Are You Slowly Losing Your Expertise to AI?


Like many readers of this newsletter, I identify as a data expert.

It took me years of study and hard work to build this expertise, and it’s something that I’m not willing to lose.

This is not just because of everything it took to get here, but because my expertise forms an integral part of my identity. And the idea of losing it feels, in many ways, akin to losing a part of who I am.

So, when I recently started hearing from data professionals who spoke of slowly losing skills as they delegated more and more of their work to AI, it made me stop and think:

If this is happening to these people, what skills have I already started to lose due to my use of AI?

And more importantly:

What practices can I put in place now to prevent the loss of the expertise I value most?

It was while I was considering this question that I came across Blair Enns’ Experts' AI Manifesto - a set of principles outlining how experts in any field should be using AI if they want to maintain their expert status, while describing the consequences of using AI the wrong way.

It articulated many things I’d instinctively felt to be true and clarified the right path for me going forward.

In the latest episode of Value Driven Data Science, Blair joins me to dig into the principles contained in his Manifesto and what they mean for data professionals who want to use AI without compromising the expertise they’ve spent years building.

You’ll discover:

  1. Why delegating to AI is always a trade-off [03:00]
  2. The crucial difference between writing to communicate and writing to think [08:40]
  3. Why you should orient yourself around the problems you solve [14:28]
  4. How to decide which skills are worth protecting and which to let go [17:49]

Building genuine expertise takes years. Losing it to AI can happen gradually - but only if you let it.

Listen now on Apple Podcasts or Spotify, or click the link below:

Episode 113: The Experts' AI Manifesto

Talk again soon,

Dr Genevieve Hayes

Data Science Impact Algorithm

Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.

Read more from Data Science Impact Algorithm

I spend a lot of time talking to data professionals about AI, and one thing I’ve noticed is that attitudes typically fall into one of two extremes: AI boom or AI doom. At one extreme are those who are excited about the new opportunities AI will create - the chance to do more interesting work or finally launch the business they’ve always dreamed about. These people are always eager to share what they’ve used AI to create, and the results invariably amaze. But at the other end are those who lie...

I recently spoke to a software developer looking to make the transition into data science. He had decades of programming experience, a goldmine of non-technical skills, and he was asking my thoughts on how to approach the move. I told him that in most respects, his timing was excellent, because the AI-wave and more recent developments in agentic AI had fundamentally changed the data science landscape in ways that worked in his favour. Since AI agents are essentially just software, for people...

The number one piece of advice I give to data professionals is to have more conversations. Conversations lead to good things. Things like job offers and stronger relationships. But more importantly, the opportunity to deepen your knowledge through learning from the wisdom of others. I owe many of the best opportunities I’ve encountered in my career to proactively seeking them out wherever I can. My podcast is a weekly example of the power of conversation. But if there’s one thing all these...