AI Automation is Hard until you learn this — Transcript

Everyone says AI Automation is easy now, just add an agent, just connect the apps, just automate the workflow, or worse, you have been sold a dream.

It's also a complete lie.

If you've actually tried to do this, build a real automation that runs inside a real business, you already know that it breaks, that it hallucinates, and also that it gets stuck.

But more importantly, instead of saving you time, you are now babysitting a robot that was supposed to replace work.

So here is the quite uncomfortable truth or reality that no one seems to want to say out loud: AI automation is hard, but not for the reason you think.

It's not because you don't know Python, and it's not because you picked the wrong tool, and probably also it's not because AI isn't ready yet.

It is, AI automation is hard actually because you are skipping the only part that actually matters.

And until you understand this one thing, every automation you build will eventually fail.

I know it sounds harsh, but bear with me.

I have the solution as well.

There is a name for what is happening, a Forrester analyst recently called it the AI Automation Fallacy.

And the fallacy is simple actually: we assume technology fixes broken processes, it doesn't, what it does is it just speeds them up.

So if your manual process for onboarding a client involves you checking three different Slack channels, guessing which email template to use, and just knowing that Bob from accounting needs to be CC'd on Tuesdays, no AI can replicate that.

That is not a process, that is vibes, it's gut feeling.

Okay, most workflows don't actually exist on paper, this is what we have learned from working with dozens and dozens and dozens of clients.

Most of the time, processes exist like, I just know when something feels off, or, oh, ask Sarah, she knows, or, we don't usually do this, except when so and so happens.

That's not a process, that is human judgment pretending to be a system.

So when you try to teach an AI to do that, it fails because the instructions are invisible, they live in somebody's head, whether it's yours or somebody else's.

And this is why 80% of automation projects stall, not on code, but on clarity.

And guess what happens when leaders rush to automate without understanding that reality?

Well, three things happen.

First, the people who actually understand the work, they feel threatened, so they stop teaching the system, they hoard knowledge.

Not out of resistance, but of self-preservation.

Number two, companies automate tasks, not workflows.

They make one step faster, like drafting an email or writing code, but they don't change how the work moves through the system.

So one person becomes faster while everything else downstream stays at human speed.

And that is because people confuse productivity with efficiency.

If AI helps one person write code 30% faster, that looks great on a slide, but the work still has to move through approvals and QA and compliance and decision points.

So nothing actually ships faster.

You did not build a more efficient company.

I'm sorry to say that, but what you did was just build a faster bottleneck.

Across industries, we see the same pattern.

AI boosts task speed, but output barely moves.

Why? Because systems move at the speed of their weakest link.

And most businesses, they don't have a tech problem.

They have a clarity problem, like I said.

And number three, and this is the part that no one wants to talk about.

Blind automation at scale is economically destructive.

You cannot build a healthy economy by optimizing margins while hollowing out the workforce that buys your products.

There, I said it.

So if there's one thing that I believe everyone needs to understand is a reframe that has changed, at least for me, everything.

You cannot automate outcomes, you can only automate decisions.

You cannot automate decisions you haven't made explicit.

This is the part that people skip.

Because it's not shiny.

It doesn't feel like doing AI.

But it's the difference between automation that compounds and automation that collapses.

And I call this the Document-First Principle.

It's so simple.

You must map the territory before you build the road.

That's it.

Before you automate anything, you must do three things.

Number one, map the process exactly as it exists today.

Number two, optimize it by removing ambiguity and vibes.

And number three, only then automate it.

If you reverse that order, the system will fail.

Every time, I can promise you that.

We talk about this so often in our founders hive, and I see it with our clients so often as well.

So, to make this practical, we need to stop thinking about automation as one big thing.

Because it's actually a pyramid, okay?

And most of you are trying to jump to the top without building the foundation.

So level number one is tasks.

This is the low-hanging fruit, okay?

Drafting a single email or summarizing one meeting.

This is the part where most people start, and it's great, but it doesn't scale.

Because it still works one-to-one.

Level two is processes.

This is connecting those tasks into a chain.

Email, CRM, Slack, this is where the real time saving happens.

But also where the complexity explodes.

And level three, the systems.

This is the Iron Man level.

And here I'm talking about intelligent agents that don't just follow a line, but are able to make decisions.

They monitor the data, they adapt, and they act like teammates, not bots, teammates, okay?

Systems that reason, that adapt, that escalate to humans when judgment is required.

But most people try to jump straight to level three.

Without actually building level one and level two properly before that.

That is why automation feels so fragile.

And once you understand this, the tool choice becomes obvious.

You don't actually need more AI, you need a place to see the whole system.

A place where human steps are visible, where decision points are explicit, and automation grows out of clarity, not chaos.

And that is why tools like Zapier's Canvas exist.

And it's my favorite new tool because it forces you to be honest about your workflow.

I'm not building the automation yet, I'm just drawing it.

Before you build anything, you need to map it.

And Zapier Canvas forces that honesty.

Because you're able to see where humans decide, where rules exist, where the machine can help, and where it absolutely cannot.

That alone eliminates probably more than 80% of automation failure.

So, let me make this super, super concrete.

Remember the Document-First Principle?

We need to map it, okay?

So here's a simple example: client onboarding.

I've talked about this before, I like it because it's a process that a lot of companies miss.

Most people describe it like this: I get an email, I see if they're a good fit, and I reply.

That is not a process.

So, in Canvas, we need to map it: email arrives, check budget, check industry, decide priority, draft response, notify the team.

And watch this.

I tell the AI what my messy process looks like.

And boom, it lays it out.

But look here.

This step is where I decide if a client is high value.

That's a black box, okay?

The AI cannot do that unless I define the rules.

AI cannot automate vibes, like I said.

So, right here, in Canvas, I can add a note that says, if budget is over X and industry is Y, mark it as high value.

Now it's logic.

Now it's automatable.

This visual map is your blueprint, without it, you're building a house without architectural drawings.

Let's be honest.

Now, we are ready to climb to the top of the pyramid, okay?

So level three are the systems.

This is where most people get it wrong.

The goal is not to replace the humans, the goal is to replace the part of their job that never should have required a human in the first place.

I call this one the Iron Man strategy.

Because you're not replacing yourself, you're just building Jarvis.

Iron Man didn't disappear when he built Jarvis, right?

He became more human, actually, because he was able to take better decision.

To have faster thinking and clearly higher leverage.

Standard automation is linear.

A happens, then B happens.

But real business is messy, sometimes you need judgment.

And that is what agents are for.

Because unlike linear automations, agents can reason, they can make decisions, okay?

They can read live data and apply judgment rules and draft outputs and escalate to humans when needed.

So, for example, let's take lead manager agent.

It is able to monitor new leads, to research the company, to draft a personalized message that goes back to the lead.

But it doesn't send it, okay?

Ideally, it hands it to a human for approval.

You do want to have the human in the loop.

This is the sweet spot, okay?

AI for speed, but humans for the judgment.

So, if AI automation feels hard right now, I have an assignment for you.

Don't open ChatGPT.

Don't open a code editor.

Just take one annoying process, whether it's in your own business or in some other business that you're familiar with, and just spend 15 minutes mapping it.

On paper, on Zapier's Canvas.

Wherever you want, on a napkin.

And find the hidden decisions and turn those vibes into rules.

Because once you do that, the automation almost builds itself.

Zapier has made the tech invisible.

The thinking is, however, still your job.

Look.

AI automation is not failing because AI is not ready.

It's failing because most businesses were never designed clearly enough to automate.

AI does not replace people.

It just exposes whether your thinking was never clear in the first place.

So build Iron Man suits, not layoffs, and you will win the next decade.

Thanks to Zapier for partnering with us on this video.

I have linked the Zapier Canvas below so you can play with it.

And start from clarity, not from chaos.

Now go build something meaningful.

Thank you so, so much for watching.

If you want to take this further and learn a lot more about AI and automation.

You are always more than welcome to join us in our free school community.

The link is going to be here as well as in the description below.

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Or take advantage of any of the free challenges that we have there.

There are probably 10 or 11 at this point.

You are able to choose whichever suits you best.

We also have a way to recommend something to you if you don't know where to start.

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I hope to see you on the other side.

And until next time.

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Until next time.

I suggest you go ahead and watch this video over here.

And I'll see you soon.

Bye.