AI won't make decisions for product leaders

Hello Product Leaders!

In my journey to becoming a product leader, I've learned the importance of taking quick actions.

Join me to discover insights on understanding the real data challenge and making faster decisions to drive your North Star Metrics.

Today at a glance

• Juicy Weekly Picks

• Deep Dive: Optimize Decisions, Combat Ambiguity

• Key Takeaways

• Product-Ivity tools

In Case You Missed Them

👉️ Product leaders must master storytelling (Link)
👉️ Build a Winning Product Strategy (Link)
👉️ 7 Hard Principles for Future Product Directors (Link)

Juicy Weekly Picks

👉️ OpenAI announces 1st university partnership: Arizona State.
👉️ Watch Perplexity CEO who wants to destroy Google
👉️ Rabbit’s first 100K R1s to come with Perplexity Pro’s “answer engine.”
👉️ X rolling out video, audio calls on Android.

Deep Dive: Optimize Decisions, Combat Ambiguity

Ambiguity aversion refers to the tendency to prefer known risks over unknown risks, even if the unknown risks offer potentially greater benefits -

But as a product manager or leader, that’s what you do every day.

Psychological Impact: With increased seniority and more impactful decisions, there's a tendency to avoid uncertain decisions by doing extensive data research (I know..scary).

However, this approach may result in analysis paralysis, causing delays in delivering value to your users and customers.

Without a doubt, data provides a compass for choices, but treading solely on its path is a risky journey witnessed by many product teams.

Let's break it down.

Here are three good reasons not to depend only on data:

1- Why You Shouldn't Only Trust Data

Imagine trying to make a decision when you don't have all the info you need (ie which happens every day for us).

It's like trying to solve a puzzle with missing pieces. You can't have all the pieces before deciding, no matter how much you try.

Even if you collect all the data you can find, it won't tell you everything. Especially when you have questions that go beyond what the numbers can answer as well described in Matt Wallaert’s book ‘Start at the End’

Eg. data can show you how much people use a certain feature, but it won't tell you how much they love that feature or because they have no choice but using (especially in the B2B space)

2- Delaying decisions can be a bad idea

Sometimes, waiting for more data can cause more problems than making decisions with some missing info.

There’s an optimum timing to decide with data and that’s your job to find it as shown below in the chart.

In my time at my previous company, we were asked how valuable our work on structured quality data was (which was our mission). It's a good question, but putting a number on that value is tough and might not be the best way.

We suggested, (half-joking), temporarily removing a core part of the product and measuring the impact on our largest user base.

Everybody disagreed!

Because the harm it could cause was more than the value of having the data or knowing the answer.