Over the past year, I've guided 500+ entrepreneurs through their first AI business projects, and they all start in the same place: overwhelmed by AI possibilities, unsure where to begin, and afraid of choosing the "wrong" thing.

Makes sense. AI entrepreneurship is a HUGE field. Where to begin?

In my eyes there are really three main approaches.

First, you can become known—build an audience, create content, establish expertise. Second, you can build—create tangible solutions like automations, apps and agents that businesses pay for immediately. Third, you can teach—run AI workshops for businesses showing them how to implement tools.

All three are valid paths to fast (and good) cash. They're not mutually exclusive—I actually do all three, and they complement each other brilliantly.

In this Playbook, we're focusing on building. And specifically building automations because they're the fastest to start and give you a tangible product that supports the other approaches.

I'm going to show you why simple automations are your best starting point and how they lead to real revenue faster than any other AI approach.

Let’s get started

Starting Where Others Quit

The most consistently successful AI entrepreneurs don’t start with the most impressive projects. They didn't build sophisticated AI agents or create viral content tools.

They build boring solutions to obvious problems.

One of my earliest students was spending 10 hours each week manually entering leads from web forms into their CRM, then creating personalised follow-up emails. Their first automation? A simple workflow that did exactly that—captured form data, added it to Salesforce, and sent customised responses automatically.

She built it for herself initially and then spun it out as a service for others.

Client willingness to pay: £750/month

Time saved per week: 10 hours

Time to deploy for client: 90 minutes

Was it sexy? Decidedly not!

It was mundane. But valuable. And something that (importantly!) businesses were willing to pay her to implement for them.

The Elephant in the Room: Why Not Agents?

Before we go further, let's address what everyone's thinking: "Kyle, why shouldn't I start with AI agents? They're obviously more advanced, more impressive, and potentially more valuable."

Fair question. Here's the reality about AI agents in early 2025.

They're unpredictable. Agents can handle complex scenarios brilliantly—until they encounter something unexpected. And when they go wrong they go really wrong. Then they can fail spectacularly, potentially damaging client relationships.

They're complex to build. Getting an agent to work reliably requires understanding prompt engineering, error handling, and often custom development. That's months of learning before you create value.

They're also hard to justify financially. When an agent costs £5,000-£10,000 to build properly, you need clients with substantial budgets and patience for testing. That’s all well and good but trickier early doors because you don’t have a track record yet.

Compare this to automations: they work consistently every time, they're built using proven tools, they solve immediate pain points, and they generate revenue within days, not months.

Think of agents as the sports car of AI solutions—impressive but impractical for most daily needs. I remember once (unthinkingly) taking a 911 Turbo to Wholefoods and trying to load the stupid thing up with shopping. Doesn’t work.

Automations on the flip-side are the reliable station wagon/ estate car that shows up every day and gets work done.

And guess which one businesses are going to be more willing to pay you for? Automations ftw.

Why Automations First?

Automations represent the fastest path from idea to revenue in the AI space.

When you automate a manual process, the value is visible from day one. No waiting for "breakthrough" moments or subjective interpretations of success. Automations either work or they don't. There's no grey area, no variables to negotiate, no "but what about this edge case?" discussions.

The financial logic is crystal clear: time saved multiplied by hourly rate equals exact monetary value. I will literally work this out with a client:

“How many hours do you spend on this problem each week? 10? OK cool and what’s the staff member’s hourly rate roughly? £50/hour. OK so that’s 10 hours per week, 40 hours per month at £50/hour which is ~£2000 right?”

We then show them how we can take that work off that staff member’s plate forever.

And the great thing is that every business has repetitive tasks they hate. Even the most exciting sounding businesses have boring busywork.You're not creating a new market—you're solving known problems that already cost money. This is key (and we’ll explore in more in the next Part).

What makes this even better? Starting with automations doesn't limit your future options. It actually enables them. Once you understand what businesses really need (not what you think they need), building AI agents, creating content, or running workshops becomes much much easier—and more profitable. Think of automations as the jumping off point in solving problems using AI. And later we’ll just add in more sophisticated methodology like agents.

The Boring Solution Framework

Here's the secret that most AI entrepreneurs miss: boring problems are goldmines.

This will be key moving forward. I’m going to help you identify the dullest (and most profitable) problems to solve.

Nothing shiny and exciting.

In fact if you explain you automation to something and they say “wow that sounds really cool!” then we shouldn’t want to do it. Boring is the watchword!

While everyone's chasing sophisticated AI implementations, there's real money in solving the tedious tasks that waste time every single day. Boring → profits.

Simple solutions often command higher prices and generate more revenue than complex ones. Why? Because simple solutions work reliably without supervision, scale effortlessly across teams, require minimal maintenance, and deliver immediate ROI that justifies the cost.

They are also much easier to sell. It becomes a much simpler discussion and a yay/nay. When you can say "This automation will save your team 10 hours per week starting tomorrow," the sale becomes obvious. When you say "This AI might improve your creative process with some training and optimisation," the sale becomes complicated.

We always lean towards simplicity. Even if it’s boring! 😛 

Taking the First Step

The hardest part isn't building automations—it's choosing to start with something simple when infinite possibilities exist.

I’ll be shouting this from the rooftops forever. And y’all still do something overly complex!

So: I’m here to continue to harangue everyone! Simple first, then add complexity if needed.

Remember, automations aren't your final destination in the AI world—they're your entry point. So we start here. For now. Once you've built a few successful automations, you'll have revenue, client relationships, and practical experience that makes every other AI opportunity easier to pursue. But all that cool extra stuff comes later - after the first $ or £ is made.

So we’ll start off by nailing down a specific problem that we are going to solve.

Next up we'll explore how to identify these golden automation opportunities hiding in plain sight within your own industry. I'll show you a framework for extracting valuable problems from your professional experience—the ones you're so familiar with that you don't even recognise them as business opportunities anymore.

Spoiler: your biggest advantage isn't your technical knowledge. It's understanding exactly what drives people in your field absolutely bonkers every single day. That’s a superpower.

Here's what we cover in this Playbook:

Part 1: The Power of Simple Automations - Understanding why simple solutions often win over complex AI projects, why automations beat agents as a starting point, and the secret value in "boring" solutions that generate immediate revenue.

Part 2: Your Industry Expertise is Gold - How your existing knowledge of your field's pain points gives you an unfair advantage, plus a framework for extracting profitable automation ideas from your professional experience.

Part 3: From Problem to Working Blueprint - Using AI to transform messy business problems into clear automation designs, avoiding common over engineering pitfalls that kill first projects.

Part 4: Tool Selection: Starting Smart - Cutting through the noise on Zapier vs Make vs n8n, why we recommend beginning with the simplest option, and when to consider upgrading your tools.

Part 5: Scaling Your Automation Business - How to evolve from single automations to complete solution suites, strategic pricing for packages, and knowing when to add technical complexity.

Keep Prompting,

Kyle