Nash Squared and Harvey Nash have run the Digital Leadership Report since 1998. The 2025 edition, its 26th year, surveyed 2,015 technology leaders across 62 countries. One number stood out: 51% report an AI skills shortage in their organization. Nash Squared calls it the steepest and largest jump in any technology skills shortage it has recorded in over 15 years.
I run a 35-50 person engineering studio. We build AI products for clients. I can tell you what this number actually means on the ground.
The Data
The Nash Squared report covers large enterprises and mid-market companies. The respondents are CIOs, CTOs, VPs of Engineering, Heads of Digital. These are not junior managers guessing about skills. They are the people responsible for hiring and delivery.
51% say they are suffering an AI skills shortage. That is up from 28% in the previous report, about 18 months earlier, an 82% jump. AI went from the sixth most scarce technology skill to the first. The next biggest jump Nash Squared has recorded was Big Data, at 38%.
Gartner's 2025 CIO survey tells the same story from a different angle. 63% of CIOs say AI talent acquisition is a "significant challenge." Deloitte's State of AI in the Enterprise found that 68% of organizations are increasing AI investment but only 27% feel confident in their ability to execute.
Three surveys. Three methodologies. Same conclusion: most organizations want to build with AI but cannot staff the teams to do it.
Why the Gap Exists
The gap is not about people who can write a Python script that calls the OpenAI API. That takes an afternoon to learn. The gap is about people who can put AI into production.
Production AI means: choosing the right model for the task and the budget. Building retrieval pipelines that surface the right documents. Designing prompts that work reliably across thousands of inputs, not just the 10 examples in the demo. Setting up monitoring so you know when the model starts hallucinating. Implementing cost controls so a runaway loop does not generate a $40,000 API bill overnight. Deploying on HIPAA-eligible infrastructure when the client is in healthcare.
Those skills take years to develop. They require experience shipping software, not just experience with AI. The best AI engineers I have worked with are senior software engineers who added AI to their toolkit, not AI researchers who learned to code.
What This Means for Hiring
If you are trying to hire an AI engineer full-time in 2026, here is what the market looks like:
Senior AI/ML engineers in the US command $180,000-$280,000 in total compensation. Time-to-hire for senior engineering roles has hit 95 days on average. Offer acceptance rates are falling. The people you want are already employed and not actively looking.
In Europe, the numbers are lower but the competition is still fierce. Lisbon, Berlin, and Warsaw are all competing for the same talent pool. Remote work expanded the candidate pool in 2020-2022. By 2025-2026, it also expanded the number of companies competing for every candidate.
What Actually Works
Upskill existing engineers
Your senior Laravel or Django developer who has shipped production systems for 5 years can learn to integrate LLMs, build RAG pipelines, and deploy AI features faster than an AI researcher can learn software engineering. The production skills transfer. The AI-specific skills can be taught.
We have done this at EltexSoft. Our backend engineers who built Nautical Commerce (Django, 200K+ monthly transactions) and MyFlyRight (Laravel, 10 years) now build AI backends for RiseMD and other clients. The production discipline was already there. The AI layer was additive.
Partner instead of hiring
The 95-day time-to-hire for a full-time AI engineer assumes you find one. Many companies spend 6 months searching and hire nobody. Meanwhile the AI project sits idle.
A retained engineering team with AI experience starts work in 2-3 weeks. Not because we have a magic bench of AI specialists waiting. Because senior engineers who have already shipped AI products can ramp into your domain in days, not months.
Start with integration, not research
Most companies do not need a custom-trained model. They need an LLM integrated into their existing product with proper engineering around it: API design, error handling, cost controls, monitoring, and a fallback for when the model is down.
That is an integration project, not a research project. It requires the same skills as any other software integration, plus domain knowledge about prompt engineering, token management, and model selection.
The EltexSoft Perspective
We charge $50-99/hr for senior engineers who have shipped production AI. Compare that to a full-time US AI hire at $200K+ plus 6 months of searching.
The math: a 2-person AI team on retainer at our rates costs $16,000-$32,000/month. A full-time senior AI engineer in the US costs $15,000-$23,000/month in salary alone, plus benefits, plus the 3-6 months they were not working while you were searching.
The skills gap is real. The question is whether you close it by hiring into a tight market or by partnering with a team that already has the skills in production.
We build AI products, LLM integrations, RAG pipelines, and AI agents for clients across FinTech, HealthTech, and eCommerce. Production AI. Not demos.
Last updated October 6, 2026