What is an AI upskilling programme?

AI upskilling means giving employees the skills to understand and work with AI technology. This encompasses technical, non-technical and ethical skills, meaning that most employees, regardless of role, can benefit from this training.

Following a step-by-step upskilling approach helps to make sure your training is organised and that its outcomes are measurable.

What your AI upskilling programme includes depends on your particular business needs. For example, if you are a marketing company, enrolling your employees on marketing prompt engineering may be particularly useful for them. To improve the productivity of your human resources team, you could encourage them to learn about the different forms of recruitment automation.

Why is AI upskilling important?

With businesses increasingly investing in AI to increase productivity and innovation, having an AI upskilling programme can keep employers competitive in the jobs market. AI upskilling can improve your business’s readiness to adopt the technology.

A UK government study identified that over half of UK businesses (54%) feel ready to scale their use, with 13% describing themselves as completely ready and 41% as fairly ready. Under a quarter (23%) are unsure, while 12% reported that they are not ready to increase their use.

For organisations that were planning to adopt AI, only 34% feel ready, 33% are unsure and 32% said they are not ready.

AI upskilling programme best practices

To achieve the best results from your AI upskilling programme, it can be useful to start with the right approach.

Before you begin building your course, consider best practices and what goals you’re hoping to achieve:

• Set a good example as a leader: encourage managers and C-suite executives to adopt AI tools. Employees might be more willing to accept AI’s value if leaders demonstrate its usefulness first.
• Identify and support early adopters: build a community around employees who are enthusiastic to learn new technologies and support them in their growth. Make this community inclusive and provide incentives for participation.
• Focus on responsible AI use: as part of their upskilling, train employees in AI ethics. Help them learn how to spot bias in training data and how to use company data safely with generative AI.
• Make training inclusive: gain a diverse range of perspectives on your AI training. Include people who have concerns about AI use at work in the conversation. They could help you integrate AI tools more effectively, safely and sustainably.

1. Set your upskilling goals

Setting upskilling goals helps you to achieve measurable, more effective results. Create a checklist based on the following:

• Define your business goals first: identify what you hope to achieve with AI-powered technology, such as whether it’s for productivity goals, innovating your company’s product offering, research or automating tasks.
• Create measurable outcomes: outline the outcomes you’re hoping to achieve through AI upskilling. For example, increasing productivity through automation by 20%. To make training goals more realistic, break them down into manageable milestones.
• Highlight specific rather than general goals: define which skills you’re specifically searching for, like ‘AI data analysis’ rather than ‘AI literacy’. The more specific you are, the easier it may be to measure upskilling outcomes.
• Identify what category each skill fits into: decide whether each skill you’re hiring for is technical, non-technical or related to ethics. Also, think about whether it requires a foundational, intermediate or more advanced level of expertise. For example, you might upskill your data scientists by training them in technical AI skills, such as how to use the technology to clean up raw data.

The UK government provides an AI tools package to help employers assess AI skills and plan their training. As part of the package, they recommend using an AI skills adoption pathway model with the following steps:

• Awareness: understanding what AI is and why it matters
• Exploration: identifying potential applications in your business context
• Assessment: assessing existing AI skills gaps and needs
• Experimentation: trialling small-scale AI tools
• Reflection: learning from these early trial experiences
• Upskilling: building staff AI capability and confidence
• Integration: embedding successful tools into employee workflows
• Strategy: assessing whether AI use aligns with your organisation’s goals
• Scaling: expanding successful AI tool adoption across teams and services

You might find it helpful to refer to these points while developing your AI upskilling programme. These steps are not linear. You may choose to move back and forth between them as you learn more about how you choose to use AI as a business.

Identify barriers to learning

Alongside defining your upskilling plan’s structure and goals, identify any possible barriers to learning. Employees may need additional support to build confidence in adopting new technology. The UK government found that only 21% of employees felt confident about using AI technology at work.

Reasons for a lack of confidence can include:

• Their employer not having a clear AI strategy
• Being unsure about interpreting outputs
• Being expected to use AI tools without any training
• Feeling hesitant to ask for help

They might also be uncertain about how it might alter their role.

Reinforce the idea of AI as a collaborative tool and highlight how it can assist employees with their daily duties. For instance, your sales team might automate some of their more repetitive responsibilities while spending more time dealing with the more ‘human’ elements of their work, such as talking to clients and customers.

2. Conduct an AI skills gap analysis

Once you have addressed these points, conduct a skills gap analysis. This enables you to identify your workforce’s current AI capabilities and the skills they need to implement your AI strategies.

While doing this, consider your business goals and how introducing AI skills can help you meet them better.

Create an inventory of current AI skills

Create an inventory of your company’s existing AI proficiencies through skills assessments, surveys, one-on-one meetings and feedback from managers.

Feedback can give you an impression of how confident employees feel about AI technology.

Skills tests show you whether employees have an aptitude for AI regardless of how confident they feel about it. You may find that some employees have more foundational knowledge than they realise.

Identify different team skill sets

Different teams may require different sets of AI skills. While conducting a skills gap analysis, consider which of your job roles would benefit most from technical vs non-technical AI skills.

Technical skills are practical competencies that require specialised knowledge, often requiring coding or data science competencies. Non-technical skills are those that enable employees to use AI tools productively without coding experience or a scientific background.

For instance, employees working in data science, computer programming or engineering roles may benefit from technical skills. You can upskill marketing, sales and customer service employees in non-technical skills.

All employees generally benefit from having ethical AI skills, such as understanding data protection. This enables them to use the technology safely, a framework sometimes referred to as responsible AI practice.

3. Create learning and development pathways

You can create your own courses or use online courses that are available. Find courses that align with both your upskilling and business goals. For example, these courses can cover:

• Fundamental AI concepts for beginner users
• How to use prompt engineering to boost your recruitment workflow
• Advanced training in machine learning, such as how to write algorithms and use neural networks
• Using AI tools to develop your business strategy
• How to use AI data analysis tools to achieve better quality predictive analytics

Reputable institutions often provide online courses that lead to certifications and credentials. Enrolling employees in these courses can help make your business appear more professional in the field of AI.

They can also help employees to build their own portfolios.

Identify free and paid learning opportunities

You can integrate existing free and paid training schemes into your learning and development strategy. Many foundational AI courses are freely available, such as through the UK government’s AI Skills Hub.

Industry professionals develop these, and learners receive confirmation that they have completed the course. They teach users how to use AI in simple tasks such as:

• Drafting text
• Creating content
• Completing administrative tasks

Advanced skills credentials

Consider upskilling your technical staff in more advanced AI skills. You can do this by enrolling them on courses tailored to specialised skills such as data analysis, AI software development and using prompt engineering to generate code.

Paid training can deliver a strong return on investment (ROI) through increasing efficiency, retaining top talent and driving innovation.

If you are hoping to become an AI industry leader, you can sponsor employee postgraduate courses in AI. That way, they can gain access to lecturers who are experts in the field and can develop their thesis around solving AI-related problems within your business.

Postgraduate courses may be beneficial for expert employees interested in developing their research around your business product. Unless this is the case, enrolling technical employees on shorter courses may be more beneficial since curricula can become outdated.

Microcredentials and short courses

Employees can gradually build their skills through short online courses or microcredentials. These courses are often stackable and completing multiple short courses can help employees gain an AI skills diploma certificate.

4. Track outcomes and gather feedback

Track learning and development outcomes, including identifying AI talent and employees who demonstrate high potential for advanced training. Gather feedback from employees and continue to identify barriers to progress.

You can gather feedback through a range of methods. These include:

• 360-degree feedback : gather feedback from different sources such as managers, customers and clients. This gives you an understanding of how an employee perceives their own skills development and how others perceive them.
• One-on-one meetings: employees can discuss their progress with their manager, voicing any concerns about their progress so far or noting any areas of study they are particularly interested in.
• Tracking employee performance: monitor metrics to determine if upskilling has met your initial objectives. Highlight any improvements in employee confidence, noting when they feel most assured in using the technology and any results they have independently identified.
• Informal discussions: through applying AI skills learned, employees might have also discovered their own innovative solutions. Record these discoveries for future training. They might also have other uses, such as new product features or improved workflow processes.
• Actively listening to employees: listening to your employees can also help you identify best use cases for AI within your business. They might find using AI tools to be particularly valuable during certain tasks and not others.

5. Encourage continuous learning

Meanwhile, identify ongoing peer support opportunities so employees can continue to build on their knowledge base. Since the AI industry is rapidly advancing, ongoing support can be of great benefit to both your teams and your business. Ongoing peer support can include:

• Mentoring colleagues who need extra support
• Collaborating with colleagues at a similar skill level on projects
• Creating employee resource groups (ERGs) where employees with a personal interest in AI can share knowledge
• Encouraging employees to seek out further training if they have a passion for learning more about AI
• Encouraging employees to engage in online support communities, asking questions to gain answers to AI-related problems.

How you build your AI upskilling programme depends on your business needs.

Those hoping to lead in innovation can train their staff in advanced technical skills, while businesses increasing their productivity benefit from enrolling staff on foundational, non-technical courses.

Ongoing learning and peer support can help you and your employees build on their knowledge over time.