AI Training and Skills 

Scotland’s world-class universities, advanced infrastructure, and collaborative business communities give SMEs a strong foundation for adopting AI. But one major barrier remains: the skills gap.

Currently, only 17.6% of Scottish businesses are using AI technologies. Building an AI-ready culture starts with preparing your people. Training supports that readiness and accelerates ethical, confident adoption across your teams.

This guide provides practical steps for identifying skills gaps in your business, structuring effective training programs, and implementing consistent learning strategies for ethical AI use.

1. Identifying AI Skills Gaps

Before you invest in training, it’s important to understand where the real gaps lie. These will vary by sector, business goals, and your team’s current capabilities.

Different sectors require different AI applications and corresponding skill sets. Below are examples of core AI skills by industry. You may use these as a starting point to define your own needs.

Retail and E-commerce

The skills needed include customer analytics, AI inventory management and personalised marketing algorithms. Employees need to understand how recommendation systems work and how to interpret customer behaviour data.

Manufacturing

Focus areas include predictive maintenance, quality control automation, and supply chain optimisation. Staff require skills in interpreting sensor data, understanding machine learning models for failure prediction, and implementing robotic process automation.

Professional Services

Key applications include document analysis, process automation, and client insights. Skills needed include understanding natural language processing, data visualisation, and how to integrate AI into client service workflows.

Healthcare

Important areas include patient data analysis, appointment scheduling optimisation, and diagnostic support. Training for this includes handling sensitive data, understanding medical AI limitations, and maintaining human oversight.

Energy

Skills in data analysis for grid optimisation and AI for clean energy transition are essential. This includes understanding predictive models for energy consumption and maintenance scheduling for renewable energy systems.

How to Conduct a Skills Audit

A well-run audit provides a clear view of your current capabilities and where to focus. It also informs your procurement planning. Consider the following steps:

1. Assess current capabilities
   Use surveys or team interviews to evaluate both technical knowledge (AI tools, data skills) and soft skills (problem-solving, ethical thinking). Include scenario-based questions that reflect real business situations.

2. Map against future needs
   Create a framework of AI applications relevant to your business and the skills required to implement them. Consider both immediate opportunities and longer-term goals.

3. Prioritise critical gaps
   Focus on areas with the biggest knowledge gaps or those tied to high-value use cases.

4. Include both technical and non-technical skills
   Think beyond coding: include data literacy, change management, and ethical reasoning.

2. Shaping Your Training Strategy

Once you’ve mapped your needs, tailor your training approach to different roles. Not everyone needs the same depth of knowledge, so segmenting your workforce can make training more efficient and relevant.

Who Needs What?

AI Citizens (all staff)

These are the staff members who need basic AI literacy, covering fundamental concepts, ethical implications, and how AI affects their specific role. Training should focus on clarifying and simplifying AI, thereby building trust and confidence in working alongside automated systems.

AI Workers (directly impacted roles)

AI workers require practical skills in using AI tools relevant to their function. For example, marketing staff might need training in using AI analytics platforms, while operations staff might need skills in monitoring automated processes.

AI Professionals & Leaders (specialists)

AI specialists have a deep technical knowledge in implementing, customising and managing AI systems. This may include programming skills, understanding machine learning models and system integration expertise. Additional staff may be needed to fill these roles.

For those new to AI, introductory courses like Living with AI provide a solid foundation, covering basic concepts, ethical considerations and practical applications; useful for all members of staff.

Upskilling vs. Reskilling

Understanding the difference between these two approaches allows for more effective planning: 

Reskilling

This refers to teaching employees entirely new skills for different roles, e.g. training a data entry clerk to become a data quality analyst as automation takes over routine tasks. This typically involves more intensive training and a significant shift in job responsibilities.

Upskilling

Upskilling is enhancing existing skills with additional AI capabilities. For example, teaching sales staff how to use AI-powered CRM systems to better understand customer needs. This builds upon existing expertise rather than replacing it.

Most SMEs will need a blend of both approaches: reskilling employees whose roles are most impacted by automation, and upskilling others to work more effectively alongside AI tools. Start by identifying which roles will be augmented by AI, versus those that might be fundamentally transformed - then match your strategy accordingly.

Creating Supportive Learning Environments

For training to stick, culture matters. The most effective AI training requires a positive workplace culture and a blended learning approach that complement each other consistently and effectively:

• Online learning
  Flexible courses (e.g. from The Data Lab) let staff learn at their own pace. Allow time during work hours to show that training is a priority.

• In-person workshops
  Partner with local universities or training providers for hands-on learning. These sessions work well for technical or ethics-focused topics.

• Project-based learning
  Let staff apply what they’ve learned to real challenges in your business — ideally with guidance or mentoring. This makes learning practical and relevant.

Embedding Ethics in Your Training

Ethical AI is a national priority — and should be a central pillar in your training efforts. Key areas include:

• Transparency
  Train staff to understand and explain how AI decisions are made — and to spot when a system is becoming a "black box".

• Fairness
  Help teams recognise bias in AI and test for unintended impacts, especially in hiring, finance, or customer service.

• Privacy
  Focus on GDPR compliance, data handling, and protecting sensitive information, especially personal or health data. Likewise, have a process in place that respects the privacy and rights of your employees.

• Human oversight
  Establish clear protocols for when AI decisions need a human check. This is especially important in high-stakes or sensitive areas.

More information regarding legal aspects can be found on our AI Governance and Regulations guide.

3. Putting Your Plan into Action

With your skills gaps identified and training approach defined, the next stage is to implement your plan effectively. Keep in mind that training isn’t just about delivering content - it’s about building capability over time.

The ability to track the progress of your employees will be fundamental to how your business adapts to AI:

Set clear learning objectives

Define specific, measurable goals for each role category. For example, after training, customer service staff will be able to effectively supervise AI chatbot interactions and intervene, when necessary, with monthly milestones tracking progress.

 Use the SMART framework (Specific, Measurable, Achievable, Relevant, Time-bound) to structure these objectives.

Run assessments

Create pre-training assessments to establish baselines, followed by post-training evaluations that include both theoretical knowledge and practical application scenarios.

Consider using simulation exercises where employees demonstrate their ability to work with AI tools in realistic business situations.

Create opportunities to apply learning

Let staff test tools in safe environments - like internal AI sandboxes - where they can safely experiment with tools relevant to their roles. Assign small-scale projects that apply new skills to actual business challenges, with appropriate support and mentoring.

Improve through feedback

Allow for easy feedback mechanisms including anonymous surveys, focus groups and one-on-one check-ins. Create a structured process for analysing this feedback and acting on it promptly, paying particular attention to identifying barriers to adopting AI.

Validate skills

Partner with Scottish educational institutions or industry bodies to develop recognised credentials. Document skills development formally to support employee career progression and also to demonstrate your business' AI capabilities to clients and partners.

Maintaining consistency and standards

To maintain quality and consistency over time:

• Build a small AI governance team with representatives from different departments (see our Governance and Regulations page)

• Develop an internal knowledge base of AI best practices specific to your industry

• Schedule regular reviews of AI systems and skill requirements as your technology changes

• Integrate AI ethics fully into your company values and decision-making frameworks

• Establish clear escalation paths for AI-related concerns or questions

4. Resources for Scottish SMEs

The Data Lab

The Data Lab offers the Data Skills for Work program, specifically designed for Scottish businesses, with short courses in data science and AI fundamentals.

National Robotarium

Based in Edinburgh, National Robotarium provides robotics workshops (and other training) relevant to construction, manufacturing and healthcare sectors.

Scottish Business Resilience Centre

Provides cybersecurity training essential for safe AI deployment.

Interface

Connects Scottish SMEs with academic expertise in AI and data science from Scottish universities.

Skills Development Scotland (SDS)

Provides the Skills for Growth service offering free skills gap analysis and training plans for businesses with 5-250 employees.