It’s been a big couple of weeks for AI image generation and design tools: with major updates from Canva, Google and Anthropic, users are spoiled for choice. The new tools speak to a trend I’m increasingly seeing—and write about below: the baseline for AI use is shifting from competent prompting to confidently delegating to agents. The exam question for publishers is getting used to managing systems of agents rather than using individual tools.

It has been a busy week: updating the materials for and delivering a lunch-and-learn session and another full day training course on AI for IPG members, and preparing a keynote presentation on the future of publishing for a conference in Chicago next week. To prepare for those things, I’ve been reading and talking to a lot of interesting people about how they are using AI at the moment. In between, I made a quick dash up to London for my ten year business school reunion—a room full of smart people from different industries who provide a triangulation to the publishing experience. There are three interesting patterns that kept coming up in all of those different contexts.

A slightly shorter newsletter than last time as it’s been a particularly busy week: several days of travel, and submitting the manuscript of my book to my publisher. More on that soon, but first, some perspectives on AI impact.

If you read my piece last year on sizing and plotting the UK publishing industry based on Companies House data, there’s an interesting coda today with the publication of the Office for National Statistics’ updated Standard Industrial Classification (SIC) codes for 2026. If you’re not familiar with what SIC codes are, you’ve probably given up reading by now—but the short version is that SIC codes are short numbers that describe what business activities companies engage in. Firms choose one or more SIC codes when they register with Companies House.

I don’t set out with a particular theme in mind for each week’s newsletter: sometimes the things I’ve found in my research cluster serendipitously around a particular area, and more often there’s a range of themes. As it happens, this has turned out to be Use Case Week: practical ideas from Anthropic, the BMJ, consultants Fathm, and Paul Ford. If you can’t find something practical to try as a result of this week’s newsletter, you can have your money back.*

Ian Mulvany has a good piece in the latest issue of InPublishing examining challenges and opportunities for publishers. A couple of his points really stuck with me. Under pressure from investors to signal progress, companies are forcing AI into products and services without a clear sense of value. The result is a proliferation of new features that are individually justifiable but risk being collectively incoherent. And while generative AI can speed up the creation of content, code or even products, it doesn’t create additional time—in other words, opportunity cost still applies. I’ve started thinking of this pattern as strategic debt.

I’m sending this week’s newsletter a day early, as Friday and Monday are public holidays here in the UK—judging by the out-of-office messages I’ve seen this morning, it looks as though a lot of publishing has already checked out for the long weekend. But if you’re still at your desk, some reading matter to take you into the Easter holiday: there’s a lot going on, and a surprising number of strategy signals in this week’s news.

I took part in a webinar on AI and publishing earlier today, hosted by the Crius Group, alongside my friends and colleagues Cameron Drew and Simon Mellins. One of the topics that came up was what mistakes we saw organisations making with AI. The conversation was about publishing, but the more we talked, the clearer it became that these failure modes aren’t industry-specific. They show up everywhere. Here are eight of the most common. If you recognise more than two or three of them in your organisation, you may not have an AI problem—you may have a strategy and operating model problem.