Prepared Minds

Get hands-on asap

People learn faster when they get hands-on experience with AI systems.

We try to create a sense of 'AI vertigo' — the sudden expansion of possibilities and shift in problem-solving approaches — early on. Framed in the right way this can be exhilarating and motivating.

We get people working with AI as soon as possible, getting them to try different models and prompt structures so they can see the difference these things can make to the quality of their results. Once they've done it themselves, we explain why it works or doesn't.

This 'show then tell' approach sparks curiosity and engagement, making subsequent explanations more meaningful and relatable.

A sense of surprise activates our brain's capacity for learning. Find the right demo, and it usually doesn't take long before even the most sceptical of sceptics are surprised and delighted by something AI can do for them.

Understand the machine

To fully grasp the implications of AI, we've found it's important to show the timeline of human efforts and events that have created thinking machines and how recent innovations in generative AI came to be.

A brief look at the history of AI helps put today's advancements in context, while recognising the differences between narrow AI, general AI, and other approaches provides a clearer understanding of what the technology can and cannot do.

Understanding the machine usually follows this structure:

• History of AI: A brief overview of AI's evolution helps put current developments in perspective.
• Types of AI: The differences between narrow AI, general AI, and various AI approaches to provide a full picture of the technology's capabilities and limitations.
• Large Language Models (LLMs): The basics of how LLMs (the systems that power generative AI) work, as they form the backbone of many current AI applications.
• The AI Revolution: Explore why generative AI represents a fundamental shift in technology and its potential impacts across industries.

Filter the noise

Deep technical knowledge isn't essential for everyone. But we've found that our clients appreciate having a basic understanding of how AI systems work in terms of:

• The companies involved and who owns the different AI models
• The commercial values of things like the data chips used to fuel AI models
• The power dynamics and corporate agendas

They want to know this so they can filter the noise and make sound judgments. Understanding the competitive landscape, or 'Tech Race', also helps inform strategic decisions around AI adoption and investment.

Our curriculum usually covers:

• The AI Value Stack: The layers of technology, processes and companies that contribute to AI's functionality, from data collection to application development.
• The Tech Race: The competitive landscape in AI development that informs strategic decisions about adoption and investment.
• The Power of Prompts: Why prompts work in directing AI behaviour and how to craft effective prompts for different purposes.

Beware the thinking traps

'Delegation dodging' — the habit of avoiding delegation because "it's faster to do it myself" — often stems from thinking traps like lack of trust or fear of failing at more complex tasks.

A similar dynamic occurs with AI adoption, where people hesitate to integrate AI into their workflow after initial training because they perceive that learning a new workflow will set them back.

The goal is to create a system of co-intelligence between you and AI, learning to collaborate rather than focusing on 'correct' use.

There's no perfect way to use LLMs yet, but experimenting with smaller, low-stakes tasks — like drafting emails or reports — can help build AI literacy before applying it to high-stakes projects, strategic decisions or products.

Think of it as finding the right fit between your working style and the technology, understanding the trade-offs between adapting your methods or the tools themselves.

Address security and ethics

The era of blanket AI bans is over.

AI literacy must go beyond technical skills to cover ethics, including fairness, transparency, and accountability. Users need to understand the impact of AI decisions and actively mitigate risks. Addressing biases is key to ensuring fair and inclusive outcomes, making equity a central part of AI education.

Sustainability is also a growing concern, especially with the energy demands of large AI models. Raising awareness of AI's environmental impact can help organisations adopt more sustainable practices.

A strong AI policy should include safe practice guidelines, clear rules on data usage, and myth-busting around what AI does and doesn't do with your data.

We've found teams want the following:

• What an AI policy should include
• Safe practice guidelines
• What data can be used where and when
• Myth-busting (what AIs do and don't do with your data)