Don't Try and Use Each AI Tool

Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

The number of AI tools available today is staggering, making it impossible to be aware of them all, let alone master them. While writing this, I tried to find a current tally, but the task proved futile. With new products launching every week, any count is obsolete almost as soon as it's published.

This explosion of choice is a double-edged sword. On the one hand, it’s fantastic to have a vast toolkit for various work and personal tasks. On the other, it means navigating a minefield of poorly built products, questionable security protocols, and sheer confusion. Trying to master, and pay for, 10+ different tools for specific tasks is hardly an efficient strategy.

This brings me to a different perspective from my own experience. At my new company, we are only permitted to use one main AI platform: Google Gemini.

While this might sound restrictive, I see it as a blessing in disguise. Instead of spreading my attention thin, I can focus on mastering one platform. I can devote my time to writing expert prompts, creating my own custom Gems, and learning how the system interacts best with my documents in Google Drive. It's a chance to hone my skills and extract maximum value from a single, powerful tool, which I believe is a compelling approach.

What about you?

How many AI tools do you typically use in a week? Do you feel you are a master of each one, or do you find yourself flitting between them?

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In this section, I’m going to bring to you a real world example of AI use.

Microsoft reports that 71 % of UK workers have used consumer AI tools at work (often weekly), for tasks like writing communications, reports, even finance tasks. They warn of serious privacy, security, and control risks from this “shadow AI” phenomenon.

Why this matters: This highlights the tension between bottom-up adoption of AI by employees and the governance, risk, and compliance challenges for organisations.

The Bank of England, along with the IMF, has issued warnings that sky-high valuations in the AI / tech sector may be outpacing actual productivity gains. They flag parallels to past bubbles and stress the risk of a sharp correction if expectations aren’t met.

Why this matters: This raises questions about sustainable business models in the AI industry, and how hype vs. value may diverge.

A European-led initiative is investing €10 million to help UK businesses and research organisations adopt AI more effectively—providing resources, training and demonstration projects.

Why this matters: This underscores public / institutional efforts to close the current AI adoption gap, especially important in a UK context where many firms are struggling with uptake.

AI use is spreading faster than oversight. Employees are adopting tools on their own, regulators are warning of a bubble, and new funding is trying to close the skills gap. Whilst adoption is easy, doing it safely and strategically isn’t.

Thanks for reading, and see you next Friday.

Simon,

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