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Hey there, Creator!

I’ve been sitting on something so big that I feel like I should have paid rent for the extra storage space.

For the past couple of months, I’ve been in the AI Automation startup trenches, having conversations so intense they might as well have come with a disclaimer: Warning—may cause excessive brainstorming. I’ve spoken to a half dozen founders, content creators, and entrepreneurs who are actively building the future, one automation or app at a time.

Meanwhile, my home office has transformed into a makeshift war room. Picture this: me furiously scribbling down notes, playbooks, and hidden opportunities like a caffeinated detective solving the case of "Where’s the Money in AI?"

Here’s the kicker: while Twitter is busy recycling the same tired “AI will change everything” headlines, the real opportunities are chilling in plain sight. And guess what? They’re not flashy or trending but bringing in the cash.

TL;DR: I’ve uncovered 10 boring AI opportunities to enhance your business in 2025, and I’m about to spill all the beans—no gatekeeping, no fluff, just the good stuff.

And yes, creators can and should take advantage of these opportunities!

Let me break down exactly what I see in the market, playbook by playbook...

Let's. Crawl. In. 🦎

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The AI Consumer App Playbook

Here's something fascinating I noticed in my founder calls:

The biggest wins aren't from building new things but from reimagining what already works.

The formula is surprisingly straightforward:

• Find a winning app from the last 5-10 years

• Look for any mechanical data entry or manual output reading

• Add AI to eliminate that friction

• Use short-form video (especially TikTok) as your viral mechanism

Want to know something wild?

One founder found massive success simply by tackling the calorie-tracking problem. Instead of manually logging calories, they built an app that estimates calories from photos.

Is it perfect? No. But it's "good enough" for most users, and its popularity is exploding.

The key isn't perfection - it's removing friction.

Decrease Friction

Most AI tools are built by technical people who love to tinker with systems. However, the problem is that most users are completely the opposite.

The playbook I'm seeing work:

• Find tools people use DESPITE hating them (that's your gold mine)

• Rebuild that tool but remove 90% of the options

• If you can completely remove prompting, even better

• Focus on ONE core function that delivers value

I've experienced this firsthand. Most automation tools still feel like they're built for engineers. The opportunity to simplify here is insane.

Quick reality check: If you need users to learn prompting, you've already lost 95% of your potential market.

Check social media comments on popular apps (especially on X), App Store reviews, and builder Discord channels to spot what frustrates users.

If you have an audience, running a quick survey works great, too.

Vertical Domination

This one's counterintuitive, but stick with me:

Seeing tons of AI products in a niche is great news—it means there's huge demand and traffic.

A great example is AI copywriting tools. There are thousands of them.

But how many are specifically built for screenplay writers? How about personal brand newsletters?

The strategy breaks down like this:

• Look for a booming product

• Niche down until it hurts (I mean REALLY hurts)

• Go hyper-focused on one specific audience

• Own that micro-market completely

The goal isn't to be the best - it's to be the only one. When you're the only AI tool specifically for newsletter writers who focus on personal branding, you don't have competition. You have created a monopoly.s

The Hardware Revolution Thesis

This is the one that keeps me up at night:

While everyone builds software, the real opportunity might be in the physical world. I recently discovered something wild - a teddy bear running a local LLM that tells children stories. Sounds like Black Mirror, right?

But this points to something bigger. As AI hardware becomes more accessible, we can see intelligence embedded in the most unexpected places.

The question to ask yourself:

• What physical products would be genuinely improved by having a voice?

• What objects could benefit from understanding context?

• Where could local AI processing create magic?

The moat here is massive. Yes, it's harder than building another SaaS tool - but that's exactly why the opportunity is so big.

Speaking of opportunities that most are missing...

AI Accountability

A pattern that became too obvious to ignore during several founder calls:

All these apps using human accountability - fitness coaches, nutritionists, language tutors - are ripe for disruption. But not in the way you might think.

The framework is simple:

• Replace human accountability with AI voice models that call you

• Let users choose personalities (strict, funny, wise)

• Focus on areas where immediate feedback matters most

Imagine an AI nutritionist calling you daily about your MyFitnessPal entries. Not just app notifications - but a voice checking in on your meals, suggesting better choices, and keeping you on track toward your goals.

I'm seeing early movers crushing it in this space.

No-Code Infrastructure

Something massive is happening in the no-code world:

Tools like Cursor and Replit are exploding, but here's what most people miss - there's a whole ecosystem of opportunity around them.

One entrepreneur I spoke with built a simple tool that converts app screenshots into prompts for Replit. The result? Nearly 1:1 builds without writing code. That's just scratching the surface.

The step-by-step approach:

• Watch where beginner devs struggle with tools like Cursor/Replit

• Build micro-SaaS solutions to solve those specific pain points

• Focus on making the complex feel simple

The Enhanced Productivity Play

This one's fascinating because it combines trusted frameworks with AI power:

Instead of inventing new productivity methods, smart founders are enhancing proven ones:

• Take frameworks like Atomic Habits or GTD

• Add AI for personalized accountability

• Build in learning capabilities so the AI improves its advice over time

• Focus on maximum output through personalization

The same approach works beyond productivity - think personal finance, fitness, learning... anywhere people already trust established methods.

Voice Interface

Picture this: You're driving, and instead of struggling with a flight booking website, you're just conversing with an AI agent who handles everything.

The opportunity here isn't just in converting existing apps to voice - it's in reimagining entire experiences:

• Focus on situations where typing is inconvenient

• Look for tasks people do on the go

• Think about activities where hands-free interaction makes sense

Just like some people prefer audiobooks to reading, some users prefer voice-first software.

The key insight? Different formats for different contexts. Not everyone wants to type or tap all the time.

The Unbundling Movement

Here's a pattern that keeps showing up in my research:

Most AI tools are trying to be everything to everyone. But guess what? The real success stories are doing the exact opposite.

Take Magnific as an example. They didn't try to build an all-in-one AI image suite. They focused on one thing - upscaling - and dominating their market.

Here's how successful founders are doing it:

• Find popular AI tools that are bloated with features

• Identify their most-used feature (usually just 1-2 that people care about)

• Strip everything else away

• Make it faster and cheaper than the competition

You don't need 100 features. You need the RIGHT feature executed perfectly.

The Middle-Man Killer

This one's particularly exciting - I recently spoke with a founder who's building something fascinating:

They're using AI to help brands find and reach out to perfectly matched creators, eliminating the need for traditional agencies.

Check them out here (no affiliation)

Here's the exact blueprint:

1. Find tiresome processes typically handled by agencies

2. Look for workflows that can be automated through scraping/mass data analysis

3. Build AI agents to replace the middle-man steps

4. Sell directly to brands

The opportunity? Anywhere, there's a middleman charging premium fees mainly for administrative work.

Knowledge Productization

This last one is a sleeper hit that's about to explode:

Imagine taking a sommelier's lifetime of wine knowledge and turning it into an AI service. Or a master chef's understanding of flavor combinations. That's where this is headed.

The playbook:

1. Help experts extract their domain knowledge

2. Train specialized models with their expertise

3. Create subscription services around this knowledge

4. Focus on hyper-specific niches where generic AI falls short