AI is rapidly changing how we work and live, but how do we stay in control and ensure it’s used responsibly?
AI researcher Ethan Mollick outlines four key principles in Co-Intelligence: Living and Working with AI to help us engage with AI effectively:
🔹 Always ask AI – AI is a powerful tool, and using it regularly helps us understand what it can (and can’t) do.
🔹 Be the human in the loop – AI isn’t perfect. We need human oversight to verify its outputs, correct errors, and make ethical decisions.
🔹 Treat AI like a person – Not because it’s sentient (it’s not), but because interacting with AI works best when you engage conversationally, refining and challenging its responses.
🔹 Assume this is the worst AI you will ever use – AI is improving fast. If today’s AI already feels impressive, imagine what’s next, we need guardrails now to ensure AI evolves in ways that align with human values.
But while these principles help individuals engage with AI wisely, what about organisations? How can businesses, charities, and governments ensure AI is fair, safe, and accountable?
That’s where AI Management Essentials comes in, a framework from the UK’s Department for Science, Innovation & Technology (DSIT) designed to help organisations use AI responsibly.
✅ It ensures AI use is documented – so there’s transparency around how AI is being used.
✅ It sets clear policies – to help teams decide when AI is appropriate and when human judgment is essential.
✅ It identifies risks like bias and security flaws – keeping AI aligned with ethical standards.
✅ It establishes accountability – so AI decisions don’t happen in a vacuum.
✅ It creates issue-reporting mechanisms – allowing people to flag AI errors or concerns.
Why does this matter?
Because AI isn’t just a tool, it’s a responsibility. If we want people to trust AI, we need clear guidelines and human oversight at both personal and organisational levels.
By following Ethan Mollick’s principles as individuals and AI Management Essentials as organisations, we can build a future where AI is used in ways that enhance, rather than replace, human decision-making.
What do you think? Do frameworks like this help build confidence in AI?