Future of Work: Emerging Tech Roles and Where to Find the Talent

The future of work is becoming a practical hiring question for UK technology leaders. As teams adopt new tools and take on different responsibilities, employers need to decide which skills they can develop internally, where specialist appointments are needed and what relevant experience looks like. The people capable of doing that work may already be working under another title. A software engineer might be building AI applications. A cloud specialist could be developing an internal platform. A technology finance professional may be helping the business understand its AI costs. Recognising those connections opens up a wider talent market. This guide explores how technology roles are evolving, where employers can find suitable candidates and how to assess people moving into a developing specialism.

What does the future of work mean for technology recruitment?

The future of work describes how technology, economic change and working practices reshape jobs and the skills they require. For technology employers, it affects both the roles they recruit and the experience they should look for. Three changes are particularly useful to distinguish:

• Existing roles gain new responsibilities. Software engineers may use AI tools while retaining responsibility for the quality, security and maintainability of their work.
• Specialisms need dedicated ownership. Platform engineering or technology cost management may become too substantial to remain one part of a broader infrastructure role.
• Work crosses professional boundaries. AI assurance can bring together technical evaluation, risk management and knowledge of the setting in which a system operates.

Each creates a different hiring requirement. Training may be sufficient when an established team adopts a new tool. A specialist appointment becomes more relevant when the business needs expertise that nobody can currently provide. For more on the changes happening within existing occupations, read our analysis of how AI is changing jobs.

What is changing in the UK technology talent market?

Long-term forecasts point to substantial demand for technology capability. Skills England’s 2026 assessment projects 239,000 additional jobs across 30 priority occupations in the digital and technologies sector between 2025 and 2035. That is a forecast for a defined group of occupations, with uncertainty around factors including AI. The immediate recruitment challenge is more specific: finding people who can apply their knowledge to the work an employer needs done. In the DSIT-commissioned AI Labour Market Survey 2025, published in January 2026, 35% of surveyed organisations reported difficulty filling AI roles. Insufficient technical skills and lack of work experience were prominent barriers. Before taking an emerging role to market, establish:

• What the person will deliver and which decisions they will own.
• Which capabilities they need when they join.
• Where comparable experience might already exist.
• What they could realistically learn with the support available.

An employer connecting an AI service to customer systems may need strong software integration experience. One developing machine learning models may need deeper modelling expertise. Advertising both as an “AI engineer” vacancy conceals a significant difference.

Which technology roles are emerging or evolving?

Some roles below are developing specialisms. Others are established professions taking on broader responsibilities. Their relevance depends on the work your organisation needs to deliver. Applied AI engineers and AI integration specialists Introducing an AI application involves connecting it to existing systems, managing data and establishing whether its behaviour is dependable. The recruitment brief should distinguish between integrating existing models, adapting them and developing new ones. Relevant candidates could include:

• Software engineers who have built reliable services and can demonstrate AI application work.
• Integration specialists experienced in APIs, enterprise systems and failure handling.
• Machine learning engineers with delivery experience suited to the application.

Ask candidates how they evaluated an application, what happened when it produced an incorrect result and how they handled that failure. This helps establish whether their experience extends from a working demonstration to a service the business can operate. For the broader business context, explore AI Transformation: A Guide to Strategy, Priorities and Scale. AI evaluation and assurance specialists AI assurance covers different kinds of work, from evaluating system behaviour to coordinating evidence and reviewing controls. The UK government’s third-party AI assurance roadmap identifies skills and professional development as priorities for the emerging market. Be precise about the expertise required:

• Testing and quality engineers may bring useful foundations for evaluation, alongside additional knowledge of AI behaviour and testing methods.
• Data scientists and model validation specialists may suit statistical evaluation and performance analysis.
• Technology risk, audit and data governance professionals may suit assurance coordination and control design, supported by technical specialists.

Someone designing an evaluation methodology needs different expertise from someone coordinating the evidence for a review. Define those responsibilities before combining them in one vacancy. Security requirements should also connect to the organisation’s wider cyber security skills and talent strategy. Platform engineering and developer experience Platform engineering becomes relevant when development teams need consistent, usable ways to build, test and release software. DORA’s guidance treats the platform as an internal product, with developers as its users. That gives employers several potential routes into the candidate market:

• Cloud, DevOps and site reliability engineers with automation and operational experience.
• Software engineers who have built internal tools and understand their support requirements.
• Technical product professionals who can help a larger platform team prioritise developers’ needs.

Look for evidence that the candidate has improved how other teams work. Useful examples include reducing setup time, simplifying releases or enabling developers to complete tasks independently. Data engineering and data product ownership As data moves from periodic reporting into operational applications, reliability and ownership become more consequential. Employers may need people who can improve pipelines, resolve inconsistent definitions or take responsibility for how a data product serves its users. Potential backgrounds include:

• Data and analytics engineers with experience in pipelines, testing and maintainability.
• Business intelligence developers who have progressed into data modelling and engineering.
• Product managers and experienced analysts who can establish users, priorities and ownership.

These capabilities may sit across several appointments. A data product owner does not automatically have the skills to engineer the underlying platform, while an engineer may need a business counterpart to resolve competing requirements. Show candidates where responsibility currently breaks down. It makes the expected contribution much clearer than a broad request to “improve our data”. FinOps and technology cost specialists Technology cost management increasingly involves understanding consumption, architecture and business value together. The FinOps Foundation’s 2026 global survey identifies AI value management as the leading skillset respondents want to add. Depending on the brief, relevant candidates may come from:

• Cloud engineering, with financial analysis skills to develop.
• Technology finance or IT financial management, with technical depth to establish.
• Existing FinOps roles, where experienced ownership is needed immediately.

A useful assessment asks candidates to explain a cost increase, identify its operational causes and recommend a response that preserves the service’s purpose. This tests whether they can influence spending decisions as well as report them. Automation, business analysis and service design Automating a process changes the work around it. Exceptions still need handling, teams need clear responsibilities and users need a service that works from beginning to end. Relevant experience can come from:

• Business analysts who understand processes across teams and systems.
• Automation and integration developers familiar with system boundaries and failure handling.
• Service designers and operational specialists who have redesigned work and measured the result.

Ask candidates to review a process with frequent exceptions. Their choices about what to automate, where to retain human review and how to measure improvement will reveal more than familiarity with a particular product.

Where can employers find talent for emerging roles?

A search built around relevant work can reach candidates whose job titles would otherwise exclude them. Start with the environments in which people are likely to have developed the experience you need. Look across adjacent professions For an AI integration vacancy, useful experience may sit in software teams connecting complex business systems. For assurance coordination, it may be found in organisations with established technology risk or model governance practices. Explore:

• Comparable project teams, specialist suppliers and consultancies.
• Practitioner networks, professional communities and technical events.
• Candidates whose responsibilities have expanded without a change in title.
• Internal employees with relevant experience outside their current remit.

Public portfolios can help reveal expertise, but they should not be a requirement. People working on confidential systems need an equivalent way to explain their contribution without disclosing protected information. Set location requirements around the work A UK-wide search only broadens the talent pool if the working arrangements make it practical. Frequent attendance at one office can narrow the search considerably, even when the vacancy is advertised as hybrid. Before setting the search area, establish:

• Where comparable employers and specialist teams are based.
• Whether relevant research groups or industry partnerships offer useful connections.
• Which activities genuinely require attendance on site.
• What travel or relocation the role involves.

State those expectations early. Candidates should be able to judge whether the opportunity works for them before committing to the recruitment process. Create internal routes into new specialisms An internal candidate may combine relevant technical foundations with knowledge of the organisation’s systems, customers and working practices. That can be valuable when the new specialism sits close to work they already perform. A credible transition needs:

• A defined project through which they can demonstrate the capability.
• Protected learning time and access to experienced review.
• Clear limits on what they can own independently.
• Recognition and progression as their responsibilities develop.

A course can support that transition, but the employee also needs opportunities to apply what they learn. If the underlying capability requirement still needs defining, Growth Starts With Capability provides a useful starting point. Build experience into entry routes Graduate recruitment, apprenticeships and career transitions can support a longer-term talent pipeline. Their effectiveness depends on the work and supervision available after appointment. Define the contribution a less experienced person can make, who will review it and what evidence will demonstrate readiness for greater responsibility. As routine tasks are automated, employers need to consider how junior colleagues will gain the experience those tasks once provided.

How do you assess candidates for a role that is still evolving?

Different career backgrounds become easier to compare when the assessment is tied to the actual work. Separate the foundations required on appointment from specialist knowledge that can realistically be developed. Use a consistent process:

1. Examine comparable experience. Explore a project with relevant complexity or consequences. Establish what the candidate personally contributed and why they made particular decisions.
2. Use a representative problem. Set a short scenario or work sample based on the role. Explain whether AI tools are allowed and what the candidate must demonstrate independently.
3. Explore the learning requirement. Identify unfamiliar areas and the support the candidate would need.
4. Score against the same criteria. Use agreed outcomes and equivalent ways to demonstrate experience, with reasonable adjustments where needed.

For an AI evaluation role, a sample of system outputs containing plausible errors could prompt a useful discussion. How would the candidate categorise failures, decide what needs escalation and improve the evaluation? The appointment decision must also reflect the employer’s capacity to support development. Adjacent experience can be a strong foundation when the remaining gap is manageable. A role with sole responsibility and limited supervision requires greater existing expertise.

What makes an emerging role attractive to candidates?

An unfamiliar title can leave candidates uncertain about the scope, support and career prospects of a position. A clear brief helps them understand both the opportunity and the expectations. Explain:

• The work: what they will deliver and which decisions they will own.
• The support: where specialist review and complementary expertise are available.
• The development: what they are expected to learn and how time will be provided.
• The terms: the pay range, working pattern and location requirements.
• The progression: how growing capability can lead to greater responsibility.

Benchmark pay against the substance of the position. Two employers can use the same emerging title for very different levels of accountability. Where the requirement sits at board or executive level, our guide to Technology Executive Search for the Modern Boardroom explores the considerations for a senior appointment.

Frequently asked questions

Which technology roles should employers watch? Applied AI integration and AI assurance are developing areas to consider, alongside evolving responsibilities in platform engineering, data, FinOps and automation. Prioritise the capabilities relevant to your business rather than treating these roles as a universal hiring list. Can people move into emerging roles from other technology jobs? Yes, where their existing experience provides relevant foundations and the remaining development needs are manageable. Software engineering, cloud, testing, data, business analysis and technology finance can all provide routes into particular specialisms. Assess individual evidence alongside the support available after appointment. Where should UK employers start looking for emerging technology talent? Start with adjacent professions, comparable project environments and internal employees. Then map relevant employers, practitioner communities and research or training partnerships across suitable locations. Clear attendance requirements will help ensure the practical candidate pool matches the advertised opportunity.

Find the experience behind the emerging role

The language of technology recruitment will continue to change. Understanding the work behind a title gives employers a firmer basis for deciding who to hire, where to look and what development they can support. TRIA helps UK organisations recruit technology specialists and build teams. If your next appointment involves an emerging role, we can help define the experience you need and identify where to find it. ➤ [[BANNER: PLANNING YOUR NEXT TECHNOLOGY HIRE? Talk to TRIA about the talent you need.]]