Familiarise yourself with the generic AI skills planning process below, and review how it will fit against how you currently do skills planning in general. 

  • If you already manage skills using an approach like strategic workforce planning or competency development frameworks, you can use it as the basis for AI skills planning. You’ll need to map the five steps described below against your own process, and integrate them appropriately for your circumstances.
  • If you don’t do skills planning formally, but want to, you could use this AI skills planning process as a starting point for a more systematic approach. The CIPD can provide guidance on selecting an appropriate approach for your circumstances.
  • If you’re not ready to do skills planning formally yet – like many organisations, mostly SMEs – think seriously about doing so just for AI skills, and possibly digital skills as well.

Once you’re comfortable with the high-level process, you can go to the section describing the detailed process for your level of AI adoption. There are three options: one for early-stage AI adopters, one for intermediate AI adopters, and one for advanced AI adopters.

Use the self-assessment quiz to find out your level of AI adoption maturity.

Go to the appropriate AI skills planning section for your level:

Planning step Early-stage (foundational) Intermediate (operational) Advanced (strategic)

1. Align

(the why)

Curiosity and safety

Move from uncertainty to working assumptions  

Feasibility

Evaluate team-level pilots and KPIs  

Transformation

Structural impact and hard choices  

2. Audit

(as-is)

Interest-based

Informal check of who is ‘AI-curious’  

Role-based

Assess specific job families and teams  

Process-based

Formal, scalable part of core systems  

3. Analyse

(gaps)

Risk mitigation

Focus on data privacy and basic literacy  

Quality control

Focus on prompting and verification  

Performance

Focus on managing hybrid AI-human teams  

4. Act

(the how)

Exploration

Create ‘sandboxes’ and use free resources  

Capability

Build internal skills and update policies  

Restructuring

Redesign career paths and structures  

5. Adjust

(evolution)

Monitoring maturity

Track attitudes and readiness for next stage  

Measuring impact

Track time saved and error rates in pilots  

ROI and analytics

Integrate AI results into HR analytics  

Details of each of the general steps are described below.

1. Align (The “why” and “what”)

Strategic alignment is about ensuring AI skills work serves a business purpose rather than just following technology trends. Its aim is to build skills plans around what the business is trying to achieve through the technology, as well as how to operate the technology. This requires input from technology colleagues on specific AI technology that will be used, for example ChatGPT or Copilot.

HR must partner with business and tech leaders to define the ‘why’ of AI. For example, is the potential aim of AI to enhance products (eg, get better customer insights about their needs and wants) or to drive efficiency (eg, automating complex data entry)?

Establishing timescales is crucial, so get a sense of anticipated management time horizons for AI. An aspiration to fully automate a function in six months needs a much more aggressive reskilling plan or transition strategy than a three-year pilot programme.

2. Audit (The ‘as-is’)

Auditing is about uncovering the current reality of AI use. This often reveals ‘hidden’ AI users, such as employees using tools for specialist tasks through their own initiative, without formal oversight – for example to speed up data analysis or report writing.

Beyond technical ability, the audit should gauge employee attitudes. Identifying where there is enthusiasm for AI can help you find ‘AI champions’, while uncovering fear allows you to tailor your communication and change management to address resistance early.

3. Analyse (The ‘gaps’)

The goal here is to identify the distance between your current audit results and your future business goals. This involves distinguishing between the need for high-volume general AI literacy and low-volume, high-impact technical skills.

Be pragmatic: prioritise gaps that pose the highest risk (like lack of AI ethics knowledge) or offer the quickest wins (like prompt engineering for high-volume writing tasks). Document your assumptions about when these skills will be needed to ensure training isn’t delivered too early or too late.

Be sure to also consider the impact of AI on non-AI skills. For example, the adoption of AI to generate written documents will reduce the need for skills associated with document preparation, such as effective business writing and preparing executive briefings.

4. Act (The ‘how’)

Once gaps are identified, HR must decide between four strategies: buy (hiring new talent), build (training existing staff), borrow (using consultants), or bot (automating the task entirely).

While ‘borrowing’ talent is fast, it doesn’t build long-term internal capability. ‘Botting’ is the most complex from a people perspective, requiring deep collaboration with IT and Finance to manage the fallout of automation, such as potential redundancies or the need for significant role redesign.

In addition to the operational aspects of filling the skills gaps, it is equally important to win hearts and minds so that the gaps stay filled. Unlike other forms of skills planning, AI-related HR work has to deal with unprecedented degrees of employee scepticism and concern, so the softer aspects of organisational change such as confidence building are an integral part of AI skills planning.

5. Adjust (The ‘evolution’)

AI is moving so fast that an annual skills plan is likely to be obsolete before it is finished. Shorten review cycles, ideally quarterly, to regularly reconfirm that the AI tools and new skills you planned for are still appropriate and your risk assumptions are still valid.

You should also plan for ‘crisis’ skill needs – having a ‘break glass’ plan to access expert AI help if a system fails or leads to errors that cause financial or reputational damage.

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