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.
Earlier this year, entrepreneur Mark Cuban posted the following on X: “There are generally two types of LLM users: those that use it to learn everything, and those that use it so they don’t have to learn anything.” As this quote suggests, AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. Given my expertise took over 20 years for me to build, I’ve...
A few years back, when ChatGPT was in its infancy, stories relating to AI hallucination-induced mishaps made the news on pretty much a daily basis. From lawyers filing briefs referencing non-existent cases to government reports riddled with fake citations, you could watch people learning the limitations of AI in real time. And no organisation was too big to avoid embarrassment. Although these incidents do still occur, people are now at least starting to become aware of the very real...
Each week, it seems like there’s yet another announcement of technical workers losing their jobs to AI. In Australia, for example, tech giant Atlassian recently laid off 1,600 workers - 10% of their global workforce - “to steer more spending into AI”. Now, granted, not every AI-related job loss is necessarily as it first appears. Some experts point to AI-washing. That is, companies using AI as cover for restructuring decisions they would have made anyway. But regardless of the reason, the...