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.
Everyone Wants to be AI-First
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Every organisation wants to be AI-first. It’s the new “data-driven” - a badge that signals ambition, modernity, and a seat at the table of the future.
“In the long run, we’re evolving in computing from a ‘mobile-first’ to an ‘AI-first’ world.” - Sundar Pichai, Google
“We are no longer a graphics card company… We are an AI-first company. From now on, we are betting the company on AI.” - Jensen Huang, NVIDIA
“Duolingo is going to be AI-first.” - Luis von Ahn, Duolingo
“Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI.” - Tobi Lütke, Shopify But in the rush to find opportunities to use AI, it’s easy to forget that AI isn’t always the right answer. In fact, according to Santosh Kaveti, CEO and founder of ProArch, when organisations come to him convinced that AI is the solution to their problems, 90 - 95% of the time the real issue turns out to be something else entirely: their data, their people, or their processes. AI just gets them in the room. In this Value Boost episode, Santosh joins me to explore the situations where AI isn’t the answer, how to recognise them, and how to have that conversation with stakeholders who are convinced it is. In just 12 minutes, you’ll discover:
The most valuable thing a data scientist can do isn’t recommend the most powerful tool - it’s recommend the right one. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 106: When AI Isn’t the Answer 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.
Have you ever seen the TV show Nip/Tuck? It centres on the lives and clients of two Miami plastic surgeons. But what it’s really about is the quest for perfection. The main characters want perfection in their own lives, but all they are actually capable of creating is the illusion of it. And beneath the perfect facades, all the characters are actually pretty terrible. It’s now over 20 years old, but rewatching an episode the other day made me realise it serves as a perfect metaphor for AI. AI...
In a recent blog post, CFO advisor and friend of the list Lauren Pearl discussed the impact of AI on the age-old software “build vs buy” decision. In short, Lauren argued that AI-driven vibe coding has made building software in-house far more appealing when the stakes are low, but when stakes are high, buying is still the way to go. To quote Lauren: “When you buy software, you don’t just buy the code. You buy a support team, expert-derived flows, tested features, platform trust, regular...
Ten years ago, everyone wanted to be doing machine learning. Go to a data conference - someone was presenting on ML. Advertising a data role - just mention ML and applications would 10X. It was the promise of machine learning that inspired an entire generation of data scientists. I know. I was one of them. And then came the statistic that revealed what many data scientists knew but few wanted to admit: around 90% of ML initiatives never made it to deployment. The ideas were there. The...