21/08/2026
Can AI Create More IT Jobs in the UK Than It Replaces?
Artificial intelligence is changing the UK technology job market, but the biggest question is not simply whether AI will replace IT jobs. A more useful question is whether AI will create enough new technology work to offset the tasks and roles it changes.
For IT professionals, graduates and employers, this distinction matters. AI can automate repetitive tasks, but it also creates demand for software, infrastructure, data, cybersecurity, governance, integration and new specialist skills.
UK Government research published in 2026 projects that employment in jobs directly involving AI activities could increase substantially by 2035, while millions more people are expected to work in occupations where AI-related skills become relevant. At the same time, evidence on entry-level hiring shows that some technology roles are becoming more competitive.
The result is not a simple story of AI replacing IT workers. It is a story of IT jobs being redesigned around AI.
Is AI creating more IT jobs in the UK?
AI is already creating demand for new technology capabilities.
Organisations adopting AI need people who can build, deploy, integrate, secure and manage these systems.
That creates demand across areas such as:
- AI engineering
- Machine learning
- Data engineering
- Cloud infrastructure
- AI security
- MLOps
- Software engineering
- Data governance
- AI testing
- AI product management
- Technology architecture
- Automation
- AI compliance and risk
The UK's AI Labour Market Survey found that AI skills gaps remain widespread among employers. It reported that 97% of surveyed organisations identified at least one AI skills gap, while 35% reported difficulty filling AI roles.
This suggests an important trend.
AI does not only need AI specialists.
It needs an entire technology ecosystem around it.
Which new IT jobs are being created by AI?
Some roles are directly connected to developing AI systems.
These include:
AI Engineer
AI engineers build and integrate AI capabilities into applications and business systems.
Machine Learning Engineer
Machine learning engineers develop, train, evaluate and deploy machine-learning models.
MLOps Engineer
MLOps professionals help organisations deploy, monitor and maintain machine-learning systems at scale.
AI Security Specialist
AI security professionals focus on protecting AI systems and managing new security risks.
AI Governance Specialist
These professionals help organisations establish rules, controls and processes for responsible AI adoption.
AI Product Manager
AI product managers connect customer needs, business objectives and AI capabilities.
AI Data Engineer
These professionals build the data infrastructure required by AI applications.
But the more important trend is that AI is also creating new responsibilities inside existing IT jobs.
A cloud engineer may become responsible for AI infrastructure.
A cybersecurity analyst may investigate AI-generated threats.
A software engineer may develop AI-powered applications.
A data analyst may work with AI-assisted analytics.
This means AI-related employment is broader than the number of jobs with “AI” in their title.
Will AI replace software developers in the UK?
AI is already changing software development.
Generative AI can help developers with:
- Code generation
- Debugging
- Documentation
- Testing
- Refactoring
- Prototyping
- Code explanation
This can reduce the time required for some tasks.
However, software engineering involves much more than writing lines of code.
Developers also need to understand:
- Architecture
- Requirements
- Security
- Performance
- Scalability
- Integration
- Testing
- Maintenance
- User needs
The UK Government's entry-level hiring snapshot found software engineering among occupations experiencing declining entry-level hiring, with a 27% fall in the period analysed. However, the report explicitly cautions that further research is required before attributing the change to AI.
That distinction is essential.
A decline in hiring does not automatically prove AI replacement.
Economic conditions, changes in recruitment, outsourcing, business investment and productivity improvements can all influence hiring.
Could AI actually increase demand for software engineers?
Yes.
If AI makes software development cheaper and faster, organisations may decide to build software they previously could not justify financially.
This is sometimes described as a productivity or demand-expansion effect.
For example, a company that previously needed six months to develop an internal application may be able to create a prototype much faster using AI-assisted development.
That could encourage the business to build more applications.
The developer's role then changes from manually producing every component to designing, validating, integrating and maintaining increasingly complex software.
The result could be fewer developers for certain types of work but more software development overall in other areas.
This is why predicting total employment from automation alone is difficult.
Why does AI create demand for cloud engineers?
AI requires infrastructure.
Large AI applications need:
- Computing power
- Storage
- Networking
- Databases
- Security
- Monitoring
- Deployment
- Scalability
Cloud engineers can therefore become important to AI adoption.
Businesses need to decide:
- Where AI workloads should run
- How infrastructure should scale
- How data should be protected
- How costs should be controlled
- How systems should be monitored
- How AI applications integrate with existing platforms
AI may automate some infrastructure management tasks, but it can simultaneously increase the complexity of the infrastructure being managed.
This creates an important career combination:
Cloud + AI infrastructure.
Why could AI increase demand for data engineers?
AI systems require data.
But organisations often have data spread across multiple systems.
Data may exist in:
- Databases
- Cloud platforms
- CRM systems
- Business applications
- Spreadsheets
- APIs
- Legacy systems
Someone needs to make that information usable.
Data engineers build the pipelines and infrastructure that allow organisations to collect, transform, store and deliver data.
As AI adoption grows, data quality can become more important rather than less important.
An AI system trained or supplied with poor-quality information can produce unreliable results.
This makes data engineering one of the strongest examples of a traditional IT discipline that can become more valuable because of AI.
Why could AI create more cybersecurity jobs?
AI creates both defensive opportunities and new threats.
Cybersecurity teams need to consider:
- AI-generated phishing
- Automated attacks
- Model vulnerabilities
- Data leakage
- Prompt injection
- Identity attacks
- AI supply-chain risks
- Sensitive information exposure
- Adversarial AI
At the same time, cybersecurity teams can use AI for:
- Threat detection
- Alert prioritisation
- Incident investigation
- Log analysis
- Threat intelligence
- Security automation
This creates a two-sided effect.
AI can automate parts of security work while simultaneously creating new security problems.
That means cybersecurity professionals may spend less time performing repetitive analysis and more time handling complex threats.
Are AI jobs only for people with advanced degrees?
No.
Some specialist AI research roles require advanced mathematical or academic knowledge.
But the wider AI-enabled technology workforce is much broader.
People can enter AI-related careers through different routes, including:
- University degrees
- Apprenticeships
- Technical training
- Professional certifications
- Work experience
- Self-directed learning
- Practical projects
The important distinction is between AI research and AI application.
A business may not need an employee to invent a new machine-learning algorithm.
It may need someone who can integrate an existing AI model into its software platform.
Those are very different skill requirements.
What IT jobs could be created indirectly by AI?
Some of the most interesting employment effects may happen outside obvious AI job titles.
For example, an organisation implementing AI may need:
Software engineers to integrate AI.
Cloud engineers to provide infrastructure.
Data engineers to prepare data.
Cybersecurity specialists to secure systems.
Business analysts to identify useful applications.
Project managers to manage implementation.
IT support professionals to support new workflows.
Compliance specialists to manage governance.
Technical writers to document systems.
Trainers to help employees use new technology.
This creates a wider AI employment ecosystem.
Will AI create more senior IT jobs than junior IT jobs?
This is one of the most important questions for graduates.
AI can automate many repetitive tasks that traditionally helped junior professionals gain experience.
For example, AI can assist with:
- Basic coding
- Documentation
- Data manipulation
- Simple testing
- Information retrieval
- Routine troubleshooting
This could make some entry-level roles more competitive.
At the same time, companies still need people who can understand complex systems and make decisions.
That creates a possible shift towards higher-value entry-level expectations.
Instead of asking only:
“Can you code?”
employers may increasingly ask:
“Can you solve problems using code and AI?”
Instead of:
“Can you analyse data?”
they may ask:
“Can you interpret, validate and communicate AI-assisted analysis?”
This changes what it means to be job-ready.
Does AI make traditional IT experience less valuable?
Not necessarily.
Traditional IT knowledge can become a competitive advantage when combined with AI.
Consider a cybersecurity professional with ten years of experience.
They already understand:
- Networks
- Identity
- Threats
- Security controls
- Incident response
Adding AI knowledge allows them to understand how those systems interact with emerging AI threats.
A cloud engineer with strong infrastructure knowledge can learn AI workloads.
A software developer can learn LLM application development.
A database professional can learn modern data platforms for AI.
The pattern is clear:
Existing expertise + AI capability can be more powerful than AI knowledge alone.
Which IT skills will AI create demand for?
Several skills are likely to become increasingly important.
AI integration
Businesses need professionals who can connect AI systems with existing technology.
AI evaluation
Someone needs to test whether AI outputs are accurate and useful.
AI security
AI systems introduce new attack surfaces.
Data engineering
AI requires reliable data infrastructure.
Cloud infrastructure
AI workloads require computing and deployment environments.
Automation
AI becomes more valuable when connected to automated workflows.
Systems architecture
AI applications need to fit into wider technology ecosystems.
Business analysis
Companies need to identify where AI actually creates value.
AI governance
Organisations need policies and controls around AI use.
These skills create opportunities beyond traditional “AI developer” roles.
Could AI create IT jobs that do not exist today?
Almost certainly.
Technology has repeatedly created job categories that were difficult to predict before the underlying technology became mainstream.
The internet created roles around web development, digital infrastructure and online commerce.
Cloud computing created new infrastructure and platform roles.
Mobile technology created app development ecosystems.
AI is likely to produce its own combinations of roles.
Some may involve technologies that are not yet common job titles.
That makes career planning based only on today's job titles risky.
A better strategy is to develop transferable technical capabilities.
What happens to IT support jobs as AI improves?
IT support is likely to experience significant automation.
AI can already assist with:
- Password-reset workflows
- Knowledge-base searches
- Ticket classification
- Troubleshooting
- User guidance
- Common technical questions
But IT support professionals can move towards more complex responsibilities.
For example:
Level 1 support → automation → systems administration → cloud
or:
Level 1 support → networking → infrastructure → cloud engineering
or:
IT support → security → SOC → security engineering
AI can therefore reduce repetitive support work while creating pressure for professionals to develop deeper technical expertise.
Can AI create more jobs through productivity gains?
Yes, although the outcome is not guaranteed.
When technology makes a task cheaper, organisations may respond in different ways.
They may:
- Reduce labour requirements.
- Produce more with the same workforce.
- Create new products.
- Expand existing services.
- Invest savings into other areas.
The second, third and fourth outcomes can create additional employment.
This is one reason why economists and policymakers are cautious about predicting the net employment effect of AI.
The technology affects productivity, demand, business models and skills simultaneously.
Is AI changing the type of IT jobs available rather than simply reducing jobs?
This may be the most useful way to understand the transition.
Imagine an organisation that previously employed IT workers to perform repetitive manual tasks.
After AI adoption, some of those tasks may disappear.
But the organisation may now need professionals to:
- Design the automated workflow
- Integrate AI
- Monitor performance
- Secure the system
- Evaluate outputs
- Manage data
- Improve processes
The work has moved up the value chain.
This does not guarantee that every displaced worker can immediately move into the new roles.
That is where reskilling becomes important.
What skills should IT professionals learn now?
IT professionals should not try to learn every AI technology.
Instead, they should ask:
How will AI affect my current profession?
A software developer can learn AI-assisted development.
A data professional can learn machine learning and AI analytics.
A cybersecurity professional can learn AI security.
A cloud engineer can learn AI infrastructure.
An IT project manager can learn AI implementation and governance.
This approach creates career-adjacent learning.
It is usually more practical than attempting to completely change careers.
What should IT graduates learn to stay competitive?
Graduates should build three layers of capability.
Layer 1: Technical foundation
Learn a genuine IT discipline.
Examples include:
- Programming
- Cybersecurity
- Cloud
- Networking
- Data
- Software engineering
Layer 2: AI literacy
Learn:
- Generative AI
- AI tools
- Prompting
- AI limitations
- Responsible AI
- AI-assisted workflows
Layer 3: Practical experience
Build projects.
Show employers that you can apply your knowledge.
This combination can make a graduate more attractive than someone who only lists AI tools without demonstrating technical foundations.
Will employers prefer candidates who know how to use AI?
In some roles, AI capability may increasingly become a differentiator.
But employers are unlikely to value AI knowledge in isolation.
A candidate who says:
“I know several AI tools”
is less compelling than one who can say:
“I used AI to automate this process, reduced manual work, tested the results and built controls to prevent errors.”
The second example demonstrates outcomes.
That is what employers ultimately need.
Does AI mean IT professionals need fewer technical skills?
Not necessarily.
It may mean they need different combinations of technical skills.
For example, writing code manually may become less important for some tasks.
But understanding:
- Architecture
- Security
- Testing
- Integration
- Performance
- System behaviour
can become more important.
The technology changes the distribution of skills rather than eliminating the need for technical expertise altogether.
What is the biggest opportunity AI creates for IT professionals?
The biggest opportunity may be the ability to become significantly more productive.
An experienced professional can use AI to:
- Research faster
- Analyse information
- Generate prototypes
- Automate repetitive work
- Document systems
- Explore technical solutions
- Support decision-making
This can allow professionals to spend more time on complex work.
The result is potentially a shift from:
Task execution
towards:
Problem solving + system design + decision-making.
That can increase the value of experienced IT professionals.
What is the biggest risk AI creates for IT professionals?
The biggest risk may not be immediate job replacement.
It may be skills stagnation.
Technology professionals who continue performing work exactly as they did several years ago may become less competitive as AI-enabled workflows become standard.
A professional does not necessarily need to become an AI expert.
But they should understand how AI is changing their discipline.
Ignoring the technology entirely can become a bigger career risk than learning it.
Will AI ultimately create more IT jobs than it replaces?
There is currently no reliable basis for claiming a precise number of jobs that AI will create versus replace.
The evidence points in several directions at once.
AI is creating demand for specialist capabilities.
AI is changing existing technology jobs.
Some repetitive tasks are becoming automated.
Some entry-level roles are experiencing hiring pressure.
New AI-related activities are expanding.
UK Government projections indicate substantial growth in AI-related employment over the next decade, while other government research highlights pressure in parts of the entry-level labour market.
The most defensible conclusion is therefore:
AI is likely to create many new technology opportunities, but not necessarily in the same roles, at the same career levels or in the same locations as the jobs it changes.
That distinction matters enormously.
What will the UK IT job market look like as AI adoption grows?
The UK IT job market is likely to become more hybrid.
AI will increasingly become part of ordinary technology work.
A job advertisement may not necessarily say “AI Engineer”.
Instead, it may ask for:
- Software engineering + AI
- Cloud + AI
- Cybersecurity + AI
- Data + AI
- Automation + AI
- Business analysis + AI
This means AI may become similar to cloud computing or cybersecurity: a specialist discipline for some professionals but a useful capability for many others.
The most competitive candidates may therefore be those who can connect existing technical expertise with emerging AI capabilities.
How can IT professionals prepare for the AI-driven UK job market?
A practical approach is to follow five steps.
- Understand your current skill set.
Identify which parts of your work are repetitive and which require judgement.
- Identify AI tools relevant to your profession.
Do not learn AI randomly. Focus on your actual career.
- Build practical projects.
Demonstrate what you can create or improve.
- Develop complementary skills.
Add security, cloud, data, automation or business knowledge.
- Keep learning.
AI technology is changing too quickly for a one-time qualification to remain sufficient throughout a career.
What does AI mean for the future of IT careers?
AI is unlikely to produce a simple future where technology jobs disappear.
The more realistic future is one where the definition of an IT job changes.
Developers will increasingly work with AI-assisted development.
Cybersecurity professionals will manage AI-enabled threats and defences.
Cloud engineers will support AI infrastructure.
Data engineers will build systems that feed AI applications.
Business analysts will identify opportunities for intelligent automation.
IT managers will oversee technology transformation.
And new roles will continue to emerge around technologies that are still developing.
The UK Government's research provides an important signal: AI-related employment is expected to expand significantly, but the skills required will extend well beyond specialist AI roles.
For IT professionals, the most useful response is therefore not to ask:
“Will AI take my IT job?”
Ask instead:
“Which parts of my job will AI change, and what new value can I create because of it?”
That question leads to a much more practical career strategy.
AI may replace some tasks.
It may reduce demand for certain types of repetitive work.
It may make some entry-level pathways more difficult.
But it can also create new technology systems, new products, new infrastructure requirements and new specialist roles.
The future UK IT job market will probably contain fewer purely repetitive technology tasks and more work involving AI, systems, security, data, architecture, integration and human judgement.
For professionals willing to adapt, that transformation can represent not only a threat, but a significant career opportunity.
Frequently Asked Questions
Will AI create more IT jobs in the UK?
AI is creating demand for specialist roles and new capabilities across software, data, cloud, cybersecurity and AI infrastructure. However, it is not yet possible to say precisely whether AI will create more IT jobs than it eliminates.
What new IT jobs are being created by AI?
Examples include AI engineers, machine-learning engineers, MLOps engineers, AI security specialists, AI governance professionals, AI product managers and AI-focused data engineers.
Will AI replace software developers?
AI is likely to automate some software development tasks, but developers continue to be needed for architecture, requirements, testing, security, integration and complex software engineering.
Will AI reduce entry-level IT jobs?
Some entry-level IT roles and tasks are under pressure. UK Government research has reported declining entry-level hiring in several occupations, including software engineering, but it has not established AI as the sole cause.
Why will AI create demand for cloud engineers?
AI applications require computing, storage, networking, security, monitoring and scalable infrastructure. Cloud engineers can provide the infrastructure needed to deploy and operate these systems.
Why are data engineers important for AI?
AI depends on reliable data. Data engineers build the pipelines, platforms and infrastructure required to collect, transform, store and deliver data to AI systems.
Will AI create more cybersecurity jobs?
AI can automate some security tasks while creating new security risks. This can increase demand for professionals who understand AI security, threat detection, incident response and risk management.
Do IT professionals need to become AI engineers?
No. Most professionals can benefit from practical AI literacy without becoming AI engineers. The appropriate AI skill level depends on the individual's existing IT specialisation and career goals.
What skills should IT graduates learn for the AI job market?
Graduates should build strong technical foundations in areas such as software, data, cybersecurity or cloud, then add AI literacy, practical projects, problem-solving and communication skills.
Can AI make IT professionals more productive?
Yes. AI can help professionals with research, coding, documentation, analysis, automation and other repetitive tasks. This can allow workers to spend more time on complex technical and strategic responsibilities.
What is the biggest career risk from AI for IT professionals?
A major risk is allowing skills to stagnate while technology and workflows change. IT professionals can reduce this risk by learning how AI affects their particular discipline.
Will AI replace traditional IT skills?
AI is more likely to change how traditional IT skills are applied than eliminate the need for all traditional technology knowledge. Programming, networking, databases, cloud, cybersecurity and infrastructure remain important foundations for AI systems.
How can IT professionals prepare for AI?
They can learn AI tools relevant to their current role, develop automation skills, build practical projects, strengthen complementary technical skills and continuously update their knowledge.
