16# Gal Rapoport (Kahoona, Amazon, AWS Inferentia) on Digital Body Language, Why Agentic Commerce Needs a New Web, and How to Derive Intent from Anonymous Traffic

17# Kelly Wortham (Forward Digital, Test & Learn Community, Experimentation Island) on Why Agentic Commerce Breaks Experimentation, Measuring What Visitors No Longer Do, and Optimizing for Machines

In this episode of Knowledge Distillation, Katrin Ribant speaks with Kelly Wortham – founder of Forward Digital and the Test & Learn Community, a network of several thousand experimentation practitioners, and curator of the Experimentation Island conference. Kelly came into the industry from academia and social sciences, where running experiments meant clipboards in shopping malls and t-tests on hiring processes. That grounding in the messy real world is exactly what makes her take on agentic commerce so sharp: she sees the current shift not as a technology problem, but as a measurement crisis hiding in plain sight. The core of the episode is a problem most experimentation teams haven’t fully reckoned with yet. As AI-referred traffic grows, visitors arrive at websites already persuaded – they’ve done their comparison shopping in ChatGPT or Perplexity and are now just confirming what they already know. A/B tests designed to persuade don’t work on people who’ve already been persuaded. And because most companies have no clean way to separate AI-referred from non-AI-referred traffic, results get blended into noise. Kelly’s practical advice: start segmenting AI-referred traffic now, even if the data is messy, because building that muscle early is more valuable than waiting for a clean solution that doesn’t exist yet. The episode closes on a category Kelly calls brand impact tests – experiments that happen entirely off your website, in the third-party content ecosystem that AI models are trained on: reviews, product descriptions, social mentions. These are the inputs that shape what an AI recommends before a customer ever lands on your page. And on a provocation both find genuinely exciting: after years of optimizing for clicks and dark patterns, agentic commerce may be forcing brands back to clarity and human-first design – because optimizing for machines increasingly means optimizing for humans. All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast [https://ask-y.ai/knowledge-distillation-podcast/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast] Learn more about ASK-Y: www.ask-y.ai [https://ask-y.ai/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast]

16# Gal Rapoport (Kahoona, Amazon, AWS Inferentia) on Digital Body Language, Why Agentic Commerce Needs a New Web, and How to Derive Intent from Anonymous Traffic

In this episode of Knowledge Distillation, Katrin Ribant speaks with Gal Rapoport – co-founder and CEO of Kahoona, a company building what he describes as the context and memory layer the web never had. Gal’s background in AI goes back well before the current hype cycle: he helped build what became AWS Inferentia and AWS Trainium at Amazon, then joined the Alexa Shopping team as its first machine learning hire, working on personalization at a time when transformers were still an internal experiment. After leaving Amazon, he pursued a PhD in multimodal AI focused on human-computer interaction and the coming data scarcity problem – and realized that everything he knew about personalization from Amazon simply didn’t exist in the open web. That gap became Kahoona, which has since won the LVMH Innovation Award for best business impact and a similar recognition from the Kering Group. The central concept of the episode is digital body language: the idea that how a user moves through a website – the speed of their scroll, where they hover, how long they pause – carries as much signal as what they click on. Gal explains how Kahoona captures this through a lightweight script, then trains models on those behavioral signals to infer intent within moments of a user arriving on a page, for the anonymous visitors who make up 97% or more of traffic. The conversation then takes a sharp turn into agentic commerce. Katrin raises the obvious tension: Kahoona was built to read human behavior, but AI shopping agents don’t hover or browse – they execute with surgical precision. Gal’s answer is counterintuitive: agents are actually easier to model than humans. Humans are noisy and shift intent mid-session. Agents have a clear mission and low behavioral variance, which makes their intent more predictable, not less. The episode closes on what brands should actually do with this today. Most are blocking agents by default, not out of hostility but because they have no policy framework yet. For analysts working with GA4, Gal offers two concrete signals to watch: declared agent traffic segmented by geography, and the correlation between declining time-on-site and rising conversion rates – a pattern suggesting agents are doing the research upstream and sending humans to the site already primed to buy. His bigger prediction: websites will soon detect whether the incoming actor is human or agent and route them to entirely different experiences – one visual and exploratory, one structured and markdown-readable. The brands that build that infrastructure now, before agents become the dominant traffic source, will have the advantage. All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast [https://ask-y.ai/knowledge-distillation-podcast/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast] Learn more about ASK-Y: www.ask-y.ai [https://ask-y.ai/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast]

15# John Lovett (Seer Interactive, Web Analytics Demystified, Digital Analytics Association, Forrester, Jupiter Research) on What Makes an Analyst an AI Analyst, Detecting Agentic Browsers in Your Traffic, and What 5,000 Olympic Prompts Reveal About How LLMs Think

John Lovett has seen every chapter of digital analytics from the inside. He started as a marketer curious about how messages reach people, moved through analyst roles at Jupiter Research and Forrester, co-founded Web Analytics Demystified with Eric Peterson, served as president of the Digital Analytics Association during the pivotal renaming from “web” to “digital” analytics, and is now VP of Analytics & Insights at Seer Interactive, where he’s building AI into every layer of the analytics practice. In this episode, John and I dig into what actually makes an analyst an AI analyst – and his answer surprised me. It’s less about new skills and more about bringing AI into the curiosity and critical thinking that always made someone a good analyst in the first place. He walks us through how Seer identified 15 core deliverables and systematically disrupted each one with AI, creating a team that starts every task with an agent rather than a blank template. We then dive deep into agentic commerce – the emerging reality of AI agents that browse, compare, and buy on behalf of consumers. John shares what Seer is learning about distinguishing human from bot traffic (spoiler: humans do “clicky clicky scrolly scrolly,” agents do surgical strikes), why log file analysis is making a comeback, and his hypothesis on which product categories will see agentic purchasing first. Finally, John walks us through his groundbreaking GEO research using the 2026 Winter Olympics as a case study – running 5,000 prompts across every major LLM to understand how models find, trust, and cite brands. The results reveal a fascinating divide between models that search the web and those that hit what he calls “the binary cliff.” Plus, we talk about his new book, The New Big Book of KPIs, and why the best time to start with AI would have been yesterday – but the next best time is today. All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast [https://ask-y.ai/knowledge-distillation-podcast/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast] Learn more about ASK-Y: www.ask-y.ai [https://ask-y.ai/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast]

14# Simo Ahava (Co-founder: Simmer, Partner: 8-bit-sheep, Co-host: Standard Deviation Podcast) on Teaching Technical Marketers When AI Removes the Incentive to Learn, Why Critical Thinking Is the Skill AI Can’t Replace, and What Agentic Commerce Means for Data Layer Architecture

In this episode of Knowledge Distillation, Katrin Ribant speaks with Simo Ahava – quite simply the person the entire digital analytics and technical marketing community turns to when they need to understand how things actually work. Simo has been writing about web analytics, tag management, and the Google marketing stack since 2010, and his blog at simoahava.com has become the definitive technical reference for anyone implementing Google Analytics or Google Tag Manager. A Google Developer Expert in both platforms from 2014 to 2025, a multiple Digital Analytics Association award finalist, and one of the most generous knowledge sharers the industry has ever seen – if you’ve ever asked a question on Measure Slack, there’s a good chance Simo answered it, thoughtfully, for free. He co-founded Simmer with his wife Mari Ahava, an online learning platform for technical marketers that has become the gold standard for courses on server-side tagging and BigQuery. He is partner and co-founder at 8-bit-sheep, a Helsinki-based digital services consultancy, and co-hosts the Standard Deviation Podcast with Juliana Jackson. The conversation opens with what Simo calls the educator’s dilemma: AI makes it trivially easy to get answers, which removes the incentive for deep learning. His students take course content to an LLM, get a conflicting answer, and bring the contradiction back – without the baseline knowledge to judge which is correct. Katrin pushes back: practitioners doing real analytics work need to understand fundamentals like context windows and attention mechanisms. They land on a distinction – Simo’s concern applies to learners seeking quick answers, Katrin’s to practitioners maintaining context continuity across complex workflows. The episode then pivots to agentic commerce. Simo draws a direct line from his data layer and server-side tracking expertise to the challenge of designing websites for AI agent access. Tag management systems have let organizations survive with poorly structured data for years. Agentic commerce breaks that – agents need structured data by design, not retroactive patches. Simo warns against over-optimizing for agents at the expense of human UX, and raises the unsolved measurement problem: how do you track agentic traffic when AI agents have no reason to identify themselves? All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast [https://ask-y.ai/knowledge-distillation-podcast/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast] Learn more about ASK-Y: www.ask-y.ai [https://ask-y.ai/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast]

13# Scott Brinker (Creator of the MarTech Landscape and Chief MarTech, HubSpot, Ion Interactive) on the SaaS Apocalypse, Why Software Isn’t Dead but Its Moats Are Changing, Why Context Engineering Is the Real Analyst Job Now, and Why Technology Changes Exponentially While Organizations Change Logarithmically

In this episode of Knowledge Distillation, Katrin Ribant speaks with Scott Brinker – the creator of the Marketing Technology Landscape Supergraphic, the map of the martech industry that started with 150 logos in 2011 and now tracks over 14,000. Scott spent eight years as VP of Platform Ecosystem at HubSpot, where he built out their partnership and integration ecosystem. He holds degrees from Columbia and MIT Sloan, co-authors the annual State of Martech report with Franz Riemersma, and now works full-time as an independent martech analyst through Chief MarTech. Katrin still drinks her coffee from a mug Scott gave out a decade ago – the one with snails at a boardroom table and the tagline: technology changes exponentially, organizations change logarithmically. Together they dig into the so-called SaaS Apocalypse – triggered by AI-native tools lowering the barrier to building software – and land on a nuanced take: the market overreacted in the short term, but the long-term disruption to SaaS business models is real. The risk isn’t that customers will vibe code their own CRM; it’s that a thousand new companies will. Scott introduces his framework of systems of truth and systems of context – an evolution of the classic systems of record and systems of engagement – and explains why delivering the right information, to the right person or agent, at the right moment is the hardest and most valuable problem in martech today. Katrin connects this directly to Ask-Y’s thesis: that the central challenge in analytics isn’t the tools, it’s maintaining context continuity across every step of the workflow – from data connection through transformation to stakeholder output. The conversation goes deep on Scott’s framework of three types of AI agents in marketing: agents for marketers (internal productivity), agents for customers (brand-controlled interactions like AI-powered chatbots and SDRs), and agents of customers (the disruptive category – AI assistants that work for the buyer, not the seller). They explore how agents of customers are forcing a rethink of everything from SEO to email marketing to e-commerce, and Katrin lays out her thesis that agentic commerce will trigger a workstream comparable to mobile platforming, the GA4 migration, and a fundamental shift in customer relationships – all at once. Scott agrees and adds his prediction that agentic email is the next major disruption most marketers aren’t preparing for. The episode closes on the AI analyst role itself: Scott argues that hands-on experience with AI tools is non-negotiable, that understanding code remains critical even when you’re not writing it, and that the only way to build the mental model required for this era is through consistent, daily practice. His advice: the only way out is through. All episodes on our website: www.ask-y.ai/knowledge-distillation-podcast [https://ask-y.ai/knowledge-distillation-podcast/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast] Learn more about ASK-Y: www.ask-y.ai [https://ask-y.ai/?utm_source=podcast&utm_medium=episode_description&utm_campaign=podcast]