Knowledge Distillation: The Rise of the AI Analyst
Welcome to Knowledge Distillation – a series exploring how AI Analysts are transforming the future of data work. We look at practitioners becoming AI Analysts, founders building AI Analyst tools, VCs backing the AI Analyst wave, and market analysts mapping the trend.
Each episode uncovers what it means to be an AI Analyst today – the workflows being reinvented, the skills analysts need now, and the promises AI is keeping or breaking. From prompt engineering to context management, we dive into the real conversations shaping this role.
Let’s distill some knowledge. Because bots won’t win. AI Analysts will.
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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.
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27 Apr 2026
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.
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10 Apr 2026
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.
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25 Mar 2026
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?
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16 Mar 2026
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.
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9 Mar 2026
12# Eliot Durbin (General Partner, Boldstart Ventures – Clay, Snyk, Crew AI, Wiz, Kustomer, Keycard…) on the SaaS Apocalypse Hype, Betting on People Over Products, What Actually Compounds at Inception and the Skills That Survive Every Cycle
In this episode of Knowledge Distillation, Katrin Ribant speaks with Eliot Durbin, General Partner at Boldstart Ventures – one of enterprise software’s most active inception-stage funds, with a portfolio that includes Clay, Snyk, Wiz, Crew AI, Kustomer, and Keycard, among others. Boldstart was founded in April 2010 with a $1M first fund and pioneered what Eliot calls “inception investing”: backing technical founders on the strength of a person and a thesis – before a product, before a pitch deck, sometimes before there’s even a market. Katrin and Eliot have known each other for 15 years, with Boldstart backing Ask-Y at its earliest stage.
Together they unpack the so-called SaaS Apocalypse – the trillion-dollar collapse in software market cap triggered by AI-native competition – and whether the hype matches the reality. Eliot argues it doesn’t: software isn’t dying, it’s evolving, just as it did through the cloud and mobile revolutions. The companies that move fast and go AI-native will survive; those that don’t will go the way of the ones that missed mobile. The conversation goes deep on what actually compounds at inception in a world where anyone can vibe-code a prototype in a week, how moats are being redefined around trust and interaction data, and why speed remains the only real advantage at the earliest stage. They also dig into agentic commerce – the wave forcing brands to re-architect their websites and data layers for both human and AI agent audiences – and what that means for analytics teams. The episode closes on the AI analyst role itself: Eliot draws a direct parallel to how Clay created the GTM engineer out of rev ops, arguing the same elevation is coming for analysts – not replacement, but a shift to higher-order reasoning. His single best piece of advice for anyone navigating this moment: play with as many tools as you can.
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2 Mar 2026
11# June Dershewitz (Co-founder: InvestInData, Web Analytics Wednesdays, Former President: Digital Analytics Association) on 25 Years of Watching Analysts Reinvent Themselves, Why Data Leaders Are Betting Their Own Money on the AI Stack, and What Community Building Taught Her About the Skills That Actually Survive Every Disruption
In this episode of Knowledge Distillation, Katrin Ribant talks with June Dershewitz – a pioneering analytics leader who has been at the center of the data community for over two decades. June started her career in web analytics in 1999, co-founded Web Analytics Wednesdays (the industry gathering that launched a thousand local analytics communities), served as President of the Board of the Digital Analytics Association, and has led analytics and data governance teams at some of the largest media and entertainment platforms in the world. Now, as co-founder of InvestInData – a collective of 50+ Chief Data Officers and VPs of Data who angel invest in early-stage data startups – she sits at the unique intersection of practitioner, community builder, and investor.
Together they explore what 25 years of analytics evolution teaches you about the current AI transformation, how angel investing from the practitioner seat gives you a fundamentally different lens on which AI tools will actually matter, and why the community-building instinct that drove Web Analytics Wednesdays is more relevant now than ever as analysts figure out what the AI Analyst role actually looks like. June shares her perspective on scaling data teams through every major disruption – from the early days of web measurement through big data, real-time analytics, and now agentic AI – and what she’s learned about the human skills that no technology cycle has managed to automate away.
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20 Feb 2026
10# Tim Wilson (Sr. Analytics Director: Search Discovery, Head of Solutions: Facts & Feelings, Co-host: Analytics Power Hour, Co-author: Analytics the Right Way) on Thinking Before Measuring in the Agentic Commerce Era
In this episode of Knowledge Distillation, Katrin Ribant talks with Tim Wilson – one of the most respected voices in digital analytics, widely known as the Quintessential Analyst (a title he’ll deny but absolutely deserves) and equally famous for climbing on a soapbox and delivering the kind of rants that somehow leave you smarter when he’s done. Tim has been working with digital data full-time since 2001, holding senior analytics roles at Search Discovery, Analytics Demystified, and across multiple agencies and Fortune 500 consultancies, and is widely known for his no-nonsense, clarity-first approach to getting business value out of data – earning him a reputation as one of the industry’s most beloved (and self-admittedly cranky) analytical thinkers.
Continuing the agentic e-commerce series, this episode goes upstream: before you instrument, before you build the data layer, how do you decide what to measure? Tim draws on over two decades of experience to argue that the agentic commerce shift – comparable in scale to mobile, Amazon, and GA4 combined – demands business clarity first, not more data collection. Together they explore why organizations keep repeating the same measurement mistakes across every technology disruption, how to use hypothesis testing and primary research to cut through the hype, and why the analyst’s real superpower is resisting the urge to solution before the business question is clear. The conversation also dives into the evolving skills analysts need now, from understanding LLMs to prompt and context engineering as the new SQL.
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13 Feb 2026
9# Josh Silverbauer (From the Future, The Third Party Show) on Why Agentic Measurement Is a Construction Not a Migration, The Black Box of Instant Checkout Attribution and What Universal Analytics Migration Taught Us About What’s Coming
Josh Silverbauer is Head of Analytics and CRO and Partner at From the Future, where he architects data layers and analytics implementations for brands navigating the messiest parts of measurement. He’s also the host of The Third Party Show – a comedy-musical talk show for digital marketing – and the creator of a rock opera about an alien named Cookie who loses his universe when Universal Analytics sunsets. In this episode, we dig into what happens when a purchase occurs without a browser session. OpenAI’s Instant Checkout means transactions can complete entirely server-side, with no JavaScript firing, no user journey, and no attribution – just an order appearing in your backend. Josh walks through where “ChatGPT / not set” shows up in your unassigned traffic, why server-side webhooks are currently the only way to count agent-driven purchases, and what the GA4 migration taught us about not dragging old assumptions into new architectures. We also explore why Microsoft Clarity is ahead of Google on AI traffic dashboards, and why building the agentic data layer feels less like a migration and more like a construction project with no blueprints.
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6 Feb 2026
8# Sani Manić (NoHacks Podcast, Search Engine Journal, WordPress Core Contributor) on Why 35 Years of Web Infrastructure Isn’t Ready for AI Agents, Making Boring Sexy Again, and How GA4 Analysts Are Accidentally Prepared for What’s Coming
“We have so much trash in terms of websites that AI is now supposed to ingest and give us something meaningful. It’s not going to work.” Sani Manić has spent 15+ years optimizing websites for speed, accessibility, and technical health. A contributor to Search Engine Journal and WordPress Core, he’s now turned that expertise toward a new challenge: making the web work for AI agents. In this episode, we explore the emerging data layer beneath the agentic web – where users never see your brand’s colors or copy because an AI assistant completed the purchase for them. Sani and Katrin discuss why clean semantic HTML is the new competitive advantage, why you should never trust a tool built by someone who couldn’t do the job without AI, and the real security exposure of frontier models. Sani hosts the NoHacks Podcast, now subtitled “Optimizing the Web for AI Agents” and is building an auditing tool that shows how different LLMs and agents see, navigate, and transact on your website. For analysts and e-commerce teams, this episode makes a compelling case that GA4’s event-driven architecture and the BigQuery skills analysts built during migration may be their best preparation for tracking the agentic web, even if nobody planned it that way.
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19 Jan 2026
7# Yaniv Makover (Nokia, Anyword) and Avigad Oron (Microsoft, Vitalert, Ask-Y) on Building AI Before ChatGPT, Why Context Management Is Everything, The Enterprise Security Nightmare Nobody Talks About and Why Full-Stack Skills Beat Specialization in the AI Era
“My mom called me about this cool thing called an LLM. I was like, I’ve been working on this for 10 years.” Yaniv Makover, CEO of Anyword, joins Avigad Oron, Head of Technology for Ask-Y, Two AI engineers with combined 40 years of experience dissect their ChatGPT moment and what it means for the future of work. We discuss the architecture decisions behind enterprise AI-from RAG and ranking to security nightmares where “your data goes into a super complex distributed system with multiple models.” They debate whether transformers will reign forever and agree on one thing: AI is pushing everyone toward senior-level integrative skills while routine tasks disappear.
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7 Jan 2026
6# Jim Gianoglio (Cauzal Analytics, MMM Hub, Measure Up) on The Dual Life of the AI Analyst, Building vs. Using AI Tools, and How to Future-Proof Your Career by Automating the Bottom of Your To-Do List
Jim Gianoglio has worn every hat in marketing-from photographer to creative director to SEO specialist to data scientist. Now he runs Cauzal Analytics, a boutique consultancy focused on marketing mix modeling and incrementality testing, and co-hosts MMM Hub, a community and newsletter for measurement practitioners. He also co-hosts the Measure Up podcast and built the session board app that digitizes Measure Camp schedules across the country.
In this episode, we explore the dual life of the modern analyst: using AI to power analytics workflows while also building AI-powered tools. Jim shares the “zero to 80% magic moment” of vibe coding with Replit, the frustration of context poisoning when LLMs get stuck in loops, and why he keeps re-testing tasks that failed months ago-because the models are always getting better. We dig into how analysts can future-proof their careers by listing every task they do, prioritizing by value, and systematically automating from the bottom up. Jim also reflects on how context engineering applies to marketing mix modeling and why understanding your business remains the skill AI can’t replace.
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23 Dec 2025
5# Patrick Thompson (Atlassian, Iteratively, Amplitude, Clarify) on Why Clean Data Matters More in the AI Era, The Difference Between Using AI as a Tool vs a Crutch, and Building the Muscle of Data-Informed Decision Making
What happens when AI meets bad data? Patrick Thompson has been building and using data tools for over 15 years. He started in growth at Atlassian, then co-founded Iteratively-a data quality platform acquired by Amplitude in 2021-where he became Director of Product. Patrick knows what it takes to build data layers, event pipelines, semantic layers, and quality monitoring systems. He knows what breaks when instrumentation goes wrong. In this episode, we explore how AI is lowering the floor for analysis while raising the bar for what makes analysts valuable. Patrick shares why natural language queries are changing user expectations overnight, why memory and context remain the hardest problems for AI to solve, and the difference between using AI as a tool versus a crutch. We dig into why gut-checking analysis is becoming a critical skill, and what Patrick tells people who ask if their kids should still study software engineering.
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15 Dec 2025
4# Michael Helbling (Search Discovery, Stacked Analytics, The Analytics Power Hour) on 20 Years in the Trenches, Why Trust Is the Real Bottleneck, and The Difference Between Data Retrieval and Actual Analysis
A very special episode of Knowledge Distillation with Michael Helbling, co-founder and co-host of the Analytics Power Hour podcast and founder of Stacked Analytics. Michael has spent two decades building analytics programs as both consultant and practitioner, and he’s not one to sugarcoat anything. Together we break down why dropping an LLM on your data warehouse won’t magically produce insights, why semantic ambiguity kills most AI analytics initiatives, and what the streetlight effect means for AI’s ability to conduct real analysis. We get into the path forward – multi-agent architectures, semantic layers, knowledge graphs – while staying clear-eyed about the heavy lift required. Essential listening for anyone caught between CEO mandates to “AI everything” and the reality that this stuff doesn’t work like the LinkedIn posts suggest.
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2 Dec 2025
3# Shomik Ghosh (Boldstart Ventures, Top Tier Capital Partners) on The VC POV on Hype Cycles, Why AI Is an Enablement Shift, and First Principles Thinking About the Skills AI Won’t Replace
In this episode of Knowledge Distillation, Katrin Ribant speaks with Shomik Ghosh, a Silicon Valley VC who has lived through multiple tech hype cycles from both the investing and operating sides. Together they break down how hype forms, peaks, crashes, and ultimately reshapes roles – from DevOps to data science and now the rise of the AI analyst. Shomik explains why this generative AI wave is an enablement shift, spreading faster than any previous platform shift, and why embracing a learning mindset is now the most valuable career differentiator. They discuss which analyst skills will survive every hype cycle – deep understanding of data, critical thinking, and the ability to evaluate AI-generated logic. The episode also explores the future of work with AI agents and what it means to orchestrate virtual teams of autonomous tools. A must-listen for anyone navigating the new analytics landscape and trying to choose what to learn next.
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27 Nov 2025
2# Mike Driscoll (Co-founder: Metamarkets, Rill, Data Collective VC) on Building for AI Analysts and the New Analytics Skillset
In this episode of Knowledge Distillation, Katrin Ribant talks with Mike Driscoll – co-founder and CEO of Rill Data and co-inventor of Apache Druid. Mike reflects on the early days of big data, the birth of real-time analytics, and what it meant to be an analyst in 2010. Together, they explore how today’s challenges have shifted from scale to maintaining analytical context across complex workflows. Mike discusses the rise of more technical, code-native analysts and how AI agents are transforming the way data teams operate. He shares why data engineering remains a superpower and how analysts can upscale to thrive as orchestrators of AI-driven processes. The episode offers a forward-looking view on the “AI analyst” skill set and the future of self-serve analytics.
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21 Nov 2025
1# Martin Kihn (Digitas, Gartner, Salesforce) on the AI Agent Revolution and Skills that Keep Analysts Relevant
In this kickoff episode of Knowledge Distillation, host Katrin Ribant sits down with Martin Kihn, AI strategist at Salesforce and author of Agent Force. Together they explore how the role of the data analyst is shifting from manual data wrangling to orchestrating AI-powered workflows. Martin breaks down why understanding LLMs, prompt engineering, and AI-generated code is becoming essential for modern analysts. They discuss the misconceptions around AI replacing analysts and explain why critical thinking and business context matter more than ever. The episode highlights how AI accelerates hypothesis testing, improves visualization, and expands what analysts can achieve. A great introduction to the emerging skill set of the AI analyst.
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