Which Tech Jobs Will Be Most Impacted by AI Development?

​Artificial intelligence is no longer a future consideration for the UK technology market. It is already changing how software is written, data is analysed, security threats are identified and digital products are developed.

The question for technology professionals is therefore becoming less “Will AI affect my job?” and more “Which parts of my job will AI change?”

According to the Office for National Statistics, the proportion of UK businesses with 10 or more employees reporting that they use at least one form of artificial intelligence increased from approximately 12% in late 2023 to around 35% by 2026.

At the same time, this does not necessarily mean widespread replacement of technology professionals. Government analysis published in 2026 stresses that AI exposure should not automatically be interpreted as redundancy: in many occupations, artificial intelligence is more likely to automate individual tasks while changing the skills required of the person performing the wider role.

For technology candidates across London, Birmingham, Manchester, Leeds, Leicester, Bristol, Sheffield and the wider UK, understanding that distinction could become increasingly important when planning the next stage of a career.

AI Is Transforming Technology Jobs - Not Simply Removing Them

Technology roles are in an unusual position.

The sector is responsible for developing many of the systems creating automation in the first place, but technology professionals are simultaneously some of the employees most capable of using those systems to increase their own productivity.

Generative AI coding assistants, large language models (LLMs), automated testing platforms, AIOps solutions, machine-learning security tools and autonomous agents can now perform activities that previously required significant manual input.

However, UK demand for sophisticated digital capability is expected to remain substantial.

Skills England's assessment of priority skills to 2030 identified programmers and software development professionals as requiring approximately 87,000 additional workers between 2025 and 2030, placing the occupation among the areas with the greatest additional employment demand.

Meanwhile, UK government projections suggest jobs involving core AI activities could account for approximately 12% of the workforce - around 3.9 million people - by 2035, with approximately 9.7 million people potentially working in roles where AI is at least adjacent to their responsibilities.

The future of technology recruitment therefore looks more like skills redistribution than straightforward job elimination.

1. Junior Software Developers and Entry-Level Programmers

Software development is one of the most obvious areas being changed by generative AI.

Modern AI-assisted development tools can already:

• generate boilerplate code;

• suggest functions and algorithms;

• refactor existing code;

• produce documentation;

• identify basic bugs;

• translate code between programming languages;

• generate unit tests; and

• explain unfamiliar codebases.

This creates particular implications for junior software developers, because many of the repetitive development activities traditionally assigned to early-career engineers are becoming easier to automate.

Employers may consequently expect junior developers to become productive more quickly.

That does not mean junior software engineering jobs will disappear.

Software development requires architecture decisions, requirements interpretation, debugging, testing, security awareness, stakeholder communication and an understanding of how individual services interact within complex systems.

AI-generated code also requires human verification.

Instead, candidates could increasingly differentiate themselves through skills including:

Python | Java | C# | JavaScript | TypeScript | APIs | Git | CI/CD | Cloud Platforms | AI-Assisted Development | System Design

For growing technology ecosystems across Manchester, Birmingham, Leeds and London, the junior developer of the future may increasingly operate as an AI-augmented software engineer, rather than somebody writing every line of code manually.

2. Software Testing and QA Roles

Quality Assurance is another area likely to experience significant automation.

AI-enabled testing platforms can assist with test generation, regression testing, defect detection and test-case prioritisation.

Routine manual QA processes could therefore account for a smaller proportion of some testing positions.

Candidates specialising entirely in repetitive manual testing may experience greater pressure than professionals capable of working across areas such as:

• test automation;

• Selenium;

• Cypress;

• Playwright;

• API testing;

• performance testing;

• CI/CD;

• security testing; and

• automated quality engineering.

Rather than eliminating testing professionals, AI could accelerate the long-running transition from manual QA towards quality engineering and automated testing.

Testers who understand both software development lifecycles and AI-generated software validation could therefore remain particularly valuable.

3. Data Analysts

Data analysis is highly exposed to generative AI because modern models can increasingly manipulate datasets, create SQL queries, identify patterns, generate visualisations and summarise findings.

Activities that previously required a data analyst to spend several hours manipulating spreadsheets or producing straightforward reports can increasingly be accelerated using AI.

Entry-level reporting roles may therefore evolve.

However, businesses still need professionals capable of deciding:

What data should we analyse?

Is the underlying dataset reliable?

Does correlation actually indicate a commercially meaningful relationship?

Can this model be trusted?

How should the organisation act on the results?

Data professionals combining technical capabilities such as SQL, Python, Power BI, Tableau and cloud data platforms with commercial interpretation are consequently likely to remain valuable.

The higher-value skill is increasingly moving away from simply producing data towards understanding, validating and communicating it.

This distinction will be important for candidates within financial, technology and professional-services centres such as London, Birmingham, Bristol and Leeds.

4. Data Scientists and Machine Learning Engineers

Unlike some jobs affected by AI automation, demand for specialists who actually build AI infrastructure could increase.

The World Economic Forum identifies AI and Machine Learning Specialists and Big Data Specialists among the fastest-growing technology occupations globally towards 2030.

UK employers are simultaneously experiencing significant difficulty securing the necessary expertise.

The Department for Science, Innovation and Technology's 2025 AI Labour Market Survey found that 97% of surveyed respondents identified at least one AI labour-market skills gap, while 57% reported a technical AI skills gap.

Skills likely to command increasing attention include:

• machine learning;

• deep learning;

• Python;

• PyTorch;

• TensorFlow;

• natural language processing;

• computer vision;

• MLOps;

• model evaluation;

• retrieval-augmented generation (RAG);

• vector databases;

• LLM deployment;

• AI governance; and

• cloud AI infrastructure.

Technology hubs including London, Manchester, Cambridge, Bristol, Birmingham and Edinburgh could consequently continue to generate opportunities for candidates capable of moving beyond basic generative-AI usage into production-grade AI engineering.

5. Cybersecurity Professionals

AI creates an unusual situation within cybersecurity because it increases both automation capability and the threat surface organisations must defend.

Security teams can use machine learning and AI for:

• anomaly detection;

• threat intelligence;

• malware analysis;

• vulnerability prioritisation;

• incident investigation;

• security monitoring; and

• automated response.

At the same time, cybercriminals can use generative AI to increase the sophistication or scale of phishing, social engineering and other attacks.

This makes complete replacement of cybersecurity professionals unlikely.

Instead, cyber roles are likely to become increasingly AI-enabled.

UK government research into the cybersecurity labour market found that 53% of cybersecurity businesses were already using AI in their day-to-day operations, while 65% expected demand for AI skills to increase during the following 12 months.

Future cybersecurity vacancies could therefore increasingly request combinations such as:

SIEM + AI

SOC Operations + Automation

Threat Intelligence + Machine Learning

Cloud Security + AI Governance

Application Security + Secure AI Development

Candidates across London, Manchester, Birmingham, Leeds and other major UK cyber markets may benefit from understanding both how AI can strengthen security operations and how new AI systems introduce additional vulnerabilities.

6. IT Support and Service Desk Roles

First-line IT support is another area where AI-driven automation is likely to have a significant operational impact.

AI-powered service-management platforms can increasingly resolve common issues involving:

• password resets;

• software troubleshooting;

• account permissions;

• basic configuration;

• knowledge-base queries; and

• ticket classification.

This could reduce the volume of repetitive Tier 1 support work.

However, escalated technical issues, infrastructure failures, security incidents and complex user problems continue to require human expertise.

Career progression from traditional helpdesk positions towards:

2nd Line Support → Infrastructure → Cloud → Cybersecurity → DevOps

could therefore become even more important.

Candidates currently working within IT support should consider developing experience with Azure, AWS, Microsoft 365, PowerShell, networking, endpoint management and cybersecurity, rather than relying exclusively on general service-desk knowledge.

7. DevOps and Cloud Engineers

DevOps engineers will certainly be affected by AI, but this could mean greater productivity rather than declining demand.

AI can assist with configuration generation, infrastructure monitoring, log analysis, deployment troubleshooting and automated remediation.

However, modern cloud environments are complex.

Organisations still need engineers who understand:

• AWS, Azure or Google Cloud;

• Kubernetes;

• Docker;

• Terraform;

• Infrastructure as Code;

• CI/CD pipelines;

• observability;

• networking;

• cloud security;

• site reliability engineering; and

• cost optimisation.

The increasing use of AI workloads may actually create additional infrastructure challenges.

Large-scale models require compute capacity, data pipelines, inference infrastructure, monitoring, security controls and resilient cloud architecture.

The DevOps engineer may therefore increasingly evolve towards an AI infrastructure or platform engineering role.

8. UX/UI and Digital Product Roles

Generative AI tools can now create wireframes, produce design concepts, generate copy and accelerate prototype development.

As a result, some lower-complexity production work within digital design could become increasingly automated.

However, successful product development involves far more than generating an interface.

Human professionals remain responsible for understanding:

• user behaviour;

• accessibility;

• customer journeys;

• product strategy;

• stakeholder requirements;

• experimentation;

• commercial priorities; and

• usability.

AI may consequently reduce the amount of time designers spend producing individual assets while increasing expectations around product thinking, research and strategic decision-making.

9. AI Governance, Safety and Responsible AI Roles

Some of the most important technology jobs of the next decade may barely have existed at scale several years ago.

As organisations deploy AI into customer-facing and operational environments, they need professionals capable of controlling how those systems behave.

This is increasing interest in disciplines including:

• AI governance;

• AI assurance;

• model risk;

• responsible AI;

• algorithmic auditing;

• AI security;

• data privacy;

• AI compliance;

• model evaluation; and

• human oversight.

These roles sit between technology, risk, data governance and regulatory compliance.

For candidates, this means an AI career does not necessarily require becoming a machine-learning engineer.

Professionals with backgrounds in cybersecurity, data protection, software assurance, risk or governance may also find emerging routes into the AI economy.

Why Entry-Level Tech Jobs Could Experience the Biggest Change

One of the most important issues for the UK technology sector concerns how people begin their careers.

Junior professionals have historically learned through relatively straightforward tasks before progressing towards more complex responsibilities.

But those straightforward tasks are precisely the activities AI can often perform effectively.

Employers therefore need to be careful not to automate the training ground their future senior workforce depends upon.

The World Economic Forum's 2026 research into entry-level work similarly describes AI as both a productivity opportunity and a potential disruption to traditional early-career pathways.

Technology employers may need to redesign graduate and junior roles rather than simply eliminating them.

That could mean exposing early-career employees earlier to architecture, client requirements, AI validation, product decisions and complex troubleshooting.

Which Technology Skills Are Likely to Become More Valuable?

The strongest candidates in an AI-driven technology market are unlikely to compete against AI.

They will know how to work with it.

Some of the most valuable areas for technology professionals to develop include:

AI Literacy

Candidates do not necessarily need to become AI engineers, but understanding LLMs, model limitations, prompting, hallucination, context windows and AI-assisted workflows is becoming increasingly relevant.

Cloud Computing

AI applications require scalable infrastructure, making AWS, Microsoft Azure and Google Cloud Platform expertise increasingly valuable.

Data Engineering

AI is only as useful as the data supporting it.

Skills involving pipelines, data architecture, ETL/ELT, SQL, Spark and cloud data platforms are therefore strategically important.

Cybersecurity

More AI adoption creates new security requirements around models, APIs, training data and sensitive information.

System Architecture

As AI generates more individual components, professionals capable of understanding how the entire system should operate may become increasingly valuable.

Critical Thinking

Developers must be capable of recognising when AI-generated code, recommendations or analysis are wrong.

That requires technical judgement rather than unquestioning reliance on automation.

AI's Impact on Tech Jobs Will Differ Across the UK

The effects of artificial intelligence will not necessarily be distributed evenly.

London is particularly exposed because of its concentration of knowledge-intensive professional employment.

Research published by the Greater London Authority estimates that at least 46% of London's workers - around 2.4 million people - are employed in occupations where generative AI could automate some tasks, compared with a UK average of 38%. Importantly, the report specifically warns that this represents task exposure rather than predicted job losses.

Other major technology ecosystems will also experience substantial change.

Manchester continues to support strong digital, cloud, fintech and cybersecurity markets.

Birmingham and the wider West Midlands combine established professional-services employers with a growing digital economy.

Leeds has considerable demand across fintech, data and digital transformation.

Bristol remains important for technology, engineering and cybersecurity talent.

Meanwhile, Sheffield and Leicester all contribute specialist technology talent to an increasingly distributed UK digital workforce.

The precise technologies employers adopt will differ, but AI capability is likely to become increasingly relevant across almost every regional technology market.

Will AI Take Technology Jobs?

Some technology jobs will undoubtedly change.

Certain tasks may disappear altogether.

Some employers may require fewer people to perform highly repetitive work.

But simultaneously, new jobs are emerging and established technical occupations are becoming more sophisticated.

The World Economic Forum estimates that global labour-market transformation could create 170 million jobs and displace 92 million by 2030, resulting in a net increase of 78 million roles across the economy. Technology-related capabilities including AI, big data and cybersecurity are among the fastest-growing skill areas.

For UK technology professionals, therefore, the more useful question may not be:

“Will AI replace my job?”

It may be:

“Which parts of my role can AI perform - and what higher-value skills can I develop that it cannot?”

The candidates best positioned for the next stage of the UK technology market will be those capable of combining strong technical fundamentals with AI literacy, commercial understanding, critical thinking and continuous professional development.

Looking for Your Next Technology Opportunity?

The technology jobs market is evolving quickly, and understanding where your skills fit within that market is becoming increasingly important.

Whether you specialise in software development, data, cybersecurity, cloud infrastructure, DevOps, IT support, testing or emerging AI technologies, working with a specialist technology recruitment agency can help you understand where demand is developing and which technical skills employers are prioritising.

Speak to our specialist technology recruitment team to discuss current opportunities across the UK and find out where your experience could take you next.