We’re doubling down on “ops” in AI Ops (and you should too)

We’ve LOVED the warm welcome back - thank you everyone!! One of the questions we got the most of was “Can you elaborate on what you mean by “ops” in AI?”

So here’s the thing – 

After spending the last 12+ months working alongside teams trying to “get AI,” we’ve seen… a lot.

The one slide in Q3’s strategy deck.

The scattered experiments happening in random corners.

The big announcements of “AI Councils” and “Task Forces.”

And hey — we love the energy and intent. But if your team’s approach to AI feels more like a sci-fi side quest than a clear process you can start on Monday, something might be off. 😉

What’s really going wrong (spoiler: it’s not the tech)

Why is it that even after all the AI advancements of the last few years, the day-to-day inside most companies is still business as usual?

We’ve noticed a few of the same repeating patterns stopping teams in their tracks, regardless of company size and industry:

• Random acts of AI

• Shiny tool syndrome

• No clear ownership with AI adoption

• The “we must get our processes perfect before using AI” trap

Notice how none of these are tech or tool problems?

They’re operational problems. The exciting thing about operational problems is that having the right people and the right processes usually solves them. (More of this in our next email!)

What are your biggest challenges with AI Ops right now?

Again, we’re doubling down on ops. Even our AI Exchange Membership is fully focused on it. And we’d love to help you get unstuck!

Submit your biggest AI Operations challenge here – we read every submission, and if we think we can be helpful, use them to shape future newsletter issues.

What’s Coming Next

In our next email, we’re going to be sharing the three AI Operation’s roles we’re seeing emerge inside teams and allow AI implementation efforts to overcome these challenges with way less friction. We can’t wait to see which one has your name on it.

Cheers,

🪄 The AI Exchange Team