20/08/2026
AI Skills vs Traditional IT Skills: What Do UK Employers Want?
The debate around AI skills vs traditional IT skills is becoming increasingly important for anyone building a technology career in the UK.
Should an IT professional learn artificial intelligence instead of focusing on programming, databases, networking or cybersecurity? Do employers now expect every technology worker to become an AI specialist? Or are traditional IT skills still valuable as AI becomes part of everyday work?
The answer is more nuanced than simply choosing AI over traditional technology skills.
UK employers increasingly need people who can combine technical foundations with practical AI capability. Skills England's 2026 research says AI is changing the skills required across many occupations, but also emphasises that most workers will need practical AI literacy — the ability to use, verify and safely integrate AI tools — rather than specialist AI skills.
This means the strongest IT careers may increasingly be built on a combination of both.
Why are AI skills becoming more important in the UK IT job market?
Artificial intelligence is moving from a specialist technology into a workplace capability.
Businesses are using AI for tasks such as:
- Data analysis
- Software development
- Customer support
- Cybersecurity
- Business research
- Documentation
- Content generation
- Process automation
- Forecasting
- Knowledge management
As adoption increases, employers need people who understand how to use these technologies effectively.
The UK's AI labour market research also identifies a significant skills gap. The 2025 AI Labour Market Survey reported that 97% of surveyed organisations identified at least one AI skills gap, with technical and non-technical gaps both being reported. It also found that 35% of organisations were struggling to fill AI roles.
That does not mean every IT vacancy will become an AI job.
It means AI capability is increasingly becoming part of the wider technology skills landscape.
What are traditional IT skills?
Traditional IT skills are the technical foundations that support computing and digital infrastructure.
They include:
- Programming
- Software development
- Database management
- Networking
- IT support
- Systems administration
- Cybersecurity
- Cloud computing
- Operating systems
- Hardware
- Testing
- Infrastructure management
- Data management
These skills remain important because AI systems operate on top of technology infrastructure.
An AI application still needs:
- Servers
- Networks
- Data
- APIs
- Security
- Databases
- Software
- Cloud infrastructure
AI does not eliminate these foundations.
In many cases, it makes them more important.
What are AI skills?
AI skills cover a much broader range of capabilities than simply knowing how to use ChatGPT.
They can include:
- Machine learning
- Generative AI
- Natural language processing
- Computer vision
- AI model development
- Prompt design
- AI APIs
- Model evaluation
- Retrieval-augmented generation
- AI agents
- MLOps
- AI governance
- Responsible AI
- AI security
- Data preparation
But there is another category that is becoming particularly important:
AI literacy.
AI literacy means understanding what AI can do, how to use it effectively, how to verify its output and when not to rely on it.
This distinction matters because most IT professionals do not need to become machine-learning researchers.
They may simply need to become competent AI-enabled technology professionals.
Do UK IT professionals need to become AI specialists?
No.
This is one of the biggest misconceptions about the changing technology job market.
There are different levels of AI capability.
A software engineer may use AI coding tools.
A cybersecurity analyst may use AI for threat investigation.
A data analyst may use AI to accelerate analysis.
An IT support professional may use AI to diagnose common problems.
A cloud engineer may use AI-assisted automation.
An AI engineer, meanwhile, may build and deploy machine-learning systems.
These are completely different levels of expertise.
The UK Government's AI skills research distinguishes between specialist AI roles and the much wider workforce that will need practical AI capabilities. Its projections suggest AI-related employment could become substantially larger over the next decade, while also emphasising that not everyone in those occupations will directly work on AI itself.
The practical lesson is simple:
You do not need to become an AI engineer to become AI-ready.
Are traditional IT skills still valuable because of AI?
Yes.
In fact, many AI systems depend heavily on traditional IT expertise.
Consider an AI-powered customer service platform.
Someone still needs to manage:
- Cloud infrastructure
- Databases
- APIs
- Security
- Networking
- Identity
- Application integration
- Monitoring
An AI model may provide the intelligence.
Traditional IT systems make the application usable.
This creates opportunities for professionals who understand both sides.
For example:
Cloud + AI
Cybersecurity + AI
Data engineering + AI
Software engineering + AI
Networking + AI infrastructure
These combinations can be more valuable than treating AI as a completely separate career category.
Which is more important: AI skills or programming skills?
Neither should automatically replace the other.
Programming remains fundamental to many IT careers.
AI coding tools can generate code, but professionals still need to understand what the code is doing.
A developer needs to know whether generated code is:
- Secure
- Efficient
- Maintainable
- Correct
- Scalable
- Compatible with the wider system
Without programming knowledge, it becomes difficult to evaluate AI-generated software properly.
This creates an interesting shift.
AI may reduce the amount of code a developer needs to write manually while increasing the importance of understanding, reviewing and designing software.
Programming therefore remains valuable, but the way programmers use it is changing.
Is AI literacy becoming as important as technical IT knowledge?
For many technology professionals, it is becoming an important complementary skill.
AI literacy involves understanding:
- What AI is capable of
- Where AI can fail
- How to write useful instructions
- How to verify outputs
- How to protect sensitive information
- How to recognise hallucinations
- How to use AI responsibly
- How to integrate AI into workflows
Skills England's 2026 report specifically identifies practical AI literacy as an important workforce capability and highlights communication, critical thinking and analytical skills as cross-cutting capabilities needed alongside AI adoption.
This is important because AI competence is not simply technical.
Someone can be excellent at operating an AI tool but still make poor decisions if they cannot evaluate the results.
Which traditional IT skills work particularly well with AI?
Some traditional IT disciplines naturally complement AI.
Software development
AI can accelerate coding, testing and documentation while developers handle architecture and quality.
Cybersecurity
AI can assist with detection and investigation while security professionals manage risk and response.
Data engineering
AI depends on reliable, accessible and well-governed data.
Cloud computing
AI applications require infrastructure for computing, storage, networking and deployment.
IT architecture
AI solutions need to fit into wider enterprise technology environments.
DevOps
AI applications need deployment, monitoring, scaling and automation.
Business analysis
Someone needs to determine where AI can actually solve a business problem.
This creates a strong argument for combining skills rather than replacing one skill set with another.
Why is data becoming more important as AI adoption grows?
AI is only as useful as the information it can access and process.
Organisations need data that is:
- Accurate
- Accessible
- Structured
- Secure
- Relevant
- Governed
This makes data-related IT skills increasingly important.
A company may purchase an advanced AI platform, but if its underlying data is fragmented across outdated systems, the AI implementation may not produce useful results.
Data engineers and data professionals therefore play an important role in AI adoption.
This is one reason the future technology workforce is unlikely to consist entirely of AI specialists.
AI requires an ecosystem.
Why does cybersecurity matter more in an AI-driven IT environment?
AI creates new opportunities but also new security challenges.
Organisations must consider:
- Sensitive data exposure
- AI-generated phishing
- Automated attacks
- Model security
- Identity
- Access control
- Prompt injection
- Data poisoning
- AI supply chains
- Shadow AI
This means cybersecurity professionals need to understand AI.
At the same time, AI can assist cybersecurity teams by helping analyse large volumes of information.
The result is a feedback loop:
AI changes cybersecurity, and cybersecurity changes how organisations deploy AI.
For IT professionals, combining cybersecurity knowledge with AI awareness can therefore create a strong career direction.
Are employers looking for AI skills in non-AI IT jobs?
Increasingly, yes.
An employer hiring a software developer may not be looking for a machine-learning specialist.
But they may expect the developer to understand AI-assisted development.
A cybersecurity employer may not require the candidate to build AI models.
But AI-enabled threat detection may be part of the environment.
A data analyst may not need to train neural networks.
But they may need to use AI tools for analysis and explain the limitations of generated results.
This is why AI is increasingly becoming a horizontal skill.
It can sit across different IT disciplines.
What does the UK job market say about the demand for AI skills?
Current evidence shows a complicated picture.
The UK has strong demand for AI-related capability, but hiring conditions are not equally strong across all technology roles.
The UK Government's AI Labour Market Survey found that 35% of surveyed organisations struggled to fill AI roles, while skills gaps were reported across technical and non-technical areas.
At the same time, the government's entry-level hiring snapshot reported that UK hiring was 14% lower year-on-year in April 2026 and that software engineering was among the declining entry-level occupations, down 27%. The report explicitly warns that further research is required before attributing these changes to AI alone.
This distinction is important.
AI demand can increase while some traditional entry-level technology hiring becomes more competitive.
That is not necessarily contradictory.
Employers may be looking for fewer people to perform repetitive tasks while demanding stronger capabilities from the people they hire.
Are AI skills replacing entry-level IT skills?
Not exactly.
But the entry point into technology may be changing.
Some junior tasks are easier to automate or accelerate with AI.
For example:
- Basic code generation
- Simple documentation
- Routine testing
- Basic data manipulation
- First-line information retrieval
This can reduce the amount of time organisations need to spend on some repetitive work.
However, junior professionals still need opportunities to develop experience.
The UK Government announced in June 2026 that it was working with industry and trade unions on how AI is affecting entry-level roles and launched initiatives including AI bootcamps and support for young people entering technology-related careers.
This suggests the challenge is not simply “AI versus graduates”.
It is about redesigning pathways into work.
What should graduates learn to compete for IT jobs?
Graduates should avoid trying to learn every AI technology.
A better approach is to build a strong foundation and add AI capability.
A practical combination could be:
Core technical skill
Choose programming, networking, data, cybersecurity, cloud or another discipline.
AI literacy
Understand how AI tools work and how to use them responsibly.
Practical projects
Build something that demonstrates capability.
Problem-solving
Show how you approached a real problem.
Communication
Explain technical decisions clearly.
Portfolio evidence
Demonstrate what you can actually do.
This is particularly important because the UK AI Labour Market Survey found lack of work experience and insufficient technical skills among major barriers to filling AI positions.
For graduates, practical evidence can therefore become increasingly valuable.
Should IT professionals learn prompt engineering?
Prompt engineering can be useful, but it should not be treated as the entire AI skill set.
The ability to communicate effectively with AI systems is useful.
But employers may value broader capabilities such as:
- AI workflow design
- AI evaluation
- Data handling
- Automation
- API integration
- Model understanding
- Security
- Critical thinking
Prompting is a tool.
It is not necessarily a complete career strategy.
A stronger approach is to learn how AI fits into a real technical workflow.
Is AI certification worth getting for an IT career?
It can help, but certification should not replace practical capability.
A certificate may demonstrate that someone has studied a topic.
It does not necessarily prove they can apply it.
Candidates can strengthen certification with:
- Projects
- GitHub work
- Portfolios
- Practical assessments
- Freelance experience
- Open-source contributions
- Work experience
For example, someone applying for a cloud role could combine a cloud certification with a project showing how they deployed and secured an AI-enabled application.
That tells a much stronger story.
What AI skills should a software developer learn?
Software developers do not necessarily need to become machine-learning researchers.
Useful areas include:
- AI-assisted coding
- API integration
- LLM application development
- Testing AI-generated code
- Retrieval systems
- Prompt design
- AI security
- Model evaluation
- Automation
The most important skill may be learning how to supervise AI-generated software.
A developer who can generate code quickly but cannot identify a security vulnerability is not necessarily more valuable.
A developer who can use AI to accelerate development while maintaining quality and security is much more useful.
What AI skills should cybersecurity professionals learn?
Cybersecurity professionals can focus on:
- AI-assisted threat detection
- AI security
- Prompt injection risks
- Model security
- AI governance
- Automated investigation
- Threat intelligence
- Adversarial AI
- Security testing of AI applications
This creates a powerful combination of existing security knowledge and emerging AI capability.
It also demonstrates why traditional IT knowledge does not become obsolete simply because AI improves.
Instead, it becomes the foundation on which new skills are built.
What AI skills should data professionals learn?
Data professionals can benefit from understanding:
- Machine learning fundamentals
- Data preparation
- LLM applications
- Data quality
- AI evaluation
- Data governance
- AI-assisted analytics
- Responsible AI
- Automation
The strongest data professionals will increasingly need to understand not only how data is analysed but also how AI systems consume and interpret it.
What AI skills should IT support professionals learn?
IT support professionals can use AI for:
- Troubleshooting
- Knowledge retrieval
- Ticket classification
- Documentation
- User assistance
- Automated responses
- Problem diagnosis
But they should also strengthen skills in:
- Cloud
- Identity
- Networking
- Security
- Endpoint management
- Automation
This can help them move from repetitive first-line support towards more technical roles.
For example:
IT Support → Systems Administration → Cloud → Automation
or
IT Support → Security → SOC → Security Engineering
AI can become part of the transition rather than an obstacle to it.
Are human skills becoming more important alongside AI?
Yes.
This may seem surprising, but as AI automates more routine technical work, human capabilities can become more valuable.
These include:
- Communication
- Critical thinking
- Collaboration
- Leadership
- Empathy
- Negotiation
- Problem-solving
- Decision-making
Skills England specifically highlights communication, critical thinking and analytical capabilities as important cross-cutting skills for an AI-enabled workforce.
Technology professionals therefore should not focus exclusively on technical tools.
A person who understands technology and can communicate its implications to non-technical stakeholders can be extremely valuable.
Is the future of IT about AI skills or traditional IT skills?
The future is much more likely to be about AI-enhanced IT skills.
Consider the difference:
Traditional approach
A developer writes code manually.
AI-enhanced approach
A developer uses AI to accelerate coding, then reviews, tests, secures and integrates the result.
Another example:
Traditional cybersecurity
An analyst manually investigates security alerts.
AI-enhanced cybersecurity
AI prioritises alerts while the analyst investigates complex threats and makes decisions.
The technology changes.
The underlying professional expertise remains relevant.
What is the best skill combination for an IT career in the AI era?
A strong combination is:
Technical foundation + AI literacy + problem-solving + communication.
For more advanced careers:
Specialisation + AI + automation + business understanding.
Examples include:
- Software engineering + AI
- Cybersecurity + AI
- Cloud + AI
- Data engineering + AI
- IT support + automation
- Business analysis + AI
- DevOps + AI
- Networking + automation
- IT architecture + AI
- Project management + AI
These combinations create career flexibility.
How can IT professionals start developing AI skills?
The best approach is practical.
Start by identifying repetitive tasks in your current work.
Ask:
Can AI help me complete this faster?
Then learn how to use AI safely.
Next, learn the technology behind the workflow.
For example, a software developer could learn AI coding assistants first, then explore APIs, LLM applications and evaluation.
A cybersecurity professional could begin with AI-assisted threat analysis before moving into AI security.
A data analyst could explore AI-assisted analytics and then learn machine-learning fundamentals.
This creates a progression rather than trying to learn everything simultaneously.
What will UK employers value most as AI adoption increases?
The strongest candidates are unlikely to be those who simply list the largest number of AI tools on their CV.
Employers need people who can produce outcomes.
That means demonstrating:
- Technical competence
- Practical AI use
- Critical thinking
- Adaptability
- Communication
- Problem-solving
- Responsible technology use
The UK Government's Skills for AI programme similarly focuses on helping organisations build AI capability that is effective, safe and responsible rather than simply increasing tool adoption.
That distinction is important for job seekers.
Knowing a tool is one thing.
Knowing when, why and how to use it is much more valuable.
What does the future IT professional look like?
The future IT professional is unlikely to be divided neatly into “AI workers” and “traditional IT workers”.
Instead, the boundaries will become increasingly blurred.
A software engineer may work with AI.
A security analyst may investigate AI-generated threats.
A cloud engineer may build AI infrastructure.
A data engineer may prepare data for AI systems.
A business analyst may identify opportunities for automation.
An IT manager may oversee AI adoption.
The common factor will be technology professionals who can adapt their existing expertise to an AI-enabled environment.
Should you choose AI or traditional IT skills?
For most people, the answer is:
Do both — but build them in the right order.
Start with a strong technical foundation.
Then add AI literacy.
Then develop practical AI experience.
Finally, combine AI with your specialist area.
This approach is more sustainable than chasing every new AI tool.
The UK Government's long-term projections underline why. Research published in 2026 estimates that jobs directly involving AI activities could rise from around 158,000 in 2024 to 3.9 million by 2035, while a broader group of 9.7 million people could work in AI-related occupations.
The implication is significant.
AI will not simply create a small group of AI specialists.
It is likely to influence a much wider technology workforce.
For IT professionals, the winning strategy is therefore not to abandon traditional technology skills.
It is to make those skills more valuable by learning how to work effectively with AI.
The most competitive technology professional may not be the person who knows the most AI tools.
It may be the person who understands a technical discipline deeply, knows how AI can improve it, recognises where AI can fail and can turn technology into measurable business results.
That is where traditional IT expertise and AI skills become most powerful — together.
Frequently Asked Questions
Are AI skills replacing traditional IT skills?
No. AI skills are increasingly being added to traditional IT skills. Programming, cloud, cybersecurity, networking, databases and infrastructure remain important because AI systems depend on these technologies.
Do IT professionals need to become AI specialists?
No. Most IT professionals need practical AI literacy rather than specialist AI engineering expertise. The appropriate level depends on their role and career goals.
Which AI skills are most useful for IT professionals?
Useful skills include AI literacy, AI-assisted development, automation, AI APIs, model evaluation, data handling, responsible AI and understanding how AI applies to a particular IT discipline.
Is programming still important in the AI era?
Yes. AI can generate code, but professionals still need programming knowledge to understand, test, secure, modify and maintain software.
Should cybersecurity professionals learn AI?
Yes. AI can support security operations while also creating new security risks. Combining cybersecurity knowledge with AI security and AI-assisted detection can be valuable.
Should cloud engineers learn AI?
Yes. Cloud engineers can benefit from understanding AI infrastructure, automation, deployment, monitoring and the cloud requirements of AI applications.
Is prompt engineering enough to get an IT job?
Prompt engineering alone is unlikely to provide the same foundation as a broader technical skill set. Combining prompting with programming, data, automation, AI applications or another IT discipline is generally more useful.
Are AI certifications valuable?
AI certifications can demonstrate structured learning, but practical projects and demonstrable skills can strengthen their value. Candidates should avoid relying on certification alone.
What skills do UK employers want in an AI-driven workplace?
Employers increasingly need a combination of technical capability, AI literacy, critical thinking, communication, problem-solving, adaptability and responsible technology use.
Will AI reduce entry-level IT opportunities?
Some entry-level roles and tasks are under pressure, but the evidence does not establish that AI is solely responsible. UK Government research found software engineering entry-level hiring down 27% in its April 2026 snapshot while explicitly stating that further research is needed to identify AI's precise impact.
What is AI literacy?
AI literacy is the ability to understand, use, verify and safely integrate AI tools into work. It is broader than simply knowing how to write prompts.
What is the best IT skill combination for the future?
A strong combination is a technical specialisation, practical AI literacy, automation capability, critical thinking, communication and business understanding.
How can I add AI skills to my existing IT career?
Start with AI tools relevant to your current role, then build practical projects and learn the underlying concepts. For example, developers can explore AI-assisted coding and APIs, while cybersecurity professionals can study AI security and AI-assisted threat detection.
