24/08/2026
How Are UK IT Leaders Changing Their Hiring Strategies Because of AI?
Artificial intelligence is changing more than the technology used inside UK businesses. It is also changing the way IT leaders think about recruitment.
For CIOs, CTOs, IT directors and technology hiring managers, the traditional approach of hiring people for a fixed set of technical skills is becoming less straightforward. AI can automate some technical tasks, increase the productivity of experienced professionals and introduce new responsibilities that did not exist a few years ago.
This is creating a new question for employers:
Should businesses hire more AI specialists, or should they make their existing IT workforce AI-capable?
The answer is increasingly becoming a combination of both.
UK IT leaders are having to reconsider job descriptions, technical requirements, workforce planning and employee development while also dealing with a rapidly changing technology market.
For IT job seekers, this means that understanding what employers are looking for can be just as important as understanding the latest AI tools.
Why is AI changing the way IT leaders hire?
Traditional IT recruitment often focused on specific technical requirements.
A job description might ask for:
- Java
- Python
- SQL
- Azure
- AWS
- .NET
- Linux
- Networking
- Cybersecurity
These skills remain important.
However, AI is changing how those skills are applied.
A developer may now use AI-assisted coding.
A data analyst may use AI to accelerate analysis.
A cybersecurity professional may use AI to investigate alerts.
A cloud engineer may work with AI infrastructure.
Therefore, IT leaders increasingly need to evaluate not only what candidates know, but also how effectively they can use modern technology to solve problems.
Are UK companies hiring more AI specialists?
There is evidence of increasing demand for specialist AI skills in the UK.
PwC's 2026 AI Jobs Barometer reported that UK job postings requiring specialist AI skills increased from approximately 112,000 in 2024 to 180,000 in 2025. It also found that AI-user roles were growing faster than AI-developer roles.
This is an important distinction.
The AI workforce is not limited to people building machine-learning models.
Organisations also need professionals who can use AI within existing business and technology roles.
That means an IT employer may not necessarily need an entire team of AI engineers.
It may need:
Software engineers who understand AI.
Cybersecurity professionals who understand AI.
Data engineers who understand AI infrastructure.
Cloud professionals who can support AI workloads.
This creates a broader AI-enabled IT workforce.
Are IT leaders prioritising AI skills over traditional IT skills?
Not necessarily.
In many cases, AI capability is being added on top of technical fundamentals rather than replacing them.
Consider a software engineering position.
A candidate who understands programming, system architecture and databases can potentially use AI coding tools effectively.
A candidate who only knows how to prompt an AI tool but cannot understand the resulting code has limited value to an engineering team.
The same principle applies to cybersecurity.
AI can help analyse security events, but a cybersecurity professional still needs to understand networks, identity, vulnerabilities and security architecture.
The emerging model is therefore:
Core IT expertise + AI capability
rather than:
AI skills instead of IT expertise.
Why are IT leaders looking for AI literacy?
AI literacy means understanding how AI works at a practical level and knowing how to use it responsibly.
An AI-literate IT professional does not necessarily need to build a large language model.
They should understand:
- What AI can and cannot do
- How to use AI tools effectively
- How to validate AI-generated information
- How AI can improve workflows
- What data should not be shared with AI systems
- How AI outputs can introduce security risks
- When human judgement is required
This is becoming relevant across IT departments.
The UK Government's AI Labour Market Survey found significant AI skills gaps among organisations and reported that 97% of surveyed organisations identified at least one AI skills gap.
That suggests employers are not simply struggling to find AI researchers.
They are also dealing with a wider shortage of people who can apply AI effectively.
Why are IT leaders changing job descriptions?
AI can make traditional job descriptions outdated surprisingly quickly.
A role written several years ago might list specific technologies without explaining how those technologies should be used alongside AI.
Modern employers may increasingly add requirements such as:
- AI-assisted development
- Automation
- AI governance
- AI security
- Data literacy
- AI tool evaluation
- Process optimisation
However, the strongest job descriptions should avoid simply adding “AI” to every requirement.
Instead, employers need to explain why AI knowledge is relevant to the position.
For example:
Instead of:
“Experience with AI tools required.”
A better requirement might be:
“Experience using AI-assisted development tools to improve software development, testing or documentation while maintaining code quality and security.”
The second description tells candidates what the employer actually expects.
Are IT leaders hiring for skills rather than job titles?
This could become increasingly important.
Technology is changing faster than job titles.
A professional might have the title:
Software Engineer
but spend significant time working with:
- AI APIs
- AI coding assistants
- Automated testing
- AI-powered documentation
- Cloud services
Similarly, a:
Cybersecurity Analyst
might work with:
- AI-assisted threat detection
- Automated investigation
- Security analytics
- AI security controls
This means employers may increasingly focus on transferable capabilities rather than relying entirely on traditional job titles.
Why are transferable skills becoming more valuable?
AI tools change rapidly.
A specific platform that is popular today may be replaced by a different tool in the future.
But skills such as:
- Problem-solving
- Programming
- Data analysis
- Systems thinking
- Security
- Communication
- Critical thinking
remain useful.
IT leaders therefore have an incentive to hire professionals who can learn.
A candidate who understands how to learn new technologies may be more valuable than someone who knows one specific tool but struggles to adapt.
Are IT leaders looking for employees who can work with AI rather than compete with it?
Increasingly, yes.
Consider a developer.
An AI system may be capable of generating code faster than a human.
Instead of competing directly with the AI, the developer can use it to:
- Generate initial code
- Suggest alternatives
- Create tests
- Explain unfamiliar code
- Identify potential issues
The developer then provides the judgement needed to validate and improve the result.
This creates a different model of productivity.
The valuable employee is not necessarily the person who writes every line manually.
It may be the person who can direct, evaluate and improve AI-assisted work.
How is AI changing hiring for software developers?
Software engineering recruitment is becoming particularly interesting because AI can now assist with many coding activities.
This may encourage employers to place greater emphasis on:
- System design
- Debugging
- Architecture
- Security
- Testing
- Code review
- Problem-solving
Candidates may increasingly be assessed on whether they understand the reasoning behind their code.
This could reduce the value of memorising simple coding patterns while increasing the importance of understanding how systems work.
For developers searching for UK IT jobs, AI should therefore be treated as a productivity tool rather than a substitute for programming knowledge.
How is AI changing cybersecurity recruitment?
Cybersecurity teams face an unusual situation.
AI can help security professionals analyse huge amounts of information, but attackers can also use AI.
This means employers may increasingly look for security professionals who understand both:
Cybersecurity fundamentals
and:
AI-enabled security threats and tools.
Relevant skills could include:
- Threat detection
- Security analytics
- Identity management
- Incident response
- AI security
- Automation
- Cloud security
Cybersecurity professionals who understand how AI changes the threat landscape may become particularly valuable.
How is AI changing data and analytics recruitment?
AI can make data analysis faster, but that does not eliminate the need for data professionals.
Instead, employers still need people who understand:
- Data quality
- Data pipelines
- SQL
- Data modelling
- Statistics
- Business context
- Data governance
AI-generated analysis must also be evaluated.
A professional who understands the data behind an answer can identify whether the answer makes sense.
This is why strong data fundamentals remain important even as AI becomes more capable.
Are IT leaders hiring more for adaptability?
Adaptability is becoming increasingly important because technology cycles are getting shorter.
An employee may need to learn several new tools during a single year.
For employers, this creates an important hiring question:
Can this person learn what we will need next year, not just what we need today?
Candidates can demonstrate adaptability through:
- Personal projects
- Certifications
- Cross-functional experience
- Open-source contributions
- Continuous learning
- Practical AI experimentation
The strongest evidence is often not a statement such as “I am adaptable”.
It is evidence that the candidate has repeatedly learned and applied new technologies.
Why are problem-solving skills becoming more important?
AI can generate answers.
But determining the correct question is often harder.
An IT professional may receive an AI-generated solution that looks technically impressive but does not address the real business problem.
A strong professional can step back and ask:
What are we actually trying to solve?
That ability becomes more valuable when AI makes information and possible solutions easier to generate.
This is one reason critical thinking may become a stronger hiring criterion.
Are IT leaders still interested in experience?
Yes.
AI does not eliminate the value of practical experience.
In some cases, experience may become even more valuable because experienced professionals understand the consequences of technical decisions.
A senior engineer can often recognise when an AI-generated recommendation is unrealistic because they have encountered similar problems in real systems.
However, employers also need to create pathways for junior professionals to develop this judgement.
This is an important workforce challenge.
How are IT leaders approaching internal upskilling?
Hiring is only one way to build AI capability.
Many organisations may also choose to train existing employees.
Internal upskilling can be attractive because employees already understand:
- Company systems
- Business processes
- Customers
- Security requirements
- Organisational culture
An employee who understands the business and learns AI may sometimes create more value than an external hire who knows AI but does not understand the organisation.
This is why AI adoption can increase the importance of professional development.
Is AI creating a “build versus buy” decision for IT hiring?
Yes.
IT leaders may have to decide whether to:
Build AI capability internally
or:
Buy AI capability from external providers.
For example, an organisation might use an external AI platform rather than developing its own model.
But even when technology is purchased, the organisation still needs people who understand:
- Integration
- Security
- Data
- Governance
- Implementation
- Business processes
Buying AI does not eliminate the need for IT professionals.
It changes the type of expertise required.
What does AI mean for IT recruitment agencies and job boards?
The change is also relevant to recruitment.
Traditional keyword matching may become less effective when job roles become more skills-based.
A candidate might have excellent AI experience without having “AI” as their job title.
Similarly, a job may require AI capabilities without explicitly using the word AI.
This makes skills-based recruitment increasingly important.
For IT job boards, clearer categorisation around technical skills, AI capabilities and transferable expertise can help connect employers and candidates more effectively.
Will AI make technical interviews more difficult?
Potentially.
If candidates can use AI during coding tasks, employers need to rethink what assessments actually measure.
A traditional coding test may measure whether someone can write code without assistance.
But the workplace increasingly involves AI-assisted development.
Employers may therefore want to assess:
- How candidates use AI
- How they verify generated code
- How they debug errors
- How they explain technical decisions
- How they evaluate alternative solutions
This could produce a more realistic hiring process.
Should candidates disclose their use of AI during technical assessments?
Candidates should follow the employer's instructions.
If an assessment prohibits AI assistance, using it can undermine the validity of the result.
If AI use is permitted, candidates should be able to explain:
- What the AI produced
- Why they used it
- What they changed
- How they tested it
- What limitations they identified
This demonstrates AI competence rather than simple AI dependence.
Are AI skills becoming essential for every IT professional?
Not necessarily.
Different roles require different levels of AI capability.
A machine learning engineer may require deep AI knowledge.
A network engineer may only need practical awareness of AI-enabled network management.
An IT support technician may need to understand AI-powered service tools.
Therefore, organisations should avoid treating AI skills as one universal requirement.
The appropriate level depends on the role.
What skills should IT job seekers develop now?
A useful strategy is to combine one core IT discipline with AI capability.
For example:
Software Engineering + AI-assisted development
Cybersecurity + AI security
Data Engineering + AI infrastructure
Cloud + AI workloads
IT Support + AI service management
Business Analysis + AI workflow design
This gives candidates a clear professional identity while demonstrating awareness of the changing technology environment.
Will AI change the definition of an experienced IT professional?
Possibly.
Experience traditionally meant having worked with technology for a certain number of years.
In an AI-enabled environment, employers may increasingly care about what professionals can accomplish with technology.
A person with five years of experience who refuses to adapt to new tools may be less competitive than someone with fewer years of experience who understands modern workflows and learns quickly.
This does not make experience irrelevant.
It changes what experience needs to demonstrate.
What should UK IT leaders do when hiring AI-ready professionals?
A practical approach is to focus on five areas.
1. Define the business problem
Do not hire for AI simply because AI is popular.
2. Identify the required AI capability
Determine what the employee actually needs to do with AI.
3. Protect core technical standards
AI should complement, not replace, technical fundamentals.
4. Assess practical ability
Use realistic projects and scenario-based assessments.
5. Invest in existing employees
Hiring alone may not solve the AI skills gap.
This approach can help organisations build a more sustainable AI workforce.
What does this mean for the future of UK IT recruitment?
The biggest change may be a move away from hiring purely around static technology lists.
Instead, recruitment could increasingly focus on:
Technical depth + AI capability + adaptability + judgement.
This does not mean traditional IT skills are becoming obsolete.
Programming, cybersecurity, cloud, databases, networking and systems architecture remain essential.
But the way professionals use these skills is changing.
For IT leaders, the challenge is to build teams that can work effectively in an environment where AI is becoming part of everyday technology operations.
For job seekers, the opportunity is to become the professional who knows both how the technology works and how AI can make that technology more effective.
The future of IT recruitment is therefore unlikely to be simply about hiring “AI people”.
It is about building AI-ready IT teams.
Frequently Asked Questions
How is AI changing IT hiring in the UK?
AI is encouraging IT employers to look beyond traditional technical skills and consider AI literacy, adaptability, problem-solving, automation and the ability to use AI responsibly.
Are UK companies hiring more AI specialists?
Demand for specialist AI skills is increasing, but organisations are also looking for professionals who can apply AI within existing roles such as software development, cybersecurity, data and cloud engineering.
Will AI replace traditional IT skills?
No. AI is more likely to change how traditional IT skills are applied. Programming, cybersecurity, cloud, networking and data skills remain important.
What AI skills do IT employers want?
Depending on the role, employers may value AI-assisted development, automation, AI security, AI governance, AI evaluation, APIs and knowledge of how AI can improve business workflows.
Are soft skills important for AI-related IT jobs?
Yes. Critical thinking, communication, collaboration and decision-making are particularly important because AI-generated outputs require human evaluation.
Should IT professionals learn AI?
Yes. IT professionals can benefit from learning how AI applies to their specific technical discipline rather than trying to become experts in every AI technology.
Is AI literacy becoming a hiring requirement?
AI literacy is becoming increasingly relevant, although the required level differs between roles. Some positions require advanced AI expertise while others need only practical AI awareness.
Will AI make IT recruitment more skills-based?
It could. As AI changes job responsibilities, employers may increasingly evaluate candidates according to practical capabilities rather than relying only on job titles or lists of technologies.
How can graduates prepare for AI-driven IT hiring?
Graduates can build strong technical fundamentals, learn AI tools relevant to their chosen career, complete practical projects and demonstrate their ability to solve problems using modern technology.
Are experienced IT professionals still valuable in the AI era?
Yes. Experience provides context and judgement that can help professionals evaluate AI-generated recommendations and make better technical decisions.
What is the best skill combination for future IT jobs?
A strong combination is core IT expertise, AI capability, adaptability, problem-solving and communication. The specific technical foundation should match the candidate's chosen career.
