get a call from companies looking for an "AI engineer" and when we dig into how we can help, it turns out they're thinking of it as a hybrid of a data scientist, a DevOps engineer, a researcher, a domain expert, and a business analyst.
If it sounds like a quest for a mythic rainbow-emitting horned flying horse, there's a siple reason: that person (usually) does not exist.And even if they did, unless you are a very small outfit, you probably wouldn't want all of those critical skills concentrated in one person.
Doing AI adoption right requires multiple roles with a variety of skill sets, which include:
- Data engineering
- Data retrieval and indexing
- Agent and workflow design
- Model evaluation
- Compliance and governance
- Infrastructure and MLOps
Posting a job description that combines all of these tasks guarantees failure before the interview process even starts.
Mistake #2: Overemphasizing model-building instead of workflow-building
Unless you're doing traditional machine learning, you don't need people who train models from scratch. You definitely don't need someone to create a Large Language Model from scratch (unless you're legitimately a foundation lab). You may need someone to fine tune an existing LLM, but given the other options available, it's unlikely.
No, what you need is people who can design workflows around existing models and integrate those workflows into the real business processes your organization relies on.
When you search for “PhD-level researchers” for production systems, you filter out the engineers who can actually deliver value, self-creating a gap. The real work is orchestration, retrieval, integration and evaluation, and too few companies acknowledge this in their hiring plans.
Mistake #3: Hiring for tools rather than capabilities
Job descriptions often include long lists of tool-specific demands. One month it's PyTorch. Next quarter it's LangChain. Next year it will be some new framework three steps away from buzzword critical mass. Unless you have a very specific reason for looking for people who are experienced in a particular tool, this tool-first mindset ignores the underlying skills that matter, such as:
- Data modeling
- Structured reasoning
- System-level thinking
- Retrieval and context optimization
- Evaluation and test design
Tools come and go. Capabilities (and most crucial, the ability to acquire them) are what's goijg to make a lasting difference – especially given how quickly the ecosystem is evolving.
Mistake #4: Undervaluing internal domain knowledge
Many companies assume external AI specialists are the key to success. In our experience a significant number of AI failures stem from weak understanding of the company’s own data and processes. External hires arrive with strong technical skills but little insight into how the business actually works. They don't know where data comes from, what exceptions matter, or how decisions get made. This limits the accuracy and usefulness of the systems they build.
Internal staff already know these details, and they often adapt quickly once given the right training. Companies that overlook this talent end up with polished but impractical AI solutions that fail in real workflows. A better approach: combine internal domain expertise with targeted external support. This produces systems that reflect actual business reality and reduces rework.
Mistake #5: Not investing in AI fluency or training
Organizations often assume they have to hire fully formed AI experts and have them do all the "AI stuff". This rarely works, not least because teams without basic AI literacy can't evaluate models, define constraints, or troubleshoot issues. Even skilled hires can't succeed when they're the only ones who understand how the system behaves.
All of this means that it's essential to train the broader team. People need to understand concepts like retrieval, evaluation, constraints, and agent workflow patterns. Without this foundation, projects slow down and expectations drift. Companies that build internal fluency see better execution and faster adoption. They also create an environment where external AI talent can be productive rather than overwhelmed by having to teach the entire organization how to use the technology.
Mistake #6: Treating prompt engineering as a magic ritual
A lot of companies think better prompts will fix weak AI systems, but unless your prompts are truly terrible to start with, that rarely works. Prompting is only one layer. It can't make up for poor retrieval, missing structure, or unclear workflows.
Look, there was a time when LLMs were new in which prompt engineering was a real thing. These days, LLMs are pretty smart and not anywhere nearly as finiicky as they used to be.
Today, most meaningful improvements come from engineering choices such as schema constraints, clean context construction, and consistent evaluation. These matter far more than clever phrasing.
Hiring a prompt engineer without giving them control over data and orchestration also guarantees failure. The issue is usually a matter of system design, not wording.
Prompting works best when it's part of a broader engineering practice. You need people who understand how prompts fit into the overall system, not standalone prompt specialists.
Mistake #7. Leaving organizational structures unchanged
AI projects cut across data, infrastructure, product, and compliance, yet many companies keep these groups siloed. The unfortunate result is slow handoffs, unclear ownership, and stalled execution that gets mislabeled as a “skills shortage.” New hires can't fix this on their own; they usually don't start with the necessary authority over the parts of the system that determine success.
AI requires coordinated inputs and shared responsibility. Companies that create cross-functional teams with clear roles and unified workflows move faster and avoid the friction that makes even simple projects drag on. Once the structure supports collaboration (hint: don't expect to get it right the first time), many of the apparent talent "gaps" disappear.
Mistake #8. Expecting talent to compensate for a poorly scoped environment
Organizations often assume that hiring strong AI engineers will solve their adoption problems. This fails because most issues come from missing foundations, not missing talent. If data is siloed, compute is hard to access, policies are unclear, or evaluation criteria are undefined, even the best hires can't make meaningful progress.
The result is that new engineers end up spending their time fighting internal blockers instead of building systems. Leadership misreads this as a talent problem when the real issue is that the environment is not sufficiently ready for useful AI work.
Common blockers to look for include:
- Siloed or low-quality data
- No access to compute resources
- No clear ownership
- Missing evaluation frameworks
- Unclear definitions of success
The better approach is to establish the basics first: clean data pathways, accessible infrastructure, clear ownership, and consistent evaluation methods. With these pieces in place, even small teams can move quickly. Without them, adding more people only increases frustration.
Mistake #9: Overpaying for senior talent while underinvesting in builders
Companies often try to accelerate AI adoption by hiring one or two senior experts and expecting them to carry the entire program. This rarely works. Senior practitioners are valuable for setting direction and architecture, but they can't also handle all the resulting integration, pipeline work, testing, data cleanup, and workflow iteration that production AI systems need.
Without mid-level hands-on engineers to execute and refine the practical parts of the design, senior hires get pulled into basic implementation tasks. Their hoped-for impact falls off, projects slow down, and leadership misinterprets the slowdown as a talent issue rather than a team-structure issue. The most successful organizations build layered teams: senior staff for strategy, mid-level builders for execution, and junior staff who can scale the routine work. Balanced teams outperform isolated experts every time.
Mistake #10: Hiring based on trends rather than needs
AI hiring often mirrors whatever is fashionable in the industry. Companies chase RAG experts, then agent specialists, then multimodal engineers, regardless of whether those skills match their actual bottlenecks. This leads to mismatches between what employees are hired to do and what the business actually needs. The result is slow progress and frustrated teams.
The smarter approach is to start with the workflow: which decisions need support? Which processes merit automation? Which systems solve more problems more effectively with greater integration. Once the real problems are defined, hiring becomes straightforward and less reactive. Trend-driven hiring creates churn. Problem-driven hiring creates clarity and momentum.
What about external support?
Some companies assume they have to build all their AI capability internally. Others fall into the opposite trap and try to outsource the entire effort. Neither approach works well.
External specialists can be useful, but only when targeted by their potential impact. A good partner helps teams establish the right foundations, such as governance practices, workflow patterns, evaluation methods, and architectural choices. This guidance prevents early design mistakes that can become expensive to unwind later.
The goal is not long-term dependency. The goal is to accelerate internal learning so that your own teams can operate confidently. In practice, the best outcomes come from a hybrid approach: internal staff who understand the business – its workflows, its customers, its market – paired with external expertise that compresses the learning curve, to keep you up to date on new developments and steer clear of avoidable failures.
Bottom line: The "AI Talent shortage" is largely self-inflicted
What companies call an “AI skills shortage” is usually something else. The real challenges come from unclear roles, weak foundations, fragmented structures, and reactive hiring. External experts can be helpful, but only when paired with internal domain knowledge and a team that understands how the business actually works.
Successful organizations take a different approach. They build basic AI fluency across their teams so that everyone can participate in design and evaluation. They fix environment issues like data access and workflow clarity before expecting major results. They form cross-functional groups that share ownership instead of relying on isolated specialists. And they hire for the specific problems they need to solve, supported by balanced teams rather than single heroes.
When these pieces are in place, the perception of a “skills shortage” fades quickly. What remains is a clear path to building AI capability that grows with the organization rather than fighting against it.
