by James Clive-Matthews | 29 May, 2026 | Structures & Models |
Agree with much of this, from my former occasional WPP collaborator Siobhán Woodrow.
Strategy, brand, marketing, content, media, delivery, and stakeholder reviewers all tend to optimise for their own objectives/needs, while trying to minimise their team’s risk at the point they hand work to someone else. That’s where the delays, rework and hidden costs creep in – and keep going up, like compound interest.
Too much important nuance has always got lost in translation between teams – I saw this clearly back at Microsoft as we tried to build global collaboration and coordination between markets and disciplines. Today, AI can help with interpretation as well as execution, but effective governance systems are vital (and remarkably hard to design, as they tend to need to adapt and evolve over time or risk being bypassed altogether).
I’ve been thinking about and working on this a lot very the last few years.
Many processes make perfect sense when viewed one step at a time, but become much harder to justify when you look at the whole chain end-to-end. Others sound great in theory, but are so annoying in practice that people just skip them.
And some are based on methods designed for an entirely different era – like the old double space after a full stop convention, created to avoid mechanical constraints on a typewriter, and before word processors introduced automated kerning and rendered manual spacing unnecessary. Without looking at the details of how a system operates it can be very hard to identify legacy ways of working like this that should no longer apply.
Creating an effective system is often about getting the basics right down in the details and building back up from more effective building blocks, with fewer gaps between them for efficiency to get lost in.
But, understandably, few organisations want to reimagine their operating models and value chains – even though, to get the most out of AI, this kind of fundamental rethinking and process / governance redesign may well be essential. And can often be very revealing about things you’ve been doing inefficiently for years.
by James Clive-Matthews | 14 May, 2026 | Systems & Technology
Just as I find myself skipping LinkedIn posts with telltale AI cadence, so Google’s skipping content that’s too AI dependent.
There’s still definitely a case for using AI to help produce content – especially if you’re working in your second language, struggle with dyslexia, or have other genuine reasons to use it to tidy up messy copy with solid substance behind it.
But now content is a commodity, it has to have very clear value for Google – or humans – to spend time with. (Why the hell would I care to see a result spat out by your prompt when I can write my own, and produce something far more relevant to my specific needs and interests?)
And the more of it you produce at ever greater speed, the more obvious it is that what you’re offering is a manufactured good, not something that’s been crafted with care and attention by a skilled artisan who really knows their stuff.
– Google’s not stupid
– Neither are the other AI search tools
– Nor are your human audiences
You may fool some of them at first, but it won’t last – and you may permanently lose their trust for having even tried.
This has long been obviously the way this was going to go – I’ve been arguing as much for 3+ years now – but finally we’re starting to get more data to back up what common sense was suggesting was likely from the moment GenAI became competent at scale.
by James Clive-Matthews | 1 May, 2026 | Structures & Models
So it turns out Google doesn’t like “commodity content”, and rewards content that’s original and interesting in search and AI results.
Give it half a second’s thought and this was always going to be the direction Google was going to take with its AI search.
Google’s whole thing was helping us find the valuable parts of the internet.
But when something – in this case content – can be mass produced, its perceived value goes down.
If mass-produced AI content takes over the web, then more genuinely original content becomes harder to find – and (relative) scarcity or genuine quality tends to create value in a sea of mass-produced “good enough” products.
(This is why a tailored woollen suit cost so much more than one made from synthetic materials and stitched in a sweatshop – the latter may be functional, but they tend to rapidly fall apart, and can also make you look bad if you try to pretend you can’t tell the difference.)
Where Google’s value lies
If Google can help us find that more valuable original, insightful, *human* content, Google continues to have value for us.
This is why their focus on E-E-A-T – Experience, Expertise, Authoritativeness, and Trustworthiness – made sense in the age of search, and it makes even more sense in the age of GenAI, where awareness of the questionable trustworthiness of AI output is increasingly front of mind.
They were never going to take the arrival of GenAI lying down, and they were always going to come back to finding ways to cut through the mass of average material out there to help us find the really good stuff. That’s their whole thing.
What makes a sensible AI strategy?
It’s also notable that while they’ve been making a lot of effort to make Gemini and the rest of their AI suite substantially better over the last couple of years (after a poor start with Bard and early AI search results), Google’s most distinctive AI product – NotebookLM – focused on providing verifiable citations from clear sources, rather than just making stuff up.
Google’s strategic need from their AI efforts has been clear for years, even if they’ve had some wobbles along the way – focus on utility. Meanwhile, OpenAI’s has largely consisted of throwing features around the place to see what sticks, and rapidly ditching what doesn’t.
ChatGPT 3.5’s launch may have led Google to scramble to catch up, but they’ve not deviated from their core objective. They’re not moving fast and breaking things, but moving deliberately and adapting their core offering to fit the new environment.
It’s something quite a few other companies could learn from.
