Chris Creel

I've spent about a decade deeply understanding Apple's approach to product design, usability and all the things since spending time at Flurry Analytics, which saw app usage **every day** on 2 billion smartphones, most of them iPhones. I had unusual access, and it was pretty cool if I'm being honest. Anyway, their approach to layering in AI is very "Apple" which means they have to consider myriad use cases on a massive existing multi-product surface area and they will not rush into it. Remember, Apple is a hardware company first which means they "measure twice and cut once" (maybe even three times). In complex production with massive supply chains, you're working multiple years out, so little problems become existential problems. For people who do "run-and-gun" fast cycle iterative software work, it's very, very different. So it doesn't surprise me that they're first looking for the intersections where AI can add outsized, obvious value and layering those experiences, first, in a slow and measured way. At the same time, a hardware-first company, they are not going to have the leading AI lab building the most cutting edge models. In the early days of AI, they will need that trust, however, as users are experiencing what LLMs can do for the first time through Apple products. And that experience needs to deliver.

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Excited to share that Aperiam has invested in Bedrock Platform. Obvious statement of the century: programmatic advertising has a complexity problem. The infrastructure that was built to give media buyers control has, over time, done the opposite. Burying teams under layers of operational overhead, legacy tooling, and opaque supply chains. Bedrock is built on a simple but powerful thesis: what if you stripped away the bloat and rebuilt the stack around what actually matters... precision, transparency, and speed to execution. Their platform combines AI-driven campaign automation with curated, premium inventory across video, CTV, DOOH, and audio, and aims to be flexible and get smarter over time. Plus, the team (Shane, Austin, Ryan, James) has deep roots in the infrastructure layer of ad tech and understands the plumbing better than almost anyone. We believe the next generation of programmatic will be leaner, faster and AI-first. Bedrock is building that.

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An awesome conversation with Instacart's very own Ali Miller and The Drum's Kiri Masters on how Instacart's platform allows retailers of all sizes to participate in retail media without contributing to the fragmentation that has been so challenging for brands and agencies to manage. Definitely worth the read! Here we go!

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Your PMM role is changing: How to build human+AI workflows that define what's next That's what I'll cover on April 1 at Product Marketing Alliance's Product Marketing Summit in Denver. I will show HOW I guide AI live using various AI platforms. Your launch campaign workflow could connect positioning, messaging, content, and sales enablement in one flow. Your sales enablement workflow could build pitch decks, dynamic sales prep dashboards, and interactive ROI calculators. This becomes possible when you move from using AI to building with it. The PMM's job is changing. Trailblazing PMMs are building AI teammates trained on their expertise, connecting them into workflows across functions, and deciding how humans and AI work together. They're writing the new job description themselves. You'll see real workflows for product launches and sales enablement, and a tool that helps you identify which workflows to build and which AI teammates to connect. You'll learn: ► How to find workflow ideas and decide which are worth building for PMM work ► How to design workflows that create new value, not just speed up existing processes ► How to evolve your role from AI user to builder and orchestrator Looking forward to learning from an insightful line-up of speakers on April 1-2. Come join us. See link in comments for more info and to register.

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My First AI Post @2025 (and thank you to the Aperiam AI Fellows and to Dawn West and Alex Netelkos for making that happen!): If viewability was the training wheels of digital measurement, attention is the carbon-fiber racing bike we’ve just learned to ride. Erica kicked us off by pointing out the obvious: “viewable” doesn’t mean “seen,” and certainly doesn’t mean “engaged.” McKinsey’s neat formula — time × focus × intent — resonated as a starting point, but the real magic is in using that across channels and across the funnel. Why does this matter now? Because omni-channel is no longer a buzzword — it’s the messy, lived reality. CTV, digital out-of-home, mobile gaming, social feeds — each has wildly different engagement norms. An attention metric gives marketers and publishers a lingua franca, a Rosetta Stone to compare value and outcomes without pretending a click on a banner is the same as leaning into a big-screen spot. Publishers want credit for premium environments. Marketers want to normalize buys across hundreds of brands, countries, and media types. Attention-weighted CPMs (ACPMs) start to solve both. Even a global CPG can look across Meta, YouTube, stadium signage, and print — and make sense of it. Is it perfect? No. Is it better than a stack of inconsistent KPIs and gut feel? Absolutely. And yes, AI is in the wings — not as the protagonist, but as the stagehand making the hard stuff easier. It can normalize messy data, connect attention signals to outcomes, and embolden teams to try bolder experiments. Which brings us to the closing provocation: What if a few brave brands and publishers agreed to really transact on attention — rough edges, no gaming, shared responsibility for outcomes? The moment feels right to test whether attention can become not just a metric, but a currency. Marc Guldimann Adelaide David Zapata AB InBev Michael Shields Cory Treffiletti Rembrand McKinsey & Company

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AI that understands advertising context like a brain 🧠 In this interview, Grego Martínez, SVP of Product at Seedtag, speaks with Andy Plesser, CEO at Beet.TV, about how innovations like network-level analysis & embeddings are taking contextual advertising beyond simple keyword matching — paving the way for neuro-contextual AI. “We discovered that with new technologies like embeddings, we could train a system in something way more complex: understanding context like a brain. This is where neuroscience meets contextual,” Grego shared. Watch the full interview: https://lnkd.in/dZdp5K_y #AdTech #NeuroContextual #AI

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C’mon. This is the kind of mistake marketers SHOULD NOT be making. Leaking abuse victim data to LinkedIn because someone wanted better targeting? It's SO avoidable. But here we are. Covered California installed over 60 trackers on their healthcare enrollment site. And some of those tags handed over DEEPLY personal health data: - Pregnancy status - Prescription usage - Domestic abuse history No governance. No data team. No basic controls. Just a marketing team trying to move fast and a compliance disaster waiting to happen. I've seen this exact pattern inside so many orgs: Marketing wants performance data → Data team gets looped in after → Everyone hopes nothing breaks Except this time, it did. Covered California wanted targeting. Well, they got a lawsuit instead. If they had RudderStack, that data wouldn’t have left the browser. It’s not magic. It’s just what happens when your tracking setup includes built-in guardrails and your data team has basic oversight. It’s how you move fast without shipping a privacy crisis. They didn’t have it. So the data left. And now they get to explain that... in court.

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