AI Literacy Training Course for Non-Technical Managers: Safe Use, Checking Outputs and Judgement

Programme Summary

AI literacy for non-technical managers is the subject of this one-week training course for department heads and senior professionals without a technical background, taught at managerial level and ending in a department AI use plan. Staff already use AI tools for drafting and analysis, often without agreed checks on accuracy, confidentiality or judgement. The programme moves from what AI can and cannot do to everyday applications, output checking and safe use, using prepared work scenarios from a public body and a commercial firm. Mars Training Centre UK delivers this programme on AI literacy to mixed-organisation cohorts.

The Business Case

Managers who can judge what AI does well, instruct it clearly and test its answers before acting save time without passing errors upward. Without that discipline, confident but wrong output enters reports, sensitive information leaves the organisation and staff rely on tools that nobody has set limits for. The programme works on the measures below.

• Rework and Error Rate: Falls when outputs are checked against sources before use.
• Information Exposure: Reduces when staff know what may be shared with a tool.
• Manager Time: Improves when routine drafting and summarising follow a tested workflow.

Principles and Evidence Base

• OECD socio-technical approach to AI literacy: A view that combines technical understanding with social and organisational context, applied in Sessions 1 and 5 to frame what managers need to know and do.
• Output verification: The practice of testing claims against sources, applied in Session 3 to guard against hallucination and bias.
• Information classification: The principle of grading data by sensitivity, applied in Session 4 to set what may be shared with a tool.
• Human oversight: The principle that a person remains accountable for decisions, applied in Sessions 4 and 5 to limit over-reliance.

Intended Participants

• Department heads who set how their teams use AI tools day to day.
• Senior professionals who produce reports, analysis or advice with tool support.
• Managers who sign off work prepared with AI help and answer for its accuracy.
• Programme and operations leads who want agreed rules before wider adoption.
• Support function leads who handle sensitive records.

Typical settings include public bodies, banking, healthcare administration, education and professional services.

Learning Outcomes

1. Participants will be able to evaluate what an AI tool can and cannot do for a given task.
2. Participants will be able to prioritise routine tasks by suitability for AI support.
3. Participants will be able to construct clear instructions for drafting, summarising and analysis support.
4. Participants will be able to diagnose errors, gaps and bias in an AI output.
5. Participants will be able to classify information by what may be shared with a tool.
6. Participants will be able to justify a department AI use plan to a senior leader.

Programme Structure

Day 1: AI Literacy Foundations for Non-Technical Managers

What can AI systems do for a manager, and where do they fail?

• Plain-Language AI System Map
• Generative and Predictive Tool Comparison Sheet
• Capability and Limit Checklist
• OECD Socio-Technical Reading of AI Literacy
• Personal AI Use Baseline

Day 2: Everyday Applications in Management Work

Which routine tasks suit AI support, and which should stay with the manager?

• Task Suitability Screen
• Drafting and Summarising Workflow
• Analysis Support Question Frame
• Meeting Preparation Template
• Time Saved and Rework Log

Day 3: Instructing AI and Checking Its Outputs

How are instructions written, and how is an answer tested before it is used?

• Instruction Structure Sheet
• Source and Claim Verification Table
• Hallucination Spotting Checklist
• Bias and Gaps Review Note
• Output Sign-Off Rule

Day 4: Safe Use, Confidentiality and Human Judgement

Which information may be shared with an AI tool, and when must a person decide?

• Information Sensitivity Grid
• Confidentiality Do and Do Not List
• Over-Reliance Warning Signs
• Human Judgement Decision Rule
• Team Acceptable Use Note

Day 5: AI Literacy Plan Review for One Department

Do the earlier findings join into a plan that a department can apply?

• Applying the Session 1 Capability Checklist
• Testing the Session 2 Task Screen
• Checking the Session 3 Verification Table
• Reviewing the Session 4 Sensitivity Grid
• Completing the Department AI Use Plan

Methodology

The programme is a structured exercise built on prepared work scenarios from a public body and a commercial firm, such as a briefing note, a data summary and a draft policy. Participants instruct a tool, test what it returns, grade the information involved and set rules, then defend their plan to peers acting as a senior leadership team.

• Suggested activity: Finding the three errors planted in an AI-written briefing note.
• Suggested activity: Sorting twenty document types into shareable and restricted.
• Suggested activity: Rewriting a vague request into a clear instruction and comparing results.

Capstone Output

Each participant completes a department AI use plan. It holds a task suitability screen, an instruction sheet, a verification routine, an information sensitivity grid and a sign-off rule. At work it goes to a senior leader for approval and becomes the shared reference for how the team uses AI tools.

Frequently Asked Questions

Is AI literacy training suitable for managers with no technical background?

Yes. AI literacy is taught through prepared work scenarios and templates. You need responsibility for a team, reports or decisions, but no programming or data science background.

Which approach does AI literacy training rest on?

AI literacy here rests on the OECD socio-technical approach, which joins technical understanding with organisational context, supported by output verification, information classification and human oversight.

How is the plan from AI literacy training used at work?

You take the department AI use plan to a senior leader, use its verification routine before work leaves your team, and keep its sensitivity grid as the rule for sharing information with tools.