AI is now firmly a business tool.
Across organisations, access to AI – particularly large language models – has increased rapidly. People are experimenting, exploring, and beginning to use these tools in their day-to-day work.
But what’s becoming clear is that access and effective use are not the same thing.
Many employees are still using AI at a surface level – as a faster way to search or generate content – rather than as a tool to support deeper thinking, problem-solving or workflow transformation.
While much of the conversation has focused on what it means to be “AI ready”, the next question for many organisations is more practical:
what does that actually mean for learning and capability development?
From recent client work, a clearer picture is emerging of how organisations are responding – and how their approach to AI training is evolving.
In practice, we’re seeing a number of consistent patterns:
1. Demand is broad – but not yet consistent
One of the clearest patterns is the breadth of AI training requests.
At one end, organisations are investing in AI literacy programmes to build shared understanding – covering core concepts, practical applications and responsible use. At the other, requests are highly specific, such as focused training on data protection or compliance.
Between these sits a growing demand for role-specific training, supporting teams such as communications, technology and operations to apply AI in ways directly relevant to their work.
This is reflected in the training being commissioned – from one-day literacy sessions for groups of 16–20 employees, through to targeted interventions and multi-day technical programmes exploring how AI can be embedded into workflows.
Overall, this mix points to a pragmatic approach: responding to immediate needs while building capability over time.
2. The shift from awareness to application
While awareness remains an important starting point, expectations of AI training are evolving quickly.
Many programmes still begin with building understanding – covering concepts, risks, common pitfalls and practical ways to get the best from AI tools, including how to prompt effectively and refine outputs.
This means focusing on:
- real use cases
- hands-on experience
- embedding AI into everyday workflows
For example, training focused on content creation is not just about understanding tools, but about applying them – generating outputs, refining prompts and integrating AI into existing processes .
In more technical environments, the emphasis shifts further – towards improving productivity, supporting development processes and integrating AI across the software lifecycle .
Across both, the direction of travel is clear:
AI training is moving from knowing about AI to using it effectively in context.
3. A growing gap in effective use
Alongside increased adoption, a consistent challenge is emerging.
Many individuals are already using AI tools – but often in limited or inconsistent ways. Typical usage tends to focus on:
- quick answers
- drafting content
- basic task support
Far fewer are:
- structuring prompts effectively
- iterating and refining outputs
- using AI to support thinking, decision-making or problem-solving
And fewer still are exploring more advanced applications such as automation or integrating AI into workflows.
This creates a gap – not in access, but in how effectively AI is being used.
For organisations, this is becoming a key focus of training: not just enabling use, but improving the quality and impact of that use.
4. Risk, governance and responsible use remain central
As organisations expand their use of AI, there is also a strong and consistent focus on risk and responsible use.
Across a wide range of training requests, there is a need to address:
- data privacy and security
- how AI tools handle organisational information
- ethical considerations and governance
- limitations such as bias or unreliable outputs
In many cases, these concerns are not secondary – they are a primary driver for training.
Organisations are looking to enable adoption, while ensuring that appropriate guardrails are in place.
5. AI capability operates at multiple levels
What is becoming clearer is that AI capability is not a single skill – it operates across different levels of the organisation.
At a strategic level, organisations are considering:
- how AI can create competitive advantage
- which tools and platforms to prioritise
- what governance and guardrails are required
At a departmental level, the focus shifts to application:
- how AI can support specific functions such as marketing, finance or operations
- where the most valuable use cases sit
- how workflows may change
At an individual level, the focus is on capability:
- how to get started with AI tools
- how to improve how they are used
- developing practical skills such as prompt engineering and critical evaluation
In many organisations, these layers are being addressed – but not always in a coordinated way.
6. What effective use can look like in practice
Where individuals and teams are developing more confidence with AI, the way it is being used begins to change.
Beyond basic tasks, AI can start to support:
- structured thinking and planning
- shaping ideas and exploring options
- drafting and refining requirements
- analysing discussions and identifying improvements
- highlighting opportunities to embed AI within processes
This represents a shift from using AI as a tool for outputs, to using it as part of how work is designed and delivered.
So what does this mean for L&D?
Taken together, these patterns suggest a few practical considerations:
- Begin with awareness, but plan early for application
- Focus on real use cases and workflows, not just tools
- Embed risk and responsible use from the outset
- Recognise that different roles require different levels of depth
- Think beyond one-off interventions towards ongoing capability development
A final thought
AI is not simply another topic to add to the learning agenda.
It is a capability that cuts across the organisation – shaping how people work, make decisions and deliver outcomes.
The organisations making the most progress are not necessarily those doing the most training, but those taking a more coordinated and practical approach to building AI capability – from strategy through to day-to-day application.
