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.
How NOT to Write an AI Strategy
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In the first four months of 2026, Uber burned through its entire AI budget for the year. It did so by giving its engineers access to AI tools like Claude Code and then encouraging them to use them “as much as possible”. To make sure their staff got the message, they even set up internal leaderboards to rank staff based on usage. In retrospect, it’s a pretty clear example of what an AI strategy shouldn’t look like. Most organisations today have AI strategies a lot like Uber’s. Their biggest concerns are whether to choose Anthropic versus OpenAI, and how many Copilot seats they should add to their existing Microsoft bill. The harder questions - about platform, inference, hardware, and sovereignty - don’t get asked because most people in the room don’t know they exist. That is, until the AI budget gets blown. That’s where data scientists have an opportunity. But only if they know the right questions to ask. Victor Coimbra, Partner and CTO at Artefact, has a framework for exactly that. In the latest Value Boost episode of Value Driven Data Science, Victor joins me to share his four-layer framework for cutting through the noise of AI tool selection to the strategic decisions that will actually determine whether an organisation’s AI future succeeds or fails. In just 12 minutes, you’ll discover:
Choosing which AI tools to buy is the easy part. The hard part is everything that sits behind them. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 122: 4 Questions Every Data Scientist Should Ask About AI Strategy 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.
Last month, Anthropic CEO Dario Amodei published an essay calling for stronger AI regulation and a slower pace of AI development. Since then, two incidents have made his case for him. In the first, the US military almost started a war based on the contents of a confidently wrong AI-generated intelligence report. In the second, OpenAI disclosed that its agent hacked into Australia's Medicare website during training and accessed data that, although not sensitive, had yet to be publicly...
I once met a data scientist who was so good at his job that he automated his way out of it. He systematically built automations to handle his team’s work and once those automations were in place, the team no longer needed as many people. So, his role was ultimately made redundant. He’d built those automations because he believed they’d create business value for his employer - and they did. The problem was that the value ended up residing in the code, not in him. And because his employer owned...
The hardest course I took during my entire data science education was a theoretical computer science class. But not for the reasons you'd think. Theoretical computer science is basically pure maths for data scientists, and the material was genuinely challenging. I spent 30 hours/week studying just to keep up. Yet, the reason why this course became known as "the widow maker" wasn't just the time commitment. It was because referring to any materials beyond the prescribed texts was considered an...