Every vendor in your stack just turned on an agent. Your ESP has one. Your CDP has one. Your MAP has one. None of them know what the others are doing, none of them were told to check, and nobody volunteered to referee it. Welcome to the final floor.More
Tag Archives: llms
226: The Eye of context (The Dungeon of martech architecture, part 2)
Agents operating on data without anything to help them causes “believable nonsense.” Data quality stops agents from misrepresenting what the warehouse contains but you need context engineering to put the right meaning, rules, and situational information in front of the model at the right moment.More
202: Aleyda Solís: AI search crawlability and why your site’s technical foundations decide your visibility
AI search is rewriting how people find information, and Aleyda explains the shift with clear, practical detail. She has seen AI crawlers blocked without anyone noticing, JavaScript hiding full sections of sites, and brands interpreting results that were never based on complete data.More
199: Anna Aubuchon: Moving BI workloads into LLMs and using AI to build what you used to buy
Anna breaks down how old build versus buy habits hold teams back, how yearly AI contracts quietly drain momentum. Her biggest story is that Civic replaced slow dashboards and long queues with orchestration that pulls every system into one conversational layer, letting people get answers in minutes instead of mornings. More
162: Rich Waldron: How to build and manage AI agents from a single, composable platform without coding
Marketing ops folks stand at a crossroads where iPaaS platforms and AI agents are colliding in crazy ways. Rich pulls back the curtain on what happens when workflows become agent “skills”: Imagine your carefully built automations transformed into autonomous assistants that diagnose tech issues, provision applications, and manage complex Salesforce campaigns without manual intervention. More
148: Stephen Stouffer: Understanding AI’s role in customer journeys and messaging
AI can transform your marketing without overwhelming you. Start with one use case. Watch the results, and go from there. You don’t need to master data science to add AI value, but you need to be willing to experiment, keep what works, and let the tech do the heavy lifting.More
147: Nataly Kelly: Making global feel local through the power of marketing localization
Global expansion is a wild process that connects brands to the unique vibe of each market, it’s not just creating a website or translating content. Moving into international territories means showing up prepared, with a localization strategy that’s flexible and has a ton of local insight. Marketing Ops and RevOps both play a key role in localization as a strategic partner, organizing data and decision-making to fuel growth across departments.More
128: Vish Gupta: Why simplification should come before automation if you want to avoid a Frankenstack
We touch on the pitfalls of Frankenstein stacks and the perks of self-service martech. Vish explains why martech isn’t just for engineers and highlights the efficiency of customized Asana intake forms. We also tackle the dangers of over-specialization for senior leaders. Additionally, we explore the intersection of martech and large language models (LLMs), providing insights on how to stay ahead in the evolving landscape.More
126: Michael Rumiantsau: AI’s role in democratizing data narratives for marketers
Michael’s on a mission to make data insights accessible and useful for everyone, not just experts, by leveraging AI to provide tailored, easy-to-understand insights that boost decision-making. This episode unpacks the future of Business Intelligence, automating insights with LLMs, and the importance of anomaly detection. Michael also discusses how proprietary data gives companies a competitive edge in the AI market.More
103: Britney Muller: Deciphering the alien nature and the ethical complexities of LLMs
Britney offers a comprehensive view of the intersection of marketing and LLMs, blending technical know-how with ethical mindfulness and human-centric approaches. Her perspectives encourage professionals in the field to not only embrace AI for its efficiencies but to also understand and address its complexities, ensuring its development and application are both responsible and inclusive.More