A business identifies a gap in its data and AI capabilities. The response seems straightforward: recruit people with the skills it needs. A few months later, new specialists have joined the team, but employees elsewhere in the organisation are still struggling to use data effectively or apply AI to their work.

The problem may not be a lack of specialist talent. It may be that the organisation needs to develop a wider range of skills across its existing workforce.

Recruitment has an important role to play in closing skills gaps, particularly when specialist knowledge is needed. However, hiring alone won't necessarily give an organisation all the capabilities it needs to make effective use of data and AI.

Why isn't hiring enough to close a skills gap?

Recruiting experienced data and AI professionals can help organisations bring in specialist knowledge, introduce new approaches and develop their technical capabilities. However, there are several reasons why recruitment alone may not be enough.

Competition for specialist skills

Demand for digital and AI-related capabilities is creating challenges for employers looking to recruit.

Skills England's Annual Skills Report 2026, published in October 2026, found that more than a quarter of UK job vacancies are difficult to fill because of skills shortages. Its analysis also identifies digital roles as a significant area of expected employment growth over the coming decade.

The government's AI Labour Market Survey 2025 also highlights difficulties recruiting experienced AI professionals, particularly in areas such as data architecture and strategy.

For employers, competition for suitable candidates can make recruitment time-consuming and expensive. Even when a vacancy is filled, the organisation may still need to develop other employees' skills to make the most of the new capability.

The challenge of retaining talent

Recruitment is only part of the picture. Employers also need to consider how they will retain the people they bring in.

Experienced specialists may have opportunities elsewhere, particularly in areas where demand for their skills is high. If they don't see opportunities to develop, progress or take on new responsibilities, an organisation could struggle to retain them.

The gap between technical knowledge and business context

An external hire may bring strong technical skills, but they will still need time to understand the organisation's systems, processes, customers and priorities.

Existing employees already have much of this knowledge. With the right development, they may be able to apply new data and AI skills directly to the work they do.

There is also a question of scale. A small team of specialists can provide valuable technical support, but it can't necessarily develop data literacy across an entire workforce or make sure employees in different departments understand how to use AI appropriately.

What kind of data and AI skills gap does your business have?

Before deciding whether to hire or upskill, leaders need to understand what their organisation is missing.

Data and AI capability can be considered across three broad levels.

1. Data and AI literacy

These are the skills employees need to work confidently with data and AI in their everyday roles. They include interpreting information, questioning results, understanding the limitations of AI-generated outputs and using tools responsibly.

Not everyone needs to become a data analyst or AI specialist. However, employees may need a basic understanding of how these technologies can support their work and where care is required.

2. Applied skills

Some employees need to go further by applying data and AI tools to specific tasks, processes or business challenges.

This might include analysing performance information, automating routine activities, developing effective AI prompts or using data to support decisions within a particular team.

These skills need to be relevant to employees' roles and the work they are expected to carry out.

3. Specialist technical skills

Organisations may also need people with deeper technical knowledge, including data engineering, machine learning, AI architecture and governance.

These capabilities can require substantial technical training and experience. Recruitment may be appropriate where the organisation lacks the necessary knowledge or needs to establish a new technical function.

The distinction matters because organisations can end up recruiting specialists when their wider challenge is a lack of basic or applied skills. Equally, providing general AI training won't fill a vacancy that requires advanced technical knowledge.

What are the benefits of upskilling existing employees?

Once an organisation understands its capability gaps, developing its existing workforce can offer several advantages.

Building on existing business knowledge

Employees already understand their roles, colleagues and the processes they work with. Developing their data and AI skills can help them apply new knowledge to familiar challenges.

For example, a finance team member who develops their data analysis skills may be better placed to identify patterns in reporting or investigate changes in performance. Someone who understands an existing process may also be well placed to identify where AI could support particular tasks.

This doesn't mean existing employees will always be able to develop the skills required. However, it gives employers another route to consider before assuming that every gap needs to be filled through recruitment.

Supporting progression and retention

Upskilling can also give employees opportunities to develop within the organisation.

The CIPD's Resourcing and Talent Planning Report 2026, published in September, found that 48% of UK employers had increased their efforts to develop talent in-house over the previous year. The report also found that 45% of employers that had introduced initiatives to improve retention had increased learning and development opportunities.

Providing relevant learning won't guarantee that employees stay, but it can help them develop towards new responsibilities and understand how their skills could support their future careers.

Making learning investment relevant to business needs

Upskilling can also help organisations connect learning with practical business priorities.

Rather than developing skills in isolation, employers can identify the capabilities needed to support a particular objective and give employees opportunities to apply what they learn.

For example, if a business wants to improve its use of data in decision-making, it could develop analytical skills within the teams responsible for reporting and performance. If it plans to introduce AI into existing processes, it could help relevant employees understand how to use the tools and evaluate their outputs.

Apprenticeships and other structured development programmes can provide a way to build these capabilities over time. Depending on the programme and eligibility requirements, apprenticeships in England may also be supported through government funding arrangements, including the Growth and Skills Levy.

Employers should check the current funding rules and available training options before deciding how to fund development.

Is upskilling cheaper than hiring?

There isn't a single answer. The cost will depend on the skills required, the number of employees involved, the learning programme and the time available.

Recruitment costs can include advertising, agency fees, salary premiums and the time managers spend interviewing candidates. There may also be a period before a new employee is fully effective in their role.

Upskilling has its own costs. These can include training fees, employees' time away from other responsibilities and the support needed to apply new skills in the workplace.

Consider a hypothetical business that needs to improve how it uses data across several departments. It could recruit a specialist analyst to provide that capability, or it could develop the analytical skills of employees in teams that already work with relevant information.

The first option could provide deeper technical knowledge more quickly, but one new recruit may not have the capacity to support every team. The second could help more employees use data in their roles, but it may take time and wouldn't necessarily provide the specialist knowledge required.

These options aren't directly interchangeable. The business needs to consider the capability it requires, the time available and the likely impact of each approach.

The same principle applies to AI. Training a group of employees to use existing tools effectively may be a sensible investment, while developing an advanced AI system may require specialist technical skills that the organisation doesn't have internally.

When should you hire rather than upskill?

There will be situations where recruitment is the right choice.

If an organisation needs a specialist capability immediately, hiring someone with the right experience may be more appropriate than developing that knowledge internally. This could apply when establishing a data engineering function, introducing a complex machine learning system or addressing a specific AI governance requirement.

Recruitment can also bring an external perspective. Someone joining from another organisation may challenge established ways of working, introduce different approaches and help teams identify opportunities they hadn't previously considered.

Upskilling may be less practical when there is little existing knowledge to build on, when employees don't have the capacity to undertake the necessary development, or when the required skill is highly specialised.

In these situations, hiring or working with an external partner can help an organisation access the capability it needs.

What should you ask before your next data or AI hire?

Before approving a vacancy, business leaders can use a few questions to test whether recruitment is the right response.

• What is the specific skills gap? Define the capability that's missing and the business outcome it affects.
• Could existing employees develop the skills? Consider whether relevant employees could reach the required level within six to 12 months, and what support they would need.
• How will the new hire contribute to wider capability? Consider whether they will work independently, support other teams or help develop internal knowledge.
• How will you retain the person? Think about career development, opportunities to progress and how the role could evolve.
• What is the right mix of approaches? Consider whether recruitment, upskilling and external support could work together more effectively than relying on one option.

Closing the data and AI skills gap requires a balanced approach

Hiring remains an important part of workforce planning, particularly when organisations need specialist knowledge or new perspectives. But it isn't the only way to develop capability.

For many businesses, the challenge is to combine specialist recruitment with opportunities for existing employees to develop their skills. That means understanding where gaps exist, identifying which capabilities can be developed internally and connecting learning with the organisation's wider priorities.

Explore Kaplan's data and technology courses and apprenticeships for employers to find out more about developing skills within your organisation.