Welcome to another edition of Leading Momentum, where we cover on-the-ground insights leaders need to know about getting AI operations to work in their organization.

We’ve got another special send for you all today. We just published our latest video on a playbook Rachel uses daily to organize her workday - AND in it, we talk through the 3 steps to connecting tools to AI.

So without fail, one of the first things that happens when we start working with a new company is someone on the team asks how to connect all their tools to AI.

Every single time.

And I get it.

It feels like the natural next step.

You've decided to take AI seriously, so now you want it plugged into your project management, your email, your CRM, everything.

And every single time, we say the same thing: not yet.

Not because connecting tools is wrong. But because most teams skip the step that makes connecting tools actually useful.

A team has connected AI to a handful of tools already. Maybe someone set it up, maybe a few people each did their own thing. And when I ask what it's doing, the answer is... kind of vague. There's a mix of tools connected to AI, some standalone automations, some manual processes that were supposed to be automated but never got finished. Half-built things. Nobody's totally sure what's running and what isn't.

And here's the part that should matter to you if you're running the company: you probably don't have visibility into any of it.

You don't know what's connected, what data is flowing where, or what AI is actually doing inside your tools right now.

That's not an AI problem. That's a governance problem.

We've used variations of this with clients, but the simplest way I can put it: you wouldn't hand a new hire your passwords on day one before they know what they're doing there.

But that's what most teams do with AI. They give it access to everything and then wonder why it's not producing anything useful.

Before you connect anything, write down what you actually want AI to do. What steps, in what order, using what information. Not in the tool. In a Google Doc. In plain language. That's the work.

When we get teams to do this first, the connecting part becomes almost anticlimactic. They plug in the tool and it just works, because the playbook already defined what it should do. The team goes "wait, that's it?" And yeah. That's it. The playbook was the hard part. The tool was never the point.

The companies that skip this step don't end up with AI operations.

They end up with a mess of half-built things and nobody knows what's actually running. More time tinkering, overcomplicated workflows, and eventually the whole thing stalls.

The ones that start with the playbook connect fewer tools and get more done. Every time.

So if your team is using AI right now, or about to start, it's worth asking: do you know what they've connected? And more importantly, do they know what they want AI to do before they connect it?

If the answer to either one is fuzzy, that's the place to start.

Hit reply and tell me: does your team have AI connected to tools right now? Do you know which ones? Have questions about what to do next? Happy to be a resource.

Thanks for reading. It's a privilege that you spend time with these!

Whenever you're ready, here's how we can help:

Cheers,
Rachel