AI TRAINING
Communicating AI to Non-Technical Stakeholders
Technical leaders gain the tools to translate complex AI work into decisions executives actually make.
What it covers
This workshop equips data scientists and technical leaders with structured communication frameworks for presenting AI models, results, and limitations to non-technical audiences. Participants practise explaining model behaviour without jargon, crafting narratives around uncertainty and risk, and designing visuals that support executive decision-making. Sessions combine theory with live demo critique, role-played stakeholder conversations, and feedback loops. By the end, participants can confidently run an AI project review or board-level briefing.
What you'll be able to do
- Translate a model evaluation report into a 5-minute executive briefing with clear business implications
- Design a visualisation that communicates prediction confidence and error rates to a non-statistical audience
- Deliver a live AI demo that remains coherent and trustworthy even when the model behaves unexpectedly
- Navigate a difficult stakeholder conversation about model limitations or project setbacks without losing credibility
- Produce a one-page AI project summary that aligns on success metrics with a business sponsor
Topics covered
- Structuring an AI narrative for a non-technical audience
- Explaining model mechanics, accuracy, and uncertainty without jargon
- Visualisation patterns that communicate model outputs clearly
- Demo craft: live model demos that build trust, not confusion
- Handling bad-news conversations (model failure, bias findings, scope changes)
- Translating technical KPIs into business impact metrics
- Managing stakeholder questions and objections in real time
- Designing one-pagers and slide decks for AI project reviews
Delivery
Typically delivered as a one- or two-day in-person or virtual workshop. The ratio is approximately 30% instruction to 70% practice: participants bring a real project or are given a realistic case study. Live demo critique sessions require participants to prepare a short (5-10 min) presentation of an AI output beforehand. Materials include a communication framework card, visualisation pattern library, and a stakeholder question bank. Remote delivery works well with breakout rooms for role-play; in-person is preferred for the demo feedback rounds.
What makes it work
- Practising with real project material rather than abstract case studies, so frameworks transfer immediately
- Having a peer review loop where colleagues critique communication choices before stakeholder meetings
- Establishing a shared vocabulary between technical and business teams prior to formal reviews
- Designating a communication owner on each AI project team to maintain consistency across updates
Common mistakes
- Leading with model architecture or technical metrics instead of the business question being answered
- Presenting a demo in a controlled environment that collapses under live stakeholder questions
- Using accuracy or F1 scores as headline numbers without contextualising what they mean for real decisions
- Avoiding difficult trade-off conversations (false positives vs. false negatives) until they become crises
When NOT to take this
This training is not the right fit when the core problem is that the AI project itself lacks a clear business objective — better stakeholder communication will not compensate for a model that was not scoped around a real decision.
Providers to consider
Sources
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.