Last week I met with a young entrepreneur who had meticulously followed all the steps we've covered so far. He'd discovered problems, categorized them, and even validated a particularly promising one through extensive research and testing.

Tbf that’s much (much) better than most of his peers. They are all doing dropshipping or crypto or some-such.

"Great," I said. "So what are you building to solve it?"

He looked at me blankly. "I... don't know. I thought I'd just build an AI app."

Basically: “AI”. Followed by some hand waving.

This is where many aspiring AI entrepreneurs get stuck. They've identified a real problem, but they haven't thought strategically about the right way to solve it.

Saying “I’ll build with AI” is like saying “I’ll build with the internet” or “I’ll build with a computer”. Meaningless.

Not all AI solutions are created equal, and matching the right technological approach to your validated problem is crucial for success.

Let's get started

Matching Problems to Technology Approaches

Different business problems require different technical approaches, but here's an important insight: most problems can be solved using a sequential approach, starting with the simplest solution and only advancing to more complex ones when necessary.

Think of it as a ladder of sophistication

AI Assistants: The Entry Point

AI Assistants are often the best starting point for any problem. They're ideal for content creation, information synthesis, expert guidance, and customer service. Almost any business problem can be initially addressed with a well-crafted prompt and a simple interface.

Implementation is straightforward—you can start with a custom GPT or Claude configuration and a basic web form. I personally use Launch Lemonade as it also allows for paywalls and restricted access.

Many successful AI businesses began with nothing more than this and still generated significant revenue while validating their market.

For example, a legal document review problem could start as an assistant that analyses contracts when users paste them into a form. This might take just hours to set up but deliver immediate value.

AI Automations: The Next Step Up

Once you've validated your solution with an assistant approach, you might find users want more automation. Now's the time to move up to AI Automations, which excel at repetitive tasks, data extraction, and defined workflows.

Automations excel when we need to connect multiple tools and pieces of software together and pass data through AI.

Building on our legal document example, you might create an automation that not only reviews contracts but also extracts key terms, generates summaries, and then sends email alerts for problematic clauses—all without human intervention.

This step requires more technical setup but delivers greater value to users who've already proven they're willing to pay for the simpler version.

AI Agents: When Complexity Demands It

If your solution requires orchestrating multiple systems or handling complex multi-step processes, AI Agents become appropriate. But crucially, you should only advance to this level after proving market demand with simpler approaches.

Too many businesses rush to agents when it’s really not required. And by doing so they just create trouble for themselves by adding complexity.

Continuing our example, an agent might not just analyse and extract information from contracts but also compare them to previous agreements, update CRM systems, draft response emails, and schedule follow-up meetings, all whilst learning and improving itself via a centralised repository.

Full AI Applications: The Premium Solution

The most sophisticated approach is a full application with custom interfaces and tight system integration. This represents a significant investment but can command premium pricing—after you've proven the market wants it.

Our legal document solution might evolve into a comprehensive contract management system with dashboards, workflow management, and enterprise integration. But building this from day one without validating with simpler approaches would be extremely risky.

The key insight here is progression. You don't need to jump immediately to the most complex solution. Start simple, validate, then advance only as far as necessary to solve the problem effectively.

Building Your Solution

With this sequential approach in mind, here's how to build your solution

1. Start Simple, Always

Begin by creating the simplest version of your solution that delivers value

• For most problems, start with an AI Assistant approach

• Create a basic interface (even just a form input is fine)

• Focus on nailing the core problem-solving aspect, not fancy features

• Get it in front of customers as quickly as possible (days, not weeks)

Importantly: If this basic version isn’t valuable then simply adding MORE is unlikely to help much. Nail the core value.

This approach allows you to validate your solution with minimal investment. I've seen businesses generate six figures in revenue with nothing more than a well-engineered prompt and a simple form.

2. Observe and Learn

Once your simple solution is in the hands of users

• Watch how they actually use it (not how you expected them to use it)

• Identify pain points and limitations in the current approach

• Ask users, "What would make this 10x more valuable to you?"

• Look for patterns in feature requests and complaints

This real-world feedback is gold—it tells you exactly where to focus your development efforts next.

3. Advance Only When Necessary

Based on user feedback and business metrics, decide if and when to move up the sophistication ladder:

• If users want more automation and less manual intervention → Advance to AI Automation

• If they need integration with multiple tools or systems, all automatic → Consider AI Agents

• If they require custom interfaces and interconnectivity with their existing systems → Explore full applications

The key is to let market demand pull you toward greater complexity, rather than pushing toward it because it seems more impressive or technically interesting. Don’t just chase “that would be cool”.

4. Pricing Should Reflect Value, Not Complexity

Super important.

Just because it took you longer to make doesn’t mean it’s more valuable to the end user.

Customers pay for what they get, not for your pain and suffering. They don’t care.

A simple solution that solves a painful problem is worth far more than a complex one that doesn't. Price based on value delivered, not technical sophistication:

• A basic AI Assistant that saves 10 hours per week is worth roughly $2,000/month in value

• Pricing at 10-30% of delivered value ($200-600/month) is reasonable

• As you move up the sophistication ladder, increase prices in proportion to additional value

Flipside is this: Don't fall into the trap of underpricing simple solutions just because they were easy to build.

Creating Service Packages and Pricing

Once you've selected your technical approach, it's time to create service packages and pricing. The key principle: price based on the value your solution provides, not your costs.

Consider what your solution does for customers

• Time saved

• Revenue increased or costs reduced

• Risk or liability decreased

• Competitive advantage gained

For example, if your solution saves a business 10 hours of work per week at $50/hour, that's roughly $2,000 monthly in value. Pricing at 10-30% of this value ($200-600/month) would be reasonable.

The pricing model you choose—subscription, usage-based, outcome-based, or hybrid—should align with how your solution delivers value to customers. Do not base it on your effort! Remember: they don’t care!

Wrapping Up The Series

Over these five parts, we've covered a comprehensive framework for finding, validating, and solving real business problems with AI:

Part 1: The Problem-First Mindset - We established why starting with problems rather than technology is crucial for AI business success, and explored techniques for mining problems from your personal experience.

Part 2: Problem Discovery Beyond Personal Experience - We expanded our problem discovery to market observation and data analysis, building a robust Problem Bank of potential opportunities.

Part 3: Problem Categorization & Segmentation - We learned how to organize and prioritize problems to identify patterns and high-value clusters using a simple filtering process.

Part 4: Problem Validation & Verification - We explored methods to verify that problems are real, painful, and worth solving before investing in building solutions.

Part 5: Building Problem-Solving AI Businesses - We completed the framework with strategies for turning validated problems into cohesive business offerings matched to the right technical approaches.

The entrepreneurs who build the most successful AI businesses in 2025 and beyond will not be those with the most advanced technical knowledge. Quite the contrary.

It’ll be who become masters at finding and solving real, painful, validated problems. And remember, the bigger the problem the bigger the business.

Keep Prompting,

Kyle