Part 4 - Automated riches?

Matching Solutions to Problems

The key to building solutions that actually work?

We don’t start with the technology. We start with the (all together now!) business problem!

We don’t sell AI. We sell solutions.

And we start the simplest approach that could solve the problem.

Traditional Automation Examples:

• Automatically creating invoices when projects complete

• Moving leads through sales pipelines based on actions

• Generating weekly reports from database queries

• Scheduling social media posts

AI Automation Examples:

• Customer service responses that understand intent and provide personalised answers

• Content moderation that identifies problematic posts for human review

• Lead scoring that analyses emails and website behaviour to prioritise prospects

• Document processing that extracts key information and routes for approval

AI Agent Examples:

• Research assistants that can gather information from multiple sources and synthesise insights

• Project managers that can allocate resources and adjust timelines based on changing conditions

• Personal assistants that can book travel, manage calendars, and handle complex multi-step requests

Notice the progression? Each level handles more complexity and ambiguity, but also introduces more potential failure points!

If a problem can be solved with a lower level, less complicate automation always do so. Do not add complexity for the sake of it. Even if you can charge a client more! It’s a disservice to over-engineer a solution.

Here's where most people go wrong: they see AI capabilities and immediately want to use them everywhere.

Customer inquiry about store hours? "Let's build an AI agent that understands natural language and can handle complex conversations!"

Or... you could just put your opening hours on the website and set up an auto-responder… that’ll get the job done. Probably better!

The sexiest solution isn't always the best solution. If traditional automation solves 80% of the problem reliably, start there. Add AI only when simple rules genuinely can't handle the task.

But what problems should you actually be solving? Let’s hop into that.

Your Industry Advantage

Remember what we covered in Parts 2 and 3? Your existing industry knowledge is your unfair advantage.

If you're in marketing, you know agencies spend hours creating client reports. If you're in operations, you've seen procurement teams drowning in vendor comparisons. If you're in sales, you understand how much time goes into lead qualification.

That intimate knowledge of business pain points is worth more than any technical certification.

So when starting to build and sell automations to businesses we start here. Within our industry, where we have a huge competitive advantage compared to generic automation providers.

Before building anything, map out the problem on paper:

1. Define the exact problem: "Sales team spends 15 hours weekly qualifying leads from website forms". If you can’t define the problem simply it’s probably not a problem.

2. Break down current process: "Receive form → Research company → Check budget fit → Score lead → Assign to rep"

3. Identify decision points: Which steps are pure rules? Which need human judgment?

4. Choose your approach: Can this be solved with simple logic, or do you need AI for interpretation, creativity and to deal with complexity?

Solution Design Framework

Once you’ve worked out a basic problem let’s run it through a prompt to see what sort of solution is apt.

Here’s a quick interview prompt to get your expertise into an AI:

You are a business automation consultant helping me design the right solution for a specific problem. Interview me about my business problem and recommend whether it needs traditional automation, AI automation, or a full AI agent.

Ask me about:
1. The exact problem and current manual process
2. How often this happens and time spent per instance
3. What decisions or interpretations are needed
4. How much variability exists in the inputs/outputs
5. What level of accuracy is required

Then provide three solution approaches:
- Traditional automation (simple rules-based)
- AI automation (rules + AI where needed)
- AI agent (full reasoning and planning)

For each approach, include:
- What it would look like
- Pros and cons
- Estimated complexity to build
- Reliability expectations

Recommend which approach to start with and why. Focus on solving the problem as simply as possible while being genuinely helpful.

This forces you to think through the business logic before getting caught up in technical possibilities. We need a sense check to save us from ourselves!

From Problem to Payment

Remember the CEO who wanted an AI agent? After mapping out their actual problem, we built a simple automation using their existing tools. Took two weeks instead of six months, cost 80% less, and worked reliably from day one.

They paid happily because we solved their expensive problem efficiently. The technology choice was invisible to them - they just cared about the outcome.

That's what businesses actually buy: solutions to expensive problems, delivered reliably.

Identifying profitable problems is far far (far) more important than the solutions you work on. I highly recommend spending more time on the problem and how you communicate and package it up than the actual first draft automation!

If you want to dive deeper into finding, building, packaging and marketing AI automations my 2-week AI Automation Accelerator with @brandNat takes you from problem identification to sellable product.

What's Next?

In the next Part we'll cover the final legitimate AI opportunity: teaching businesses how to implement AI effectively. This is where your knowledge from Parts 3 and 4 combines - you know how to talk about AI and you can build solutions.

We'll explore how to turn this expertise into high-value training and consulting that businesses desperately need. Like…all of them.