Samantha Dixon

An interesting read for all the Management Consultancy / Professional Services firm Partners in my network - and the conclusion reached provides some good food for thought: 💡 Focus on using AI to drive up your quality (and the value-added), rather than to accelerate your speed of delivery, and you're more likely to be able to sustain your fee volumes. Is that the direction you're seeing firms take right now, out of interest? Here's the full article from Source Global Research: https://lnkd.in/eVa9Sdb9

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WPP are all in on outcomes – are they betting the house? My first experience of payment by results didn't go well. In my early programmatic days, we moved from CPM to CPCV and a raft of performance metrics. We had almost no benchmarks which meant we lost our shirt on a few deals. Then we got the numbers right and it became the norm. I've been watching the same pattern play out in creative. Two years ago at DAIVID 🦩 Creative Data we were testing on average 2–10 hero assets. Now it's 2–10,000 and we're modelling creative directly to outcomes. It was a seismic move for us and we couldn't have made the leap if we were still relying on legacy tooling. Outcome-based pricing has always been attractive in theory and a nightmare in practice. Too many variables and often too hard to isolate your contribution. Which makes the hard cost difficult to absorb if you get it wrong. What's changed is the economics - and the measurement. When AI reduces production costs by an order of magnitude it makes it possible to take on risk you couldn't before. For example, when you can test thousands of creative variants instead of ten, you can predict what will work with real confidence. The watchout and is upper funnel. Brand building doesn't show up as cleanly on a performance dashboard. There's a genuine risk that outcome-based models quietly deprioritise the work that's hardest to measure but does the big lift long-term and some agreement needed on what's still not a perfect world - like true incrementality. Creativity and craft don't disappear in this model. But they'll need to fight harder to prove their place in it. Can this model work as the article questions or will it be quietly parked if the numbers improve? https://lnkd.in/eXcH89r8

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Yesterday I was at a really interesting talk on data journalism from Martin Stabe at the FT. I learned a lot. I also… absolutely did NOT put my hand up when they opened the floor for questions. Not because I didn’t have a question. But because I worried I’d sound like an idiot. (How very human of me.) The question I wish I’d asked was this (very much from a marketing bod’s perspective): Are we in a true AI revolution… or are we partly witnessing one of the most successful rebrands in tech history? Because as I listened, many of the tools being described sounded like things that have been around for years, just steadily evolving and improving - data analysis, automation, pattern recognition, predictive models. I explain AI to my kids as predictive text on their phones. Software learning from patterns and recommending what comes next. That idea has existed in different forms for decades. We just didn’t call everything “AI” back then. So now I’m curious how others see it. Especially people working in data, tech, media… or marketing. Are we seeing a true step-change in capability? Or are we partly seeing smart, established technology with very smart positioning? Honestly, I think it’s both. Some developments genuinely are different. Like the examples Martin walked us through. But there’s also a wave of “AI” labelling. The real shift? How we choose to position and apply it. Thanks so much to Intelligent Conversation for inviting me! Lovely to see Sarah Learoyd Bethany Lee Michael Macfarlane Adam Dennett Fritzi Wemheuer

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