How to think about AI
Kyle Balmer
?? AI entrepreneurship || RISEN? prompt framework || International AI trainer || 60k newsletter readers || 8x best sellers || 200k audience || Forbes featured || Great eyebrows
AI is like magic.
Until you see it perform a bad trick.
So you stop using it.
This is a big mistake.
Keep the magic alive by thinking about AI like this.
When you first start using it in your business the natural reaction is “OMG this can do everything for me! I’ll be on the beach sipping Mai-tais in a week!”
Totally natural reaction!
We tend to go through this journey:
First up we’re excited - AI can do?any?task we throw at it in business.
But as we start to use it more we see the cracks and limitations.
ChatGPT starts producing generic responses, forgetting instructions or just making stuff up!
This destroys confidence. Some people even stop using AI at this stage - loudly proclaiming that “AI is an overhyped fad”.
The problem isn’t AI. It’s you!
We tend to i) expect too much and ii) not spend the time to learn the limitations of AI.
When AI doesn’t instantly do what our imagination expects it to we throw the baby out with the bathwater.
What’s the fix?
Instead, treat AI as a hyper-intelligent?human?assistant.
They’ve just entered the job and don’t know the context of your business.
If this was a human new hire you’d:
We need to do this with AI to get best results!
Don’t expect AI to read your mind and know exactly what you want!
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The key to this is?prompting. Or if yo’ fancy: “prompt engineering”
This basically means “how to talk to AI”. That’s it.
It’s a communication skill. Not a technical coding skill.
In the next Part I’ll give you a framework for the Perfect Prompt.
Right now let’s review a decision matrix for?what?tasks we should be using AI on.
What are the best task to use AI on?
So we’ve got over our initial excitement with AI and realised that it’s a hyper intelligent assistant that?still?needs some guidance to produce the best results.
This imposes limitations.
We can’t just use AI on everything. It would be a disaster.
Instead we need to be selective. We need to decide the?best?tasks to deploy AI on.
For this I’ve devised a decision matrix. Here it is:
On the axes we have:
Tasks that take a lot of time and money might be things like meetings, reporting, customer service and all the myriad of time-consuming tasks a business has. Simple.
How critical errors are is a little more complex. Basically errors are not built the same. Errors in some tasks are not the end of the world.
For example let’s say you prepare a daily report you send to a team-member internally. If there’s an error your team member will just send it back and say “hey think you missed something here”. Embarrassing? Sure. Mission critical? Nope.
A critical error is one that affects the business as a whole. For example maybe that report wasn’t a daily send to a colleague but instead an annual report sent to all your key investors.
In this case making a similar error could be disastrous!
Using the matrix we can categorise all our business tasks into:
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