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.
A data scientist friend of mine once took a role in a team that never stopped complaining about their difficulties in recruiting technical staff. She had solid data skills, but not the specific organisational knowledge the role needed. Yet, she was willing to learn, and management seemed willing to train her. So, she asked for training. And she asked again. Her boss promised to show her “as soon as an easy project came up.” The work was too important for her to make any mistakes. In the...
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