Ahead of our ‘AI Special — Q&A’ webinar on 24th November (not yet registered? Register here), I thought I would whet your appetite with a top tip from each of our panelists:
- Increasing quality is better than increasing quantity or speed. AI is great at producing things that “look good” but don’t bear close inspection. (Carl Myers, Chief Technology Officer at Faversham House)
- Sort out what’s underneath: If your commercial information is fragmented, duplicated or out of date, your shiny new agent has shaky foundations. Understand where your data sits, how systems connect and what information the AI should be allowed to access. (John Cheney, CEO at Workbooks)
- Everyone working with content should be transparent in their use of AI. This is a legal requirement in the EU, but also an ethical and strategic choice long term. Trust is key to any publisher, and once it’s lost, it’s difficult to gain back. And while AI can help in many ways, it should not be implemented at the expense of content trust. (Janniche Moe, chief product officer at Fotoware)
- Training must lead to implementation: It is easy to give people a new tool, run an inspiring session and declare progress. Much harder is to identify a real use case, change the operating model around it, measure the result and decide whether it should become standard practice. (Toby Wiseman, MD, content at Hearst UK)
- Treat your content as data. Is it organised and accessible? Can a system reach your content, and does it get back something usable? For most publishers, the honest answer is ‘no’. Decades of reporting sit in formats nothing can read, with no consistent structure and no way to request a component rather than a whole page. Anything built on that will underperform, and the AI will take the blame. Rebuilding an archive was prohibitively expensive until recently. AI has made it viable. (Martin de Heus, chief commercial officer at WoodWing)
- Automate the routine. Most publishers imaging is high volume, repetitive work – toning, background removal, upscaling, tagging. That’s what AI does best and fastest, freeing your team rather than replacing their judgement. (Derek Milne, commercial pixometrist at Pixometry)
- Keep evolving your thinking and be honest. Be specific about what you will and will not do with AI, and don’t dress up the problems. Be willing to try things and either scale or fail fast. We have stopped several experiments that weren’t a good fit and kept the learnings for future projects. (Roxanne Fisher, director of digital and generative AI content strategy at Immediate Media)
- Start with an editorial priority. Decide what you want to improve before choosing a tool. That might be more consistent tagging or reducing repetitive work before publication. Give editors a specific task to try and judge whether the suggestions genuinely help. Where they keep supplying the same content or repeating the same steps may be something worth automating. Use that experience to decide where to invest next. (Nedim Dedic, chief technology officer at Glide Publishing Platform)
- Optimise for structure, not just transcription. The real bottleneck in content transformation is rarely converting audio or raw material into text or other formats, but it is turning that material into something with an editorial shape: sections, a narrative arc, a lead. When evaluating AI tools, look past raw transcription accuracy and ask whether the output actually gets you closer to a usable draft, not just a cleaner recording. (Sara Forni, product manager at Atex)
- The growing importance of making trust visible. Researchers have always relied on trusted sources, but as AI increasingly becomes a route to discovering and consuming information, signals such as peer review and attribution become even more important. Publishers have a responsibility to make those signals easier to recognise and understand. (Laura Feetham-Walker, head of publishing strategy and performance at IOP Publishing)
- Collect data everywhere you can – ideally in one system, though don’t stress about whether it’s called a “data lake” or some other awful term – and have AI act on that data to help make product decisions. (Stewart Robinson, managing director at Full Fat Things)
- Treat the bots as an audience, not a threat. AI crawlers and agents are now a real channel, so blocking everything leaves money on the table while allowing everything gives your work away; decide content by content, what is blocked, what is licensed and what is priced, and manage it where the content actually lives. (Brian Alford, founder and CEO of Bright Sites)
You can hear more insights from our experts and put your own questions to them at the webinar — look forward to you joining us on the 24th November. You can also read the articles they wrote for the AI special here.
You can catch James Evelegh’s regular column in the InPubWeekly newsletter, which you can register to receive here.
