Data and AI literacy for teams
Build a shared understanding of data and AI across a team or department. Plain-language sessions cover interpreting information, questioning outputs and discussing evidence before using it to support a decision.
How we can help
When a report or AI-generated answer reaches a team, people need enough understanding to ask where the information came from and what it can support. This programme introduces data and AI concepts in plain language, using examples drawn from the organisation's work. It is designed for staff and managers who use information in decisions without necessarily producing the analysis themselves.
Participants practise reading tables and charts, examining the meaning of a measure and identifying information that is missing. We also explain how AI systems generate responses and why the quality of an answer depends on its context and inputs. The aim is a shared basis for discussion: colleagues can raise useful questions, recognise uncertainty and know when specialist advice is needed.
When this may be useful
- Your organisation is introducing dashboards or AI tools and staff need a common understanding of the information they will encounter.
- Meetings reveal different interpretations of the same figures, measures or AI outputs, making decisions difficult to explain.
- Managers and operational staff need an accessible foundation before progressing to more specialised analytics or AI training.
What the work involves
Reading and questioning data
Work through tables, charts and common measures. Ask what is being counted, which period is covered, who is missing from the data and whether a comparison is meaningful.
Understanding AI in plain language
Explain the role of inputs, patterns and generated outputs with familiar examples. Discuss why an answer can sound convincing while containing errors and why different tasks require different levels of checking.
Limitations, bias and responsible use
Explore how incomplete records, unrepresentative samples or assumptions can affect a result. Practise describing uncertainty and recognising information that requires care because it concerns people or confidential organisational work.
Applying a shared vocabulary
Use the organisation's examples to rehearse a discussion about evidence. Develop common questions for report reviews and AI outputs so colleagues can explain concerns clearly to specialists and decision-makers.
How the assignment runs
Assess the group's familiarity
Discuss the information participants use, the questions that arise and the planned data or AI initiatives. Identify the concepts the programme should prioritise.
Explain and practise
Introduce each concept through an example and a guided discussion. Give participants space to question a result and explain their interpretation to colleagues.
Connect learning to team decisions
Review examples from the organisation and agree a set of useful questions and terms the group can carry into its regular meetings.
What to prepare
These details will help us understand the starting point and agree a useful scope:
- The participating team's roles and current familiarity with data or AI
- Examples of reports, dashboards or outputs that staff need to understand
- Background on planned initiatives and recurring questions about the information used
If some information is still being developed, we can discuss what is available in the first conversation.
Questions about this service.
For anything specific to your organisation, get in touch. We can discuss the requirements before you decide on an engagement.
Should managers attend with the rest of the team?
A mixed group can be useful when managers and staff discuss the same reports or decisions. We can adjust examples and discussion activities to accommodate the different responsibilities and levels of experience in the room.
Does the programme teach statistics or programming?
It introduces the ideas needed to read and question information in everyday work. It does not require programming or advanced mathematics. More specialised learning needs can be identified during the initial discussion.
How is this different from AI for professionals?
Data and AI literacy develops a common foundation for understanding and discussing information. AI for professionals spends more time practising specific workplace tasks, prompts and review routines in AI tools.
Let’s talk about what you need.
Share a little about your project, the challenge you are facing and where you would like some help.