Artificial intelligence is reshaping how organisations operate, but the reality behind the AI conversation is more nuanced than the headlines suggest. Many businesses are racing to be seen as “AI organisations”, often before they have a fully formed AI strategy in place. What they do know, however, is that AI capability has become a differentiator and data professionals sit at the centre of making it work.
According to the Nash Squared/Harvey Nash Digital Leadership Report, 65% of digital leaders are increasingly prioritising AI capability over years of experience when hiring. In practice, this doesn’t mean every data role is suddenly an AI role, but candidates who can demonstrate real experience working with AI, even alongside core data skills, are moving through hiring processes faster.
“If you’ve got AI skills on top of your data experience, you’ll get interviews quicker. Time to hire is definitely shorter.”
Daniel Neaves, AI & Data Recruitment Specialist
The opportunity for data professionals
The Nash Squared/Harvey Nash Digital Leadership Report found that demand for AI skills has created the sharpest technology skills shortage seen in 16 years, while demand for data skills remains consistently high. Big data, analytics, and data engineering sit just behind AI as the most in-demand capabilities across the technology sector.
In fact, the report also suggests that organisations most advanced in large-scale AI adoption are more likely to be increasing technology headcount, particularly across AI and data roles. AI is not replacing data talent. Instead, it’s increasing its importance.
However, market conditions are creating an unusual dynamic. While demand for data professionals remains strong, fewer people are moving roles. Economic uncertainty, salary stagnation and risk aversion are leading many data professionals to stay put.
This reluctance to move is quietly reducing competition. And with fewer candidates per role, faster recruitment processes and a greater willingness from employers to engage candidates who already show AI experience, there is a real opportunity for those willing to make a move, either into an AI-adjacent role, a hybrid position, or a role that expands their current responsibilities.
As Daniel Neaves, AI & Data Recruitment Specialist, explains, “I’m seeing far fewer applications per role than last year. People are staying put, which actually gives strong candidates a real advantage.”
What employers are really looking for
Employers are no longer impressed by theoretical knowledge of AI alone. What matters is applied capability, the ability to use and work with AI tools within real data environments to deliver outcomes.
Crucially, expectations differ by role.
Data engineers
For data engineers, AI is not about building models. It is about enabling them.
Organisations are looking for engineers who can build robust, scalable data foundations, clean pipelines, reliable infrastructure and well-structured data environments. Without this, AI initiatives fail to get off the ground.
“Every AI role is ultimately bound by the state of the data. If the data isn’t mature, the AI won’t work, no matter how good the model is.”
Daniel Neaves, AI & Data Recruitment Specialist
GenAI tools increasingly support engineers through automation, code assistance and documentation, but getting the data right remains the core enabler.
Data scientists
While data scientists have long worked with machine learning and automation, expectations are changing. The focus is shifting from technical model building alone to applying AI responsibly and translating results into measurable business impact.
This includes deeper profiling, more sophisticated modelling, and greater focus on how outputs influence revenue, efficiency or customer behaviour.
“Companies aren’t interested in data scientists who just enjoy building models anymore. They want outcomes, revenue impact, ROI and business change.” Daniel Neaves, AI & Data Recruitment Specialist
Governance, bias and explainability matter most in regulated or mature environments, but outcome-led thinking now cuts across every sector.
Data analysts
AI expectations for analysts are more pragmatic. Most employers want strong core skills, SQL, Python, data preparation, combined with the ability to use AI-enabled tools to automate reporting, accelerate insight and prepare data for downstream use.
“Analysts don’t necessarily need deep machine learning skills. They need to organise, structure and prepare data so it’s ready for the next step.” Daniel Neaves, AI & Data Recruitment Specialist
For many, analyst roles remain a stepping stone into more advanced data or AI-focused positions.
Why data quality matters
Across every data role, the success of an AI project ultimately depends on data quality. Whether enabling models, generating insight or driving business decisions, AI can only perform as well as the data it is built on.
Organisations that struggle to scale AI are rarely held back by a lack of tools or ambition. More often, the constraint is fragmented data, weak governance or poorly defined data foundations that limit how effectively AI can be applied in real-world environments.
Strong architecture and governance provide the framework, but it is the everyday work of data teams, designing pipelines, maintaining standards and ensuring consistency, that determines whether AI delivers meaningful outcomes
“If the information isn’t captured properly, filtered properly or detailed enough, you simply can’t generate value from it, AI or not.” Daniel Neaves, AI & Data Recruitment Specialist
This reinforces a clear message for data professionals. As AI becomes embedded across roles, foundational data skills are not being replaced. They are becoming even more important.
Why AI skills get you hired faster
AI skills do not automatically command a salary premium, but they do significantly improve employability.
Candidates with AI exposure:
- Move through hiring processes faster
- Generate more recruiter and client interest
- Secure interviews more consistently
If you’re eyeing your next data role, AI experience can give you the edge that gets you noticed.
Salary expectations (UK, London-based)
While salaries have stabilised, demand remains strong across core data roles:
- Data analysts: £50k–£65k
- Data engineers: £75k–£90k (lead/principal £100k+)
- Data scientists: £75k–£90k (lead/principal £105k–£120k)
- Data architects: £80k–£100k
- Head of Data: £120k–£160k
* Salary ranges are based on London market rates.
Despite increased skill requirements, many organisations are asking more from candidates without necessarily increasing pay. In fact, anyone looking to change roles should view AI skills and experience as a key to securing their short and long-term employability.
Where AI meets your next data role
The message from both the Digital Leadership Report and the market is clear. Data jobs remain in high demand, but the fastest-moving candidates are those who combine strong data foundations with applied AI experience.
With fewer professionals willing to change roles, those prepared to step up into hybrid roles that combine data and applied AI responsibilities, AI-adjacent projects or broader responsibilities face less competition and more opportunity.
AI skills may not rewrite salary bands overnight. But they are already reshaping who gets hired, how quickly, and who gets the chance to progress next.
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