Systemic issues in AI adoption

In the previous issues of this multi-part blog post I shared some lessons in AI adoption that pertain to definition and framing of the opportunity. We’re now looking at the systemic, structural issues that can impede proper adoption. To note, I don’t go into technical details in this blog series (maybe one for later?). But if you want to learn more about the technical issues and opportunities that a technical adopter may face, head to Simon Willison’s blog. I’ve learned a lot of my tech skills in AI from him.

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What does AI adoption means in practice

Part 1 was a preamble based on framing AI in the context of building solutions. Here, we get a bit more practical detail.

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The context in which we build AI

This is a little preamble because it’s mostly about how to frame AI in a way that makes you… forget about AI.

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In 2020, I joined the NHS AI Lab. Part of the now defunct NHSX (a joint digital unit between NHS England and the Department for Health and Social Care), the AI Lab was a £250M initiative to develop and deploy safe, ethical AI across the UK healthcare system.

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I bumped into this nice visualization of Google Trends. It’s about web searches for “Davos”, which clearly exhibit a seasonal pattern consisten with the dates of the World Economic Forum.

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I’ve been meaning to play with microcontrollers for a long time. When I was at university, I was really jealous of my friends who had studied electronics in high school and were able to use these mysterious devices. The learning curve was really hard before ESP32 and Arduino made things much more standard and easy. The latter, especially, came with an IDE that, over time, has become the de facto standard not just for the Arduino family of devices, but also for a range of others, supported by installing external libraries.

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This article was first published in Nightingale

“Data is multidisciplinary” is my mantra—it’s 2025, and I’ve now worked 20 years in every possible flavour of data—data visualization, open data advocacy, data pipelines in healthcare, data-driven national-scale services, AI innovation, and more. Whatever the application or project, my take on data literacy is the fundamental ability to challenge your own assumptions about the data you have or don’t, the appropriateness in using it, the ethics of your application, and ask yourself: is there a different way, perhaps? Here is a gallery of some of my most treasured eureka moments working with data.

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I’ve just come back from a splendid week in Bologna, where I helped run csv,conf,v9 for the first time.

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A couple of weeks ago I saw this interesting map of commute times in Germany made by Datawrapper’s co-CEO David Kokkelink, noting how commutes into major cities are the longest in Germany.

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Photograph: The Helen Hamlyn Centre for Design

Open Data is about the “Data” as much as it is about the “Open.” Some stories from my experience as an Open Data activist and adviser illustrate it.

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