How Procrastination Sneaks into AI Decisions

Lately, I’ve been thinking a lot about procrastination.

Not the “I’ll do it later” kind - that’s related to boredom, distraction or low stakes. But the kind that shows up when something really matters, you know it’s important and you also know you can’t escape it.

Some say this procrastination is a planning problem or even laziness. But if you scratch the surface, there’s something much deeper going on — especially when it comes to AI.

I listened to a podcast recently that framed it perfectly:

“Procrastination is an emotional regulation issue.”

Deep procrastination - “this really matters” kind - is not just about the task, it’s about what that task means about you. It taps into the fear that you’re not good enough… or worse, that you are — and then what?

Once you see it like that, you can’t unsee it.

Why AI triggers procrastination

If you’re a product leader, strategist, or innovation lead, chances are you’ve felt it: that silent pressure around AI.

You know it’s important.

You know decisions need to be made.

You’ve probably been asked, “What’s our AI strategy?” more than once.

But still… you’re hesitating.

You’re reading, researching, waiting. Maybe tinkering. Maybe ignoring it altogether. Maybe asking even more questions about it.

This isn’t indifference on your part. It’s avoidance.

When we look at the psychology of procrastination, here’s what we are desperately trying to avoid:

1. Fear of failure (what if I try and it sucks?)
2. Fear of success (what if it works and my life changes?)
3. Fear of judgment (what will others think?)
4. Perfectionism (it’s never quite ready to start)
5. Lack of clarity (I don’t know the next step, so I stall)
6. Identity threat (if this project doesn’t work, what does it say about me?)
7. Overwhelm (it feels too big to begin)

In fact, the bigger the stakes, the more we emotionally invest in the outcome… and therefore, the more likely we are to avoid starting at all. That’s why people often procrastinate most on the things they care deeply about.

These emotional barriers are common and predictable, and they become more active in moments of transition — like when your organization shifts from “AI curiosity” to “AI action.”

The hidden tax of avoidance

The most dangerous part when we avoid doing this big, important change is that avoidance doesn’t feel urgent.

You don’t get punished right away for not taking a step.

There’s no obvious cost. No failure you can point to.

But the tax is still there — it just arrives later.

In missed momentum. In half-baked pilots. In “we tried AI” initiatives that die in silence.

I’ve seen teams that finally take action months after their competitors did — and by then, the window for experimentation has narrowed. The pressure has grown. And now they’re solving not just the problem… but the regret.

The antidote to avoidance? Small steps.

This might sound too simple, but it works.

The brain resists “Big Project,” but it tolerates “Open a blank doc.”

It avoids “Create an AI strategy,” but it accepts “Write down 3 areas where AI could reduce friction for users.”

This is what behavioral scientists call chunking down the friction. Start tiny small.

Because when momentum kicks in — even if it’s micro — the emotional resistance fades.

When it comes down to AI - you don’t need to overhaul your team’s roadmap today, you don’t need a 40-page AI strategy or a high-stakes pilot tomorrow.

But you do need a place to start. A small first move — with structure.

That’s exactly what the AI Problem Framing workshop offers. We’ve designed this workshop and method for the exact moment when the idea is too big, the pressure is real, and no one knows how to begin. Instead of jumping straight to use cases, or letting tech leads drive the conversation alone, this process helps you slow down just enough to:

✅ Make sense of what matters strategically

✅ Align on the problem, before the prototype

✅ Spot dead ends before you pour time and money into them

✅ Think across functions — not in silos

It works because you’re not doing it alone — you’re working through it with a team.

It’s intentionally small. It’s structured — like a recipe, with step-by-step activities.

No jargon. No technical deep dives. No inflated promises.

Just clear thinking, together.

And it’s already battle-tested in Saudi Arabia — with students from five different universities learning how to frame AI problems before jumping into solutions. Check it out.