I’m writing this on the way home from Chicago, where I gave a keynote on AI and publishing at the ECPA Leadership Summit. As I said in my presentation, this is not an area of publishing I’ve worked in, and my role was to provide an outside perspective—less about the specifics of the sector, more about what AI might look like when viewed from beyond it. It was a really rewarding and thought-provoking experience, and I am very grateful to Jeff Crosby and his colleagues at the ECPA for their hospitality.

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.

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.

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 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.

This piece was commissioned by James McConnachie and first published in the Spring 2026 issue of The Author, the journal of the Society of Authors. It is reprinted with their permission.

I gave the opening keynote at the ALPSP University Press Redux conference in Liverpool earlier today, discussing how generative AI is affecting scholarly publishing. My professional background is in trade rather than scholarly publishing, so instead of focusing on the university press business model or specific AI tools, I tried to step back and look at a macro question: what happens when AI changes both how knowledge is produced and how it is discovered.

I’ve just had three very busy days with friends, colleagues and clients at the London Book Fair. I spent time with every company in my portfolio, and had a great discussion with my publisher about my forthcoming book (watch this space for more). While the show is still fresh in the mind, here are a few reflections—less a comprehensive account of the Fair than a set of personal observations, raw signals and conversations that struck me over the last few days.