Since November 2022, the term “artificial intelligence” has gained a lot of traction. We’re now in the midst of implementing AI wherever we can. As a result, companies are now looking for talent that’s comfortable with using AI and knows how to get the most out of it.  The hiring part is where things are becoming complicated. Despite the amount of discussion around AI replacing jobs, the bigger issue right now is actually the opposite: there aren’t enough people with the right AI skills to meet demand. According to a 2025 global AI adoption report from Microsoft, roughly one in six people globally are now using generative AI tools, and adoption continues to rise across industries and business functions. The important detail here is that AI adoption is growing faster than AI hiring strategies are maturing, which is creating a skills gap rather than a job shortage. This is starting to show in how companies are hiring AI talent.

AI adoption is growing, but hiring hasn’t caught up

A lot of organisations are now implementing AI in:

• automation
• customer service
• document processing
• analytics and forecasting
• software development
• internal copilots and knowledge tools

In fact, industry surveys show that over half of finance leaders are already using AI in their departments (source: Microsoft), particularly for automation, anomaly detection, and knowledge management. And that’s just one industry among so many others.  So AI is not just a tech company topic anymore. It’s now being implemented across:

• finance
• operations
• customer service
• HR
• software development
• sales and CRM systems

And that’s why we’re seeing a change in how these roles are named. For instance, the role of Data scientists is evolving into AI engineers or automation specialists to make the technology inclusive for other industries. 

The AI talent shortage is not what people think

When people talk about an AI talent shortage, they usually imagine companies competing for machine learning researchers. But that’s not the hiring demand, it’s more extended than that.  Most companies are not building large language models or training neural networks from scratch. What they are doing is:

• integrating AI into software
• automating workflows
• building AI-powered internal tools
• using AI APIs
• implementing copilots
• analysing business data

So the most in-demand profiles are actually:

• AI Engineers
• Machine Learning Engineers
• Data Engineers
• Software Developers with AI experience
• Cloud Engineers working with AI services

The shortage is not just in AI researchers, it’s in engineers who know how to apply AI in real business environments.

AI is changing existing jobs more than creating new ones

One of the most interesting trends in the labour market is that AI is not creating entirely new job categories at the scale people expected. Instead, it’s changing existing roles. However, degree requirements for most roles have decreased by 15%. Which indicates that AI is augmenting existing roles, rather than replacing them entirely.   That is why companies are increasingly hiring:

• .NET developers with AI experience
• cloud engineers with AI experience
• CRM developers using AI tools
• automation specialists
• data engineers
• software developers building AI features

The biggest mistake companies make when hiring AI talent

A company decides they want to “start using AI,” and the first thing they do is try to hire: Senior AI / Machine Learning Engineer But when you actually look at the work they need done, it’s usually:

• automation
• chatbot integration
• workflow optimisation
• reporting and analytics
• document processing
• CRM automation
• internal tools

That’s often closer to:

• software engineering
• cloud engineering
• automation development
• data engineering

Not pure machine learning research. So companies either:

• struggle to find candidates
• hire the wrong profile
• or overpay for skills they don’t actually need

This is why defining the role properly before hiring is becoming one of the most important steps in AI hiring.

Why AI hiring is difficult right now

There are a few reasons companies are finding AI hiring challenging.

1. Demand increased very quickly

AI adoption accelerated extremely fast after generative AI tools became mainstream. Hiring pipelines and university programs haven’t caught up yet.

2. Roles are poorly defined

Many job descriptions ask for:

• machine learning
• data engineering
• cloud engineering
• software development
• automation
• analytics

That’s multiple roles combined into one job description.

3. Experienced AI professionals are already employed

Most experienced AI engineers are not actively applying for jobs. Companies often need a proactive recruitment approach rather than relying on job ads.

4. AI skills are now valuable across many roles

Candidates with AI skills are significantly more likely to receive interview invitations and job offers, which means companies are competing for the same talent across multiple industries. This increases competition for AI-skilled professionals even further.

What companies should do before hiring AI talent

Before starting an AI hiring process, organisations should answer a few key questions:

1. Are we building AI models or using existing AI tools?
2. Is this an automation project, a data project, or a software project?
3. Do we need a data scientist, an AI engineer, or a developer with AI experience?
4. Do we have the data infrastructure to support AI projects?
5. Is this a one-person role or do we actually need a small team?

Companies that answer these questions first usually hire faster and make better long-term hires.

Takeaway

AI hiring is still a new area for many organisations and it shows. To add to that struggle, the companies treat it like standard software hiring.  Hiring the right talent for your needs is what’s most important here. Before you make that important decision, it’s important to focus on a few pointers: 

• clearly define what they want AI to do in the business
• hire the right type of AI role
• and move quickly when they find the right candidates

AI talent exists, but you need to have clarity before starting your hiring process.