AI Tool Ideas

Hey Prompt Entrepreneur,

Alright, real talk. The uncomfortable, keeping-you-up-at-3am kind.

Jobs are dying. Like...actually proper dying. Not in some far-off "robots are coming" way. Right now. AI is about to put a hole in the job market bigger than my coffee budget…

Check out this cheerful graph from the International Monetary Fund:

Those parabolic curves smashing into the ground? Those are wage levels if/when we get to AGI (Artificial General Intelligence).

Now, of course we can go back and forth about if/when AGI will get here. But…if I’m in a job looking at that chart I’m not going to wait for the theoretics to resolve. I need to do something.

"No worries!" you might say, "I'll just become an indie hacker!"

Well...about that. Indie hacking is dead too. Don't believe me? The godfather of indie hacking himself, Pieter Levels, called it:

It's not that indie hacking failed - it's that it succeeded so hard it became the default.

Everyone is now "building in public" and "launching fast". Hell, it’s been what I’ve been doing for the last few years too so I’m in the same boat.

What’s next up then? How to not just survive but thrive moving forward? Literally the $1M question right now.

It’s AI right?

Just build AI tools, right? Kyle is an AI guy and he’ll tell me AI is the answer.

Oh lord no.

Want to see something properly depressing? Go browse any AI directory. It's not just a graveyard - it's a mass grave of abandoned projects. My inbox is flooded with desperate founders begging for marketing help because their "revolutionary AI tool" is drowning in obscurity.

And these aren't bad tools! Some of them are actually brilliant. But the tool quality itself doesn’t matter.

Why? This is why:

80% Distribution, 20% product - that is what you need to pay attention to.

Because the market is busy. And about to become busier.

AI is allowing anyone to build…which means more competition, more noise and less chance of you building something that’ll hit.

Well crap. What’s the fix?

Let’s get started:

What’s next?

Welcome to the age of AI Entrepreneurship. This is the next big thing.

Nay: the Next Big Thing. Let’s capitalise it - it deserves it.

This isn't just another tech trend or market shift. This is a fundamental change in how businesses will be built and scaled.

What exactly is AI entrepreneurship? It's the sweet spot between two crucial capabilities:

1. The ability to build AI-powered solutions (which is getting easier every day)

2. The ability to get those solutions into the hands of people who need them most

And here's why it's happening now: building AI tools is becoming democratised. Those 8-year-olds with Cursor? That's just the beginning. Your nan will be spinning up web apps before she spins up her next batch of scones.

This is where AI entrepreneurship comes in. It's about being able to build AND get your solution in front of the right people.

Where do we start with AI entrepreneurship?? Your industry expertise is the key to both.

AI is overrated

Here's something that might surprise you: I don't actually care how much you know about AI.

Like...at all.

The number of people who've come to me saying "I've done all the AI courses, I know prompt engineering inside out, I've built three different ChatGPT plugins..." - and they're all struggling to make their first dollar.

Meanwhile, take Lucy, a procurement manager, who built a simple AI tool that analyses supplier invoices for her industry. Nothing fancy. She barely knew what ChatGPT was six months ago. Doesn’t matter. She knew her industry's problems like the back of her hand.

She's now charging £500/month per customer.

Why? Because she wasn't trying to "do AI". She was solving a real problem that she'd lived with for years.

This is the fundamental difference between success and failure in this space. Are you chasing AI trends, or are you solving legit problems?

Let me show you what I mean. Here's what most people do:

1. Learn about AI capabilities

2. Think "ooh, I could use this for X"

3. Build something cool

4. Desperately try to find people who want it

Silly. That’s the fast lane to the AI graveyard.

Instead we’re going to:

1. Look at your industry's problems

2. Find one that's costing people money or time

3. Check if AI could help

4. Build the simplest possible solution

See the difference? One starts with technology, the other starts with problems. One requires you to create demand, the other taps into existing demand.

How do you actually find these problems? Well, first up - start in your current industry! If you’ve been working in manufacturing, procurement, marketing, education or whatever your industry is for years then you are in a far better position to know what the problems are!

Let’s use a prompt to help with the process:

You are an expert business analyst focusing on process improvement and automation opportunities. Help me analyse my industry for AI implementation opportunities.

Industry: [Your industry]

Please help me identify:
1. Common time-consuming manual tasks in my industry
2. Processes that often result in errors or delays
3. Areas where data analysis is done manually
4. Tasks that require parsing unstructured information
5. Repetitive decision-making processes

For each identified area, explain:
- Why it's a problem worth solving
- Who specifically feels this pain
- Current ways they try to solve it
- Potential value of solving it (time/money saved)

This prompt is your starting point. Run it. Then run it again with different aspects of your industry. Build a list of problems that make you think "yeah, that's proper painful."

You, as someone in the industry are the ultimate arbiter. Not the AI! We’re using prompt to spark some ideas, that’s all.

But here's the crucial bit: for each problem, ask yourself:

• Have I experienced this personally?

• Do I know people who face this problem?

• Can I reach the people who have this problem?

If you can't answer "yes" to at least two of these, move on. We're not here to solve theoretical problems. We're here to solve real problems for real people that you can actually reach.

Next Steps

Over the next four Parts of this Playbook, we're taking our first steps into AI entrepreneurship by finding our unique advantage - the intersection of AI capability and market access through our industry expertise.

Here's where we're headed:

Part 2: Going Wide

We're going to generate every possible idea for AI implementation in your industry. No filter, no limitations - just pure possibility. Think of it as your industry knowledge having a wild night out with AI capabilities. We'll use specific prompts to dig out problems you might not even realise are solvable.

Part 3: The Reality Check

Time to get brutal. We'll slash through our ideas, eliminating anything that's got red flags around privacy, GDPR, or technical limitations. Better to kill bad ideas early than waste months building something we can't sell. This is where we separate the possible from the practical.

Part 4: Finding Your MVP

We'll zero in on the smallest, most focused solution we can build that people will actually pay for. Not the "wouldn't it be cool if..." ideas, but the "I'll pay you right now to solve this" opportunities. This is where we apply the "boring but valuable" filter.

Part 5: Your Project Brief

We'll wrap it all up by creating a crystal-clear plan for your first AI tool. You'll walk away with a focused brief that identifies exactly what you're building, who it's for, and most importantly - how you'll get it into their hands.

PS. If you’ve got this far we’re exploring launching a 30 Day AI Entrepreneurship Accelerator where we:

1. Hone a business idea

2. Build a focused AI tool

3. Test and refine the tool

4. Market and launch

Course, community and live sessions.

You know what drives me absolutely bonkers? When someone comes to one of my entrepreneurship workshops and proudly announces "I've got THE idea!"

Like...dude… You haven't even started yet.

I've run these workshops and courses for years now, and it's always the same story. Someone rocks up, clutching their precious idea like Gollum, completely convinced they've cracked it. Their idea is the one. The next big thing.

Hell, sometimes they’ll want me to sign an NDA because the idea is “so good”. Nothing tells me they are an amateur more than this…

And you know what? They're probably wrong. Almost certainly wrong.

Not because their idea is bad (though sometimes, bloody hell, they really are). But because they're already emotionally invested in it. They're wearing idea-tinted glasses, seeing everything through the lens of their "perfect" solution. They are primed to see what’s good about it and to ignore what’s bad about it.

Ultimately the thing about ideas is that they literally come from air. They're worth exactly nothing. Zilch. Nada.

Want proof? Let’s spin up some ideas for tools we could sell to marketing firms:

• AI tool that reformats messy client campaign data from Excel into standardised agency reporting templates

• AI system for checking Google Ads headlines against brand compliance guidelines

• AI assistant that converts monthly marketing metrics into "client-friendly" email updates

• AI tool for finding and flagging outdated brand assets across Google Drive folders

• AI system that compares competitor Facebook ad copy against past performance data to predict engagement

I just made those up. Right now. While typing this newsletter. They took me about 27 seconds.

Are they good ideas? Could be. Are they bad ideas? Probably.

But here's the crucial bit - what WE think doesn't matter.

Only the market and our customers can tell us.

Let’s get started:

First go wide

The reason we want loads of ideas is dead simple: most of them will be crap. And that's absolutely fine. Actually, it's better than fine - it's exactly what we want.

You see, when you've got just one idea, you're stuck defending it like it's your firstborn. But when you've got 20? 30? 50? It's much easier to be ruthless. To look at each one objectively and go "nah, that's rubbish" without feeling like you're killing your dreams.

It’ll be one of 50 instead of one of one. Much easier to murder.

Over the next few parts of this Playbook, we're going to be proper brutal. Privacy concerns? Bin it. Technical limitations? In the trash. No clear route to market? See ya later.

But to do that effectively, we need ammunition. We need ideas. Loads of them.

And not just any ideas - we need ideas that leverage both AI capabilities AND your industry knowledge. Remember what we talked about last week? Your industry expertise is your superpower. We start here.

Start with Problems

First up, let's use this prompt to extract every single pain point in your industry:

You are an expert business analyst specialising in [your industry].

Help me identify every possible pain point, inefficiency, and annoyance in this industry, no matter how small or seemingly insignificant.

Focus on:
- Administrative tasks
- Communication flows
- Data handling
- Decision making processes
- Customer interactions
- Resource allocation
- Quality control
- Compliance and reporting

For each area, list out:
1. What's frustrating
2. Who it frustrates
3. How often it happens
4. Current 'solutions' (even bad ones)

Do NOT filter or judge - list EVERYTHING.

Run this prompt multiple times, changing the focus areas each time. Build up a massive list of problems.

AI Translation

Now we're going to take each problem and brainstorm how AI could help. Here's our second prompt:

You are an AI solutions architect. For each of these industry problems, generate 3-5 potential AI-powered solutions. Include both simple and ambitious solutions. Don't worry about feasibility yet.

Problems: [Paste your problems here]

For each solution, specify:
1. What the AI would do
2. What input it needs
3. What output it provides
4. How it helps

At this point don’t worry about complexity or feasibility. We’re going to use these (and more) as filters as we proceed.

Go Ridiculous

This is where it gets fun. For every sensible solution you've got, generate a completely over-the-top version.

Use a basic prompt like:

Give me a 10x version of each of these ideas

What if money and technology weren't limitations? What if you had access to every piece of data in the world?

The reason we do this? Sometimes the ridiculous ideas lead to practical insights. Or they help us spot patterns we wouldn't have noticed otherwise.

Moving forward

By the end of this exercise, you should have at least 50 ideas. Yes, 50. If you don't, keep going. Remember - we're not judging quality yet. That comes later.

Most of these ideas are rubbish right?

Correct! Most of them will be terrible. That's the point. We're not looking for perfect ideas - we're looking for raw material.

In the next Part of this Playbook, we'll start filtering these ideas through various reality checks (privacy, technical feasibility, market access, etc.). A boring but important first pass filter!

Think of it like mining for gold. You don't expect every scoop of dirt to contain nuggets. And if you do you delusional!

But you need to move a lot of dirt to find the gold. Digging is necessary but not sufficient!

Get digging!

PS. If you’ve got this far we’re exploring launching a 30 Day AI Entrepreneurship Accelerator where we:

1. Hone a business idea

2. Build a focused AI tool

3. Test and refine the tool

4. Market and launch

Course, community and live sessions.

I was working with an NGO in Switzerland - one of those serious, UN-affiliated organisations. Proper big boys. The kind where "data privacy" isn't just a checkbox, it's an entire department.

They had this brilliant idea for an AI tool that would help process and analyse medical data to speed up assistance delivery. The potential impact was huge. Lives could literally be changed.

My client was buzzing with excitement. The use case was clear. The value was obvious. The technology was feasible.

And we had to kill it. Stone dead. Before we wrote a single line of code.

Why? Because when you're dealing with vulnerable populations and UN-level data privacy requirements, some doors are firmly locked. And they're locked for good reason.

It just wasn’t feasible because of the rules and regulations in place. The pain of killing that idea early was nothing compared to the pain we would have felt after spending months building something we could never deploy.

We’re going to hyper-accelerate this process in this Part and begin to reduce our long-list of ideas.

Let’s get started:

Brutality

In the last Part we went wild with ideas. We generated dozens of potential AI solutions for your industry. It was the business equivalent of a brainstorming party - no idea too small, no concept too ambitious.

Now comes the hangover. Time to get brutal.

The whole reason we went wide last time is that now we are going to aggressively narrow it down.

Because here's the thing about ideas in the AI space - they don't just need to be good. They need to be legal, ethical, technically feasible, and practically deployable. I know I know…you don’t want to hear it!

Let me be clear - I'm not some regulation-loving bureaucrat who gets excited about compliance documents. And I'm not saying these more complex ideas aren't doable. They absolutely are!

Almost any AI project can be made to work with enough time, money, and legal expertise. Need to handle sensitive data? There are frameworks for that. Worried about GDPR? There are compliance experts who can guide you through it. Got regulatory hurdles? They can be cleared.

All doable.

But here's the thing: for your first AI app, we want to move fast. We want to get something out there, get feedback, and start learning. We want to remove every possible roadblock between you and your first deployed solution.

Why? Because speed matters more than complexity right now.

Those complex, highly regulated projects? They're like trying to run before you can walk. They involve multiple stakeholders, legal reviews, compliance checks, special infrastructure... each one adding weeks or months to your timeline. No bueno.

Save those for later! We can absolutely loop back to those once you've got a few wins under your belt, once you're comfortable with the basic process of building and deploying AI solutions.

Right now, we're looking for the path of least resistance.

We want to be able to deploy in a week not a year. Yes, that fast.

Let's get our first win before we try to change the world. Cool?

OK, let’s use some prompts to help us begin the winnowing process. First up, everyone’s favourite!

Privacy and Data Protection.

GDPR isn't just some annoying popup on websites. It's a set of rules that can absolutely demolish your AI dreams if you're not careful. And it's not just GDPR - every region has its own flavour of data protection laws. California's got CCPA, China's got PIPL, and the list keeps growing.

Here's your first reality-check prompt:

You are a data privacy expert and regulatory consultant. Evaluate these AI solution ideas for potential privacy and regulatory concerns.

My ideas: [Paste your ideas here]

For each idea, identify:
1. What sensitive data might be involved
2. Which regulations could apply
3. Whether it's a red flag (stop), yellow flag (proceed with caution), or green flag (relatively safe)
4. What specific issues need to be addressed

Run this first. Any ideas that come back with red flags? Bin them. Right now. Don't even think about trying to find workarounds. That takes time and saps energy.

Technical Feasibility

Just because ChatGPT can write poetry doesn't mean it can do everything. Here's your technical reality check prompt:

You are an AI systems architect with extensive practical experience. Review these AI solution ideas for technical feasibility with current technology.

My ideas: [List remaining ideas]

For each idea, assess:
1. What AI capabilities are required
2. Whether these capabilities exist and are accessible
3. Known limitations or challenges
4. Whether it's a red flag (stop), yellow flag (proceed with caution), or green flag (relatively safe)

Yellow flags here are okay - technology moves fast. Red flags? Those ideas go in the bin too. We want to fill that bin up!

But we're not done. Now comes the practical reality check. For each remaining idea, ask yourself:

• Can I actually get the data needed to train/run this AI?

• Do I have access to the target customers?

• Do I have the required industry domain knowledge and experience or could I get it?

These ones AI can’t answer for you. They are personal to you.

If you can't answer "yes" to all of these, that idea goes in the bin too.

"But Kyle," I hear you say, "this seems really negative. We're killing so many ideas!"

Exactly. That's the point! We’re killing our darlings.

Remember that NGO project? Killing it early saved months of work and thousands of francs. More importantly, it saved us from the nightmare scenario of building something we couldn't deploy. Wasted time, energy and money.

By the end of this process, you should have eliminated at least 70% of your ideas.

If you haven't, you're not being brutal enough.

What remains are ideas that are:

• Legal and ethical to build

• Technically feasible

• Practically deployable

• Within your reach

In the next Part we'll take these survivors and identify which ones are actually worth building. Because passing our reality checks doesn't automatically make an idea good - it just makes it possible!

PS. If you’ve got this far we’re exploring launching a 30 Day AI Entrepreneurship Accelerator where we:

1. Hone a business idea

2. Build a focused AI tool

3. Test and refine the tool

4. Market and launch

Course, community and live sessions.

I spend a lot of time around AI builders.

Everyone's building. Literally everyone. My feed is full of "just launched" and "building in public" posts. It’s wonderful.

But here's the fascinating bit: I keep an eye on these projects (occupational hazard of being waaaay too online). And I'm noticing a clear pattern in who succeeds and who doesn't.

Take two creators I know - let's call them Alex and Sam. Not their real names.

Alex announced they were building an "AI-powered complete marketing suite". Content generation, campaign analysis, social scheduling, performance prediction, the whole shebang. Proper ambitious stuff. Set to revolutionise how global agencies work.

Sam? They built a tool that does one thing: takes YouTube transcripts and turns them into Twitter threads. Huh.

Guess who's actually making money right now?

Sam's pulling in $3k/month with their tiny, focused tool. Alex is still "building" six months later.

Let’s get started:

I watch a lot of AI projects fail. The pattern's always the same. Grand ambitions, feature creep, endless development. By the time they launch (if they launch), the market's moved on. Or wasn’t even there in the first place!

Meanwhile, the creators making actual money? They're the ones who picked one problem and solved it properly. Got to market quickly, proved their value and start generating revenue or move to a quick sale.

Not as exciting to watch, but actually gets the job done.

Here’s a great example:

Remember the Underpants Gnomes from South Park?

A simple plan:

1. Collect underpants

2. ???

3. Profit!

I see this ALL THE TIME in AI projects. Here's their version:

1. Build AI tool

2. ???

3. Profit!

Trust me, I've made this mistake myself. Multiple times.

Built something really clever, technically impressive even. I’ve been super proud of it!

But that step 2? The bit between building and profit? That's where most AI projects go to die.

And you know what? The solution isn't some complex business strategy.

It's brutal clarity before you start building.

Let’s get started:

Right, we've spent the last four parts finding and refining our idea. Now we're going to define it so clearly that even our friendly neighbourhood underpants gnomes could build it.

Your AI tool needs three things defined with crystal clarity:

1. INPUT: What exactly goes in? Not "marketing data" but "CSV export from Google Analytics with these 6 specific columns" Not "content" but "YouTube video transcript in .txt format"

2. PROCESS: What exactly happens? Not "AI makes it better" but "Extract key points and reformat into 5 bullet points" Not "optimise it" but "Remove jargon words and replace with plain English alternatives"

3. OUTPUT: What exactly comes out? Not "insights" but "PDF report using this exact template" Not "social media content" but "3 tweet thread in text format"

The more specific you can be now (on paper!) the easier our build will be.

Let's use this prompt to nail down your project brief:

You are a technical project manager specialising in AI implementations. Help me create a precise project brief for this AI tool.

My tool idea: [Your simplified, validated idea]

Define exactly:
1. INPUT
- What format(s)?
- What specific data points?
- Any size/format limitations?

2. PROCESS
- Step by step what happens
- What specific AI capabilities needed
- What transformations occur

3. OUTPUT
- Exact format of deliverable
- Specific contents included
- How it will be delivered

For each element, highlight any potential technical constraints or requirements.

Look at what comes back. Is anything vague? Anything that could be interpreted different ways? Anything that makes assumptions about what people know or want?

If yes, run this follow-up prompt:

You are a detail-oriented QA specialist. Review this project brief and identify any areas that could be misinterpreted or need more specific definition.

Project Brief: [Previous output]

Highlight:
1. Any vague terms or assumptions
2. Missing technical specifications
3. Undefined parameters
4. Areas needing more precise definition

For each issue, suggest how to make it more specific and testable.

Keep refining until every single element is crystal clear. Until anyone could read your brief and know exactly what goes in, what happens, and what comes out.

This should not be a confusing document - it should be practical and clear. This isn't just documentation - it's your roadmap for actually building the darn thing.

PS. If you’ve got this far we’re exploring launching a 30 Day AI Entrepreneurship Accelerator where we:

1. Hone a business idea

2. Build a focused AI tool

3. Test and refine the tool

4. Market and launch

Course, community and live sessions.