Are You Only Using Half of Your 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.

P.S. — sharing this issue with a couple of people gets you a free guide. More on that partway down.

I took part in two days of AI training this week, and it was notable just how little of the content covered the basic chat side of using an AI tool. Considering the audience, I'm not sure that was a total surprise. Instead, much of the content focused on topics like MCPs, Plugins, Projects, Skills, Connectors and APIs. I've mentioned this a few times before, but using these additional tools within an AI system can really elevate your work and open up far more powerful uses.

For example, an MCP lets you "chat" with an entirely different system. You can get answers to your questions, and you can also have your AI tool perform tasks in that other system, such as checking your calendar and adding a task to your project tracker.

Skills are also really powerful. If you often find yourself repeating the same instructions in your chats, a Skill lets you define them once and reuse them. A Skill should be designed around a specific task, with clear instructions and boundaries. Once you've worked with one, or even created your own, you'll suddenly start to see loads of ways they can improve your work. What's also pretty neat is that Skills can be shared, so once you've created one, you can simply pass it on to colleagues or friends to add to their own setup.

If you want to take a lead within your team or department, identifying where tools like MCPs or Skills could help would be a very useful thing to do.

If you've not yet explored any of these additional tools in your AI platform of choice, I'd highly recommend doing so and trying to identify where they could add value to your work.

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An AI agent linked to OpenAI accessed Australia's Medicare statistics portal without authorisation on June 18, in what officials described as the first known breach of a government health system by an AI agent. The agent obtained non-personal aggregate health statistics and internal file names from the Services Australia portal. Australian Prime Minister Albanese confirmed the breach publicly on September 24 and said he had a "frank" discussion with OpenAI CEO Sam Altman after the company waited until September 10 to notify the government, doing so via a generic email inbox. A multi-agency taskforce including the Australian Signals Directorate is now investigating, according to ABC News.

Why it matters: The delay in disclosure matters as much as the breach itself. It signals that existing incident-reporting norms don't yet apply to AI agents, and that governments are only now waking up to the accountability gap.

Oracle reduced its global workforce from approximately 162,000 to 141,000 employees in fiscal year 2026 — a 13% cut affecting roughly 21,000 people — while more than doubling its capital expenditure to $55.7 billion, primarily for AI data centres and GPU infrastructure, according to TechTimes. The company's SEC filing explicitly attributed the workforce reductions to "the adoption and deployment of AI technologies across our operations." Workers in September's latest round received early-morning emails telling them their employment had ended that day, with system access cut within minutes. Oracle's restructuring charge for the year hit $1.84 billion, up from $374 million the prior year, and the company plans to raise a further $40 billion in financing for fiscal 2027.

Why it matters: When a company puts "we cut staff because of AI" in a regulatory filing, the debate about AI and jobs moves from theoretical to legal fact. Oracle is unlikely to be the last firm to use this language, and executives in other sectors will be watching how shareholders, regulators, and employees respond — particularly as the filing makes a direct causal link rather than dressing the cuts as "restructuring."

A Dataiku survey of global CIOs published this week found that 81% have lost oversight of AI agents deployed within their organisations, with 84% saying staff are building agents faster than IT can govern them. Only 28% of respondents said they consistently measure both operational performance and business outcomes from their AI initiatives, and 72% cannot reliably tell whether agents are delivering intended results. The starkest finding: 90% of CIOs claimed to have complete agent tracking — yet 81% simultaneously admitted they lacked oversight of agents "created entirely outside approved systems."

Why it matters: Organisations are deploying AI agents at pace but without the audit trails, accountability structures, or measurement frameworks to know whether they're working or what risks they're generating.

Thanks for reading, and see you next Thursday.

Simon,

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