A keynote gives a mixed audience a common starting point, usually within 30 to 60 minutes. It might explain generative AI, which produces text, images or other material in response to instructions, then relate it to familiar work such as drafting a client email, comparing meeting notes or preparing questions for a supplier. Ask for examples that reflect your sector rather than a parade of impressive demonstrations. A useful session should show where human judgement remains necessary. If attendees leave knowing both one sensible use and one clear limitation, the talk has achieved more than simple excitement.
A workshop is better suited to people who need to practise. Participants can take a vague request and specify the role, objective, audience, constraints and preferred format. They can then compare two outputs, identify a weak assumption and revise the instruction. The facilitator should explain that a prompt is not a magic formula; the surrounding context and source material affect the result. It is also worth discussing when not to use an AI system, such as handling sensitive personal information in an unapproved service. Time for experimentation matters, because watching a demonstration rarely builds a reliable working habit.
The explanation should include enough technical detail to prevent misplaced confidence. A large language model generates likely sequences of words from patterns in its training, so fluent wording does not prove that an answer is accurate. Hallucination describes an answer that sounds credible but contains unsupported or incorrect information. Retrieval augmented generation can provide selected documents for the system to consult before responding, but staff still need to check the result against the original material. A practical exercise might involve comparing an AI summary with a published report, marking every claim that needs a human check before it reaches a customer.
Risk also depends on the work itself. A finance team could use an approved tool to organise public reports while keeping private client details outside it. A marketing team might ask for campaign research, then verify names, dates and quotations before publication. The speaker should address access permissions, data handling, copyright, bias and accountability in plain language, without offering legal certainty they cannot provide. Ask whether participants will receive a simple checking process or reference sheet afterwards. A small habit, such as saving the source document beside an AI generated draft, can prevent repeated checking and avoidable rework.
The room’s level of trust should shape the format. A fireside conversation or panel can give executives, technical staff and sceptics space to test different assumptions, provided the moderator does more than collect agreeable opinions. Clarify who will guide questions, whether practical examples are included and how disagreement will be handled. A podcast or small interview may suit a group exploring the relationship between human judgement, artificial systems and personal responsibility. If you are comparing options, the AI speaker Sydney page can form one part of your research, alongside conversations about tone, experience and audience fit.
Use the planning call to discuss the venue, timing, technology, audience mix and the behaviour you want to change the following week. Ask a prospective speaker to adapt examples to your industry and explain how questions will be handled if someone raises job concerns or a confidentiality problem. Confirm whether slides, prompts or workshop materials can be shared afterwards. Review past themes, but do not judge a speaker by entertainment alone. Clear preparation, honest limits and a concrete session plan offer better event speaker selection guidance than a polished demonstration without a route into daily work.
