The AI Talent Shortage Is Real But Hiring Isn’t the Only Answer
The AI talent shortage is the second biggest challenge facing organisations investing in artificial intelligence, according to a January 2026 IDC study of 1,317 senior AI decision-makers.
Some 31.5% of respondents anticipate significant difficulties finding, hiring and retaining skilled AI and machine learning talent over the next two years. Demand for experienced data scientists, ML engineers and MLOps specialists far exceeds supply, salaries are being driven up and most SMEs simply cannot compete.
But here’s what the same research reveals – hiring your way out of this problem isn’t the answer most businesses need.
Why the AI Talent Shortage Hits SMEs Hardest
The AI talent shortage isn’t new but it’s intensifying as AI adoption accelerates. Every business deploying AI — from startups to enterprises — needs people who understand model training, infrastructure optimisation, data pipeline management and production deployment. The pool of people with these skills is small and the big tech companies and well-funded AI labs are hoovering them up at salaries most SMEs can’t match.
The IDC study highlights this as more than just a recruitment problem. When organisations can’t find the right people, AI projects slow down, models take longer to develop, deployments stall and the business falls further behind competitors that either have the talent or have found another way.
The problem is compounded by the breadth of skills required. Modern AI deployment isn’t a single discipline. You need people who understand the models, the data, the infrastructure, the security, the AI compliance and the business context. Finding one person who covers all of that is nearly impossible but building a full team is prohibitively expensive for most SMEs.
The AI Talent Shortage Doesn’t Mean You Can’t Deploy AI
The IDC research reveals a significant trend in how organisations are responding. Rather than trying to build internal AI teams from scratch, 26.3% of respondents are choosing to partner with specialist AI service providers to access external expertise and best practices.
This is the third most common strategic investment organisations are making to overcome AI challenges — ahead of adopting MLOps practices (24.1%) and investing in internal upskilling (23.5%). In other words, more businesses are outsourcing AI expertise than trying to train it internally.
This makes commercial sense. For most SMEs, AI isn’t a core competency, it’s a tool to improve operations, reduce costs and serve customers better. You don’t need a full-time data science team any more than you need a full-time legal department. What you need is the right expertise at the right stage of your AI journey.
What the Research Says About Getting AI Right Without a Full Team
The IDC study paints a clear picture of where businesses struggle and what help they actually need.
The top concern for organisations over the next two years is controlling and optimising the rising costs of AI (32.6%). This is a strategic and architectural challenge that requires someone who understands how AI infrastructure works, where waste accumulates and how to design deployments that deliver value without runaway costs. That’s consultant expertise, not a permanent hire.
The third biggest concern is measuring and demonstrating clear ROI (29.8%). More than half of respondents (53.9%) want to measure “intelligence per dollar” but can’t because of limitations in their current setup. Another 50.8% can’t do AI ROI measurement at all. Again, this is a frameworks and strategy problem — defining what success looks like, building the right measurement approaches and connecting AI outputs to business outcomes.
These challenges don’t require a team of ML engineers. They require experienced AI consultants who understand both the technology and the commercial reality of deploying it in a real business.
An AI Workshop is designed for exactly this situation. It gives your business access to specialist AI expertise in a fixed-fee diagnostic format, identifying where AI can make the biggest impact, what it will cost and what you’ll gain — without the overhead of building an internal team. From there, an AI Roadmap provides the implementation plan and AI Implementation delivers the execution with ongoing support.
Internal Upskilling Still Matters — But It’s Not Where You Start
The IDC study shows that 23.5% of organisations are investing in internal training and certifications. This is important because your team needs to understand AI well enough to work with it, manage it and make informed decisions about it.
But upskilling is a medium-term investment. It doesn’t solve the immediate problem of deploying AI effectively, avoiding costly mistakes and demonstrating ROI quickly enough to justify continued investment.
The businesses seeing the best results are combining external expertise with internal capability building. They bring in specialist partners to design and deploy AI solutions correctly, then use AI training to ensure their teams can operate, monitor and evolve those solutions over time. This is faster, more cost-effective and significantly less risky than trying to hire and build from scratch.
The Bottom Line
The AI talent shortage is real and it’s not going away but for most SMEs, the answer isn’t competing with Google and OpenAI for data scientists, it’s partnering with specialists who can deliver the expertise you need, when you need it and at a cost that makes commercial sense.
The IDC data backs this up. More organisations are turning to AI service providers than investing in internal upskilling. The businesses that move fastest on AI won’t be the ones with the biggest teams, they’ll be the ones with the right partners.
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