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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchThere is no universal winner. Upskill current employees when the AI skills gap is practical, tied to work they already know, and teachable before the capability is needed. Hire or contract specialists when the work calls for advanced expertise or experience your team cannot build in time. Many teams will need both: broad internal AI literacy and responsible-use practices, plus specialist help for scarce technical needs.
Choose by mapping the work and the skills it requires—not by assuming training is always cheaper or that a new hire can solve every AI problem.
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What AI skills does your team actually need?
AI capability is broader than building models or writing code. A UK review groups AI skills into three connected areas: technical skills, responsible and ethical use, and non-technical skills. The right mix depends on the role, task, and organizational context. The British Academy and Skills England review also points to data and governance issues as part of the capability picture.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsStart with the work rather than job titles. List the tasks where AI is intended to help, the decisions employees will make, and the consequences of an error. Then identify the skills needed to do each task safely and well. For example, using an AI assistant to draft routine communications calls for different skills than building or deploying a model; both still require people to know when not to rely on an AI output.
- Technical: the level of AI, data, or engineering knowledge needed for the task.
- Responsible and ethical: safe use, appropriate oversight, and awareness of risks such as bias and data protection.
- Non-technical: the role-specific judgment and work practices needed to use AI appropriately.
Use the Skills England overview as a starting point for assessing the workforce, with the qualification that these are UK employer resources rather than a universal skills prescription.
When should you upskill current employees?
Upskilling is a strong option when the gap is teachable through practice in familiar work: foundational AI or data fluency, responsible-use habits, or the practical use of tools in existing tasks. Employees who understand the process, customers, and consequences can apply new skills in context. That is a practical inference from the emphasis on task-linked learning, not a measured guarantee that training will outperform hiring.
Training is more likely to be useful when employees have the time and support to practice, receive feedback, and apply what they learn. The UK Skills for AI (SKAI) programme, drawing on 23 workshops, 10 case studies, and 536 employer survey responses, says effective training should be practical and task-based, accessible, integrated into work and governance, modular, scalable, and kept current. More than 44% of organizations surveyed for the programme reported using AI tools daily, underscoring why training needs to address real workflows rather than remain detached from work. These figures describe UK programme evidence, not all employers. The SKAI executive summary puts the principle this way: “Good AI training must be practical and task based. It must build technical, non-technical and responsible AI skills together. It must also help staff know when AI should, and should not, be used.”
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Informal experimentation, peer support, online videos, and built-in prompts can help employees get started, but relying on them alone can lead to uneven or risky practices. Pair hands-on use with task-specific guidance, time to learn, oversight, and refreshers as tools and workflows change.
When should you hire or contract specialists?
Bring in external expertise when a role requires advanced technical depth, prior deployment experience, or specialist accountability that current employees cannot develop before the work is due. This is especially relevant if the team lacks capacity to build the capability internally or needs help with architecture, data foundations, or governance. If you contract a specialist, make knowledge transfer to internal staff part of the engagement so the expertise does not remain isolated.
Specialist hiring can itself be difficult. In the UK AI Labour Market Survey 2025, 35% of surveyed organizations said they struggled to fill AI roles. The survey identified candidates lacking work experience (31%) and insufficient technical skills (30%) as recruitment barriers. These findings apply to the surveyed UK organizations and roles; they are not a measure of hiring difficulty in every labor market. Read the UK survey’s executive summary.
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Compare the options against your actual constraints
| Decision factor | Questions to ask |
|---|---|
| Capability fit | Is the gap AI literacy, responsible use, data fluency, model engineering, deployment, or another specialist skill? |
| Urgency | When must the capability be working, and can employees realistically learn and apply it by then? |
| Scale | Does the need affect many roles, or is it concentrated in a small number of specialist positions? |
| Time and capacity | Can employees make room for training, practice, and feedback without undermining their current responsibilities? UK employers reported time and capacity barriers in the SKAI evidence. |
| Hiring constraints | Is experienced talent available in the relevant labor market? The UK survey results above should not be generalized to other countries. |
| Responsible use | Who owns data protection, oversight, bias, and safe-use expectations, and how will staff learn them? |
| Durability | Will the skill be used often enough to retain, and how will learning change as tools and work practices evolve? |
| Cost and evidence | Compare local training and hiring costs directly. The sources do not establish a universal cost advantage, ROI, or break-even point for either route. |
Why training and hiring are not an either-or choice
Many organizations need broad, shared competence as well as a smaller amount of specialist depth. Internal training can help employees across roles understand appropriate use and apply AI in their work; specialists can address requirements such as engineering, deployment, or governance that call for deeper experience. A blended approach is a decision option, not a promise of a particular cost or performance outcome.
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There is evidence that training is associated with positive reported outcomes, but it does not prove a causal return on investment. The OECD summarizes evidence that workers who use AI and receive training are “more likely to report positive outcomes from AI adoption, including better job performance and improved working conditions.” The wording concerns workers’ reported experiences, not a randomized comparison of training with hiring. The OECD also summarizes survey evidence that more than half of workers using AI reported receiving employer-funded training. The OECD’s 2026 report, AI and skills, also reports that nearly 40% of SMEs experiencing a skills gap said generative AI helped compensate for it. That finding indicates reported partial help for some firms; it does not show that AI replaces the need for employees, training, or specialist expertise.
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A practical way to choose
- Map AI-related tasks. Identify where the team expects AI to help, who will use it, and what could go wrong if its output is inaccurate or inappropriate.
- Define the skills for each task. Separate technical, responsible and ethical, and non-technical requirements rather than treating “AI skills” as one capability.
- Sort teachable gaps from specialist gaps. Ask which skills current employees can build through role-linked practice and which require experience or depth the team does not have.
- Check time, capacity, and foundations. Establish whether staff can learn and practice before the capability is needed, and whether data or governance issues must be addressed first.
- Choose a route for each gap. Upskill for distributed, task-linked needs; hire or contract for urgent specialist requirements. Set expectations for oversight and, where external expertise is used, knowledge transfer.
- Review and refresh. Revisit the skill map as tools, tasks, and governance expectations change. Training that is not kept current may no longer fit the work.
The UK government’s AI skills for the UK workforce overview links to employer resources including a skills framework, adoption pathway, and checklist that can support this assessment.
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