Podcasts about Martech

AI gives confident answers without the context to back them up. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where AI's missing context creates risk for go-to-market teams. She explains how GTM intelligence platforms fill gaps that generic AI models miss, why context depth matters more than model size, and what marketers need to validate before trusting AI-driven insights across the customer journey.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI gives confident answers without the context to back them up. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where AI's missing context creates risk for go-to-market teams. She explains how GTM intelligence platforms fill gaps that generic AI models miss, why context depth matters more than model size, and what marketers need to validate before trusting AI-driven insights across the customer journey.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI dashboards can look perfect while missing critical context. Kelly Hopping, CMO at 6sense and four-time marketing executive, explains how confidently wrong AI outputs mislead go-to-market teams. She points to closing the context gap in AI models, validating AI-driven insights against real buyer signals, and building go-to-market intelligence that accounts for the full customer journey, not just surface-level metrics.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI dashboards can look perfect while missing critical context. Kelly Hopping, CMO at 6sense and four-time marketing executive, explains how confidently wrong AI outputs mislead go-to-market teams. She points to closing the context gap in AI models, validating AI-driven insights against real buyer signals, and building go-to-market intelligence that accounts for the full customer journey, not just surface-level metrics.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Michael Sorg and Katie Dudas are joined by Sam Eitzen, CEO of Snapbar, for a deep dive into how artificial intelligence is changing experiential marketing, trade shows, conferences, brand activations, and the humble photo booth. Sam explains how Snapbar evolved from physically hauling hundreds of photo booths to events into building a web-based platform that lets brands create interactive experiences accessible from an iPad, touchscreen, QR code, or an attendee's own phone. What started as a necessary pandemic pivot has developed into AI photo experiences, highly personalized AI Stories, and new ways for brands to combine entertainment with lead generation. The conversation also gets beyond the AI hype. Sam discusses the unpredictability of image models, why prompt engineering still requires humans, how Snapbar tests and switches between different AI models, and why brands need to understand that generative AI is not simply automated Photoshop. Snapbar's system remains cloud-based, meaning connectivity and third-party model reliability are still real considerations for live events. Topics and products discussed: What experiential marketing actually means: Sam distinguishes traditional broadcast and digital advertising from interactive marketing designed to bring the audience into the experience through participation. Snapbar's origin story: The company began as a side-gig photo booth business before moving from weddings into conferences, trade shows, sporting events, product launches, festivals, and other B2B activations. The pandemic pivot: When physical events disappeared in 2020, a client asked whether Snapbar could bring its photo experiences online. That request became the foundation of the company's current web-based platform. World Cup fan activations: Sam describes branded soccer experiences that let attendees transform themselves into stylized or animated fans and players without improperly copying team intellectual property. QR codes instead of long lines: Experiences can run on event iPads while also letting thousands of attendees participate from their own phones by scanning a QR code. Understanding the audience: A soccer activation that works at the World Cup may flop at a legal conference. Sam says cybersecurity audiences, for example, often respond particularly well to gaming, fantasy, and trading-card concepts. Digital experiences as event swag: The group discusses whether personalized digital content can become an alternative to another pile of branded pens, bottles, and promotional giveaways. Snapbar's planned digital spinner wheel: Sam explains a digital version of the familiar trade-show prize wheel with customizable branding, prize probabilities, inventory management, QR control, redemption codes, and lead capture. The rise of the AI photo booth: Early image generation was unreliable, producing extra arms, heads, and other artifacts. As models improved, AI photo experiences became one of Snapbar's biggest products. AI Stories: Snapbar adds Mad Libs-like personalization to AI-generated experiences, allowing attendees to influence the resulting scene while brands maintain controls over the experience. Taking event tech into unusual environments: Sam recalls activations at music festivals, on boats, and experiences that attendees could even access while traveling because everything is web-based. Working across AI providers: Rather than building its own image models, Snapbar evaluates available third-party systems and can shift between models depending on prompt adherence, realism, animation style, and client needs. Why testing matters: AI models can regress or behave differently after updates. Snapbar's team tests experiences ahead of events, attempts to break them, adjusts prompts, and can intervene while an activation is live. Treat AI like a new team member: Sam suggests thinking of AI less like an infallible machine and more like a new employee that needs context, supervision, defined permissions, and an understanding of what it does well. AI is not automated Photoshop: Clients sometimes expect pixel-perfect control, but generative systems still involve prompting, probabilities, and outputs that cannot always be completely dictated. Why AI may not magically fix event networking: Sam pushes back on the idea that feeding hobbies and job titles into an AI matchmaking system automatically creates meaningful human connections. Event attendees ultimately want genuine personal and professional connection. The danger of easy AI startups: AI makes prototypes and products dramatically easier to build, but that also means competitors can build similar products just as quickly. Distribution becomes the advantage: Sam argues that customer relationships, audience access, SEO/AEO, reputation, and reaching the right buyers become even more important when everyone has access to powerful AI tools. Learn more about Snapbar at https://snapbar.com, which Sam specifically shares at the end of the interview.

AI gives confident answers built on missing context. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where that context gap hits go-to-market teams and how her CPG training shapes the fix. She explains treating brand marketing as full P&L ownership, not logo management, and applying classic 4P discipline—pricing, distribution, promotion, and messaging—as one system instead of disconnected levers. She contrasts this GM-style, cross-functional execution model with the narrower brand role common at most B2B SaaS companies.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Want a reason to be optimistic about the future of Marketing jobs? Duarte Garrido, Co-Founder of DOJO AI, makes a pretty great case for why great Marketers are about to matter more than ever. He ran Marketing for over a decade at giants like Coca-Cola and Sky, then set out to rebuild the broken Martech stack from scratch. He and Daniel talk about the difference between automation and intelligence, why Duarte can smell AI copy from 10 miles away, and why he says everything AI touches becomes a commodity...except this 1 thing it still can't do. If you're a Marketer who wants to use AI without turning your work into slop, this episode is for YOU. Hit follow and leave a rating if this one got your brain going. It helps more Marketers find the show. -- Own every listening moment with SiriusXM Media. As the access point to the most valuable audiences, we make it easy for advertisers to reach people across premium audio, creators, video, and live experiences—all through one connected ecosystem that's built for scale, quality, and measurable results. Learn more about the largest addressable audio ecosystem at https://www.siriusxmmedia.com/daniel -- Follow Duarte: LinkedIn: https://www.linkedin.com/in/duartegarrido/ Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter: www.workweek.com/brand/the-marketing-millennials

AI gives confident answers built on missing context. Kelly Hopping, four-time CMO and current CMO at 6sense, breaks down where that context gap hits go-to-market teams and how her CPG training shapes the fix. She explains treating brand marketing as full P&L ownership, not logo management, and applying classic 4P discipline—pricing, distribution, promotion, and messaging—as one system instead of disconnected levers. She contrasts this GM-style, cross-functional execution model with the narrower brand role common at most B2B SaaS companies.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI tools sound confident even when they're wrong. Kelly Hopping, Chief Marketing Officer at 6sense, explains where that missing context breaks go-to-market execution. She unpacks how account scoring models miss buying signals, why intent data needs human judgment layered on top, and what marketers should check before trusting an AI-generated account list.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI tools sound confident even when they're wrong. Kelly Hopping, Chief Marketing Officer at 6sense, explains where that missing context breaks go-to-market execution. She unpacks how account scoring models miss buying signals, why intent data needs human judgment layered on top, and what marketers should check before trusting an AI-generated account list.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI segments and messages without full context can be confidently wrong. Kelly Hopping, four-time CMO and CMO at 6sense, explains why more automation doesn't equal more pipeline. She breaks down multi-touch attribution across a six-month lookback, the shift from bottom-of-funnel demand gen to owned content that surfaces in LLM answers, and using gifting for deal acceleration instead of last-touch meeting bait.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI segments and messages without full context can be confidently wrong. Kelly Hopping, four-time CMO and CMO at 6sense, explains why more automation doesn't equal more pipeline. She breaks down multi-touch attribution across a six-month lookback, the shift from bottom-of-funnel demand gen to owned content that surfaces in LLM answers, and using gifting for deal acceleration instead of last-touch meeting bait.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you've ever wondered why splitting your brand budget from your demand gen budget always seems to end with brand getting cut first, this episode is for you. I sat down with Drew Neisser, founder of CMO Huddles, a peer community built exclusively for B2B CMOs with over 800 members, to dig into what's actually changed for CMOs in the AI era, and what hasn't.We talk about why AI is turning competent content into a commodity, and why that actually makes human judgment, humor, and original thinking more valuable, not less. Drew makes the case for the return of the big idea, and explains why treating brand and demand gen as separate budgets sets marketing and sales up to work against each other.If you're worried about CMO tenure or wondering whether the job is even still worth pursuing, Drew shares brand new data from a study he ran with Findem on how long CMOs actually last at public, venture backed, and private equity backed companies, and why the gap is bigger than you'd think.We also get into what a CMO should do in their first 90 days at a new company, why peer communities like CMO Huddles matter more than ever, and why knowing your customer better than anyone else in the room is still the strongest superpower a CMO has.Key Topics Covered"(00:00)" - Introduction"(00:04)" - Why CMO Huddles is built"(00:06)" - What's changed and what hasn't for CMOs in the AI era"(00:09)" - Why the pressure on marketers keeps rising"(00:13)" - Why splitting brand and demand gen backfires"(00:19)" - The rise of unified GTM teams and who should lead them"(00:21)" - Is the CMO role disappearing?"(00:23)" - The buyer journey in healthtech"(00:25)" - Agent sprawl: a new kind of MarTech debt"(00:28)" - Inside Reputracker"(00:31)" - New CMO tenure data from the Findem study"(00:36)" - The Quick Wins Playbook for a CMO's first 90 days"(00:39)" - Why marketers should join a peer community"(00:42)" - How CMO Huddles supports CMOs in transition"(00:44)" - Why AI fluency is now a make or break interview question"(00:45)" - Talking to customers: the real CMO superpower"(00:48)" - Adam's five key takeaways from the conversationIf you are interested in discussing this or any other topic, let's have a chat.  Reach out to me directly to schedule a no-obligation discussion. This isn't a sales call, but rather an opportunity to talk through your questions and challenges.Follow me on LinkedIn.Subscribe to The Healthtech Marketing Show on Spotify or watch us on YouTube for more insights into marketing, AI, ABM, buyer journeys, and beyond!Find all of our episodes on the Health Podcast Library.Thank you to our presenting sponsor, HealthcareNOW, 24/7 expert shows, interviews, and podcasts, powering healthcare leaders with innovation, policy, and strategy insights.

In this episode of Supercharge Marketing, host Thomas Klinger sits down with Melissa Day, who leads global marketing communications at Vishay Intertechnology and has spent her career untangling marketing operations across B2B and B2C. She's the person who gets called in when a company has too many tools, too much complexity, and not enough clarity on what's actually working. Melissa's story starts, fittingly, with a mistake. Early in her career, she built a webinar registration page in Marketo, hit publish, and went to bed proud of herself. Two weeks later her boss asked how many people had registered. The answer was zero. There was no confirmation, no thank-you page, no email. Just a form that led nowhere. "It was such a silly mistake that had such a huge impact to our program," she says. "It's probably why I'm so focused on ownership and accountability now, because I never want to be in that position again." That single failure shaped how she runs teams today: fewer tools, deeper trust, and a stubborn refusal to let automation replace judgment. Why You Should Care Every marketing team is being sold the same story right now: more automation, more AI, more tools, more speed. Melissa's conversation is a useful counterweight. She's not anti-automation. An automated abandoned cart email, the simplest kind of workflow, once helped her team connect to millions of dollars in sales for a B2B company using nothing more than an existing tool and a sharp observation in GA4. But she's just as clear about where automation quietly turns into a liability: nurture campaigns nobody remembers to turn off, one customer getting the same email seventeen times, tools that promise to replace a person entirely. Her rule of thumb is refreshingly unglamorous. You don't need the flashiest MarTech stack. You need to know exactly which problem each tool is solving, and you need someone accountable for watching what it does after launch. What You'll Learn Why the best ideas are often the cheapest ones. How a simple abandoned cart email, built with existing tools and zero budget, turned into a multi-million dollar win for a B2B companyHow to turn your sales team into a marketing channel. Why every customer conversation your sales team has is a brand touchpoint, and how marketing communications can arm them for itThe case for a 7-tool MarTech stack. Melissa's rule of thumb for knowing when your tech stack has grown past what your team actually needsWhere AI genuinely helps, and where it doesn't. Why her team uses AI to draft, refine, and challenge their own thinking, but never to replace a single end-to-end processWhat an accountability culture actually looks like. The mistake that shaped her leadership style, and why she'd rather own a failure early than automate her way around noticing it

How do marketers develop judgment in the age of AI? Jacey Berg, SVP of Strategy and Planning at Media Bridge Advertising, examines marketing technology's impact on talent development, training, strategy and judgment. SHOWPAGE: www.ninjacat.io/blog/wgm-podcast-the-judgment-gap-between-martech-talent  © 2026, NinjaCat

Marketing has more data, technology and specialist expertise than ever.But is all that sophistication actually making marketing better?In this edition of The Barber's Brief, Marc and Vassilis unpack five stories that all raise different versions of that question.First, AI assistants are increasingly embedded in how people discover and evaluate products, yet they still rank near the bottom of trusted sources for shopping recommendations. Marc asks whether AI will eventually become a reliable source of marketing knowledge or simply produce increasingly confident averages of everything we've already said. Then, V looks at PepsiCo's decision to consolidate its global media business with Publicis under a single operating model spanning strategy, planning, activation, data, identity and technology. The bigger question: has fragmentation itself become one of marketing's biggest effectiveness problems? Marc follows with the $700 billion MarTech delusion. One former privacy executive pulled her own data-broker profile and discovered she belonged to 500 segments, simultaneously classified as male and female, low income and high income. Research discussed in the episode suggests purchased targeting data can sometimes perform little better than chance, while contextual targeting may outperform it at lower cost. Then comes something refreshingly simple.The UPS Store has introduced its first brand character, Blu, designed to help expand the brand's mental associations beyond shipping into printing, shredding, mailboxes and other small-business services. V argues the opportunity isn't simply creating entertaining advertising; it's building a distinctive memory structure that can work across multiple buying situations. Finally, Marc's Ad of the Week goes to Canva's Wild Design: handcrafted stop-motion advertising in an era where almost anyone can generate something instantly with AI. The campaign combines showmanship with salesmanship while continuing to invest in a recurring character as a potential distinctive asset. More technology doesn't automatically mean better marketing.Sometimes the advantage may come from making the whole system work together — and remembering the fundamentals underneath it.Chapters:00:00 Welcome to the Barber's Brief01:58 Why Shoppers Use AI But Don't Trust It03:01 Who Do Consumers Trust for Recommendations?04:13 The AI Credibility Gap05:09 When Low-Evidence Categories Reward the First Answer06:23 Does AI Know Marketing Effectiveness?08:18 Is AI Learning From Its Own Slop?09:01 PepsiCo's Massive Global Media Shift10:29 The Problem With Marketing Specialization12:24 Is Fragmentation Hurting Marketing Effectiveness?15:15 The $700 Billion MarTech Delusion16:02 When Audience Data Is Completely Wrong17:30 Does Targeting Actually Work?18:42 Did MarTech Solve the Wrong Problem?19:15 What Should CMOs Do With Their MarTech Stack?21:31 The UPS Store Introduces Its First Brand Character23:25 Blu as a Distinctive Brand Asset24:21 Why Brand Characters Need Consistency25:41 Are Brand Characters Really Making a Comeback?27:20 Marc's Marketing Effectiveness Haiku28:05 Ad of the Week: Canva's Wild Design29:05 Showmanship Meets Salesmanship30:22 Building Distinctive Assets Over Time31:51 Why Canva Chose Craft in the Age of AI34:18 What's Coming Next35:38 Stay SharpEpisode Links:Shoppers ask AI for help but don't trust its adviceLink: https://www.emarketer.com/content/shoppers-ask-ai-help-don-t-trust-its-advicePepsiCo hands global media to Publicis amid transformation at CPG giantLink: https://www.marketingdive.com/news/pepsico-hands-global-media-to-publicis-amid-transformation-at-cpg-giant/829556/The $700bn DelusionLink: https://www.mi-3.com.au/26-06-2024/data-delusion-does-using-data-target-specific-audiences-advertising-actually-makeThe UPS Store enlists first brand character to support franchiseesLink: https://www.marketingdive.com/news/the-ups-store-enlists-first-brand-character-to-support-franchisees/829215/Ad of the week - Canva - Wild Design Link: https://www.adsoftheworld.com/campaigns/wild-design-f89d06d3-3b9d-4cd0-95de-189714c47446

AI agents are replacing humans as the primary marketing audience. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, has spent 25 years building growth systems across defense, healthcare, and enterprise technology. He explains how brands need to build AI awareness and visibility so agents recommend them during customer queries. He breaks down the shift from human-to-human marketing toward machine-to-machine interactions, including brands deploying MCPs that let AI agents query pricing, delivery timelines, and case studies directly.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send us Fan MailDeloitte, PwC, IBM, and 4,500 other companies use Adobe Experience Cloud products.And they can't find enough people who know how to implement them.Nick Hilton, Tyler Tu, and Brian Au from Adobe's Customer Experience Certifications team named the Adobe jobs they're struggling to fill: MarTech analysts, implementation consultants, solution architects. None of them require a technical degree – what they require is proof you can use the tools.That's what Adobe is putting in front of graduate students this fall in the Case Competition World Cup Graduate Track.The 50 teams selected get a sandbox Customer Journey Analytics environment and a real business problem to solve inside it, not documentation to read about. Plus a free Adobe certification voucher.Free to enter. Applications close September 13.Resources:Grad students, apply to the Case Competition World Cup – Graduate Track presented by Adobe by Sunday, September 13Start free with Adobe CX courses and certificationsStudents: Get 50% off your certification exam applied automatically at checkout (use your university email)Connect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

AI agents are replacing humans as the primary marketing audience. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, has spent 25 years building growth systems across defense, healthcare, and enterprise technology. He explains how brands need to build AI awareness and visibility so agents recommend them during customer queries. He breaks down the shift from human-to-human marketing toward machine-to-machine interactions, including brands deploying MCPs that let AI agents query pricing, delivery timelines, and case studies directly.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Campaign management still relies on manual, segment-based work. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains how to rebuild it with AI from scratch. He covers automating project scheduling and resource calendars, tracking completion across platforms, and moving from segment-based campaigns to true one-to-one personalization at scale.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Campaign management still relies on manual, segment-based work. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains how to rebuild it with AI from scratch. He covers automating project scheduling and resource calendars, tracking completion across platforms, and moving from segment-based campaigns to true one-to-one personalization at scale.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Album 8 Track 20: Unlocking the Predictive Advantage w/Sheldon PoonWhat happens when you mix a computer science background, a decade teaching high schoolers, and a philosophy degree? You get today's guest, Sheldon Poon, Founder and CEO of Drive Marketing, a leader driving $10 back for every $1 spent on Meta and Google ad campaigns.In this episode of Brands, Beats & Bytes, Darryl "DC" Cobbin, Larry Taman, and Sheldon Poon examine why the digital marketing playbook shifted 180 degrees. Sheldon explains how modern ad algorithms use 50,000+ data points to identify deep psychographic patterns and why forcing tight demographic boundaries actually destroys your return on ad spend. Plus, Sheldon shares a candid, real-time look at a major business misstep that reshaped how he approaches pipeline growth, agency scaling, and company culture.In today's AI infused world, we know this is an episode you'll enjoy (and learn a lot from)Don't forget to subscribe, rate, and share with a fellow Brand Nerd!Instagram | LinkedIn

Companies need their own AI intelligence layer, not just API access. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, breaks down why proprietary operating context matters more than model choice. He explains how enterprise AI accounts avoid training data leakage, why businesses should build fallback capability across multiple models, and when locally hosted open-weight models beat frontier models for high-risk processes.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Companies need their own AI intelligence layer, not just API access. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, breaks down why proprietary operating context matters more than model choice. He explains how enterprise AI accounts avoid training data leakage, why businesses should build fallback capability across multiple models, and when locally hosted open-weight models beat frontier models for high-risk processes.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketers assume AI understands business context automatically. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains why that assumption fails without proper training. He breaks down giving off-the-shelf AI tools business-specific context, setting operational constraints, and building governance frameworks before deploying them at scale.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketers assume AI understands business context automatically. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains why that assumption fails without proper training. He breaks down giving off-the-shelf AI tools business-specific context, setting operational constraints, and building governance frameworks before deploying them at scale.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most marketers treat AI as another app, not an operating system. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains how to build an agentic foundation instead. He breaks down recursive learning loops built on goal, context, measure, and levers, plus signal-based personalization that replaces static ICPs and segments. Ferreira also outlines a use-case-based rollout, starting with one workflow like campaign management, to prove ROI before scaling data infrastructure company-wide.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most marketers treat AI as another app, not an operating system. Isaac Ferreira, VP of Growth Systems and AI at Shift Paradigm, explains how to build an agentic foundation instead. He breaks down recursive learning loops built on goal, context, measure, and levers, plus signal-based personalization that replaces static ICPs and segments. Ferreira also outlines a use-case-based rollout, starting with one workflow like campaign management, to prove ROI before scaling data infrastructure company-wide.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Album 8 Track 19: How Authentic Positioning Drives Success w/Krista PattersonIn this episode of Brands, Beats & Bytes, hosts Darryl "DC" Cobbin and Larry "LT" Taman welcome positioning expert Krista Patterson, Co-Founder and CEO of Positioning Collective.Drawing from her executive tenure at tech giants like Cisco, ServiceNow, and VMware, Krista reveals why systemic business problems almost always trace back to murky positioning.Krista breaks down the distinction between targeting demographics versus targeting mindset, shares deeply personal stories about overcoming career missteps, and discusses managing high-profile partnerships like F1 McLaren. The conversation tackles modern industry shifts, from why communications leaders must question the ecological footprint of AI infrastructure to how Burger King reclaimed its market share through core positioning. Tune in for a masterclass on brand strategy, authentic leadership, and career growth.Don't forget to subscribe, rate, and share with a fellow Brand Nerd!Instagram | LinkedIn

How much of your marketing technology budget is quietly underperforming, not because the software is flawed, but because your teams can't, or won't, fully adopt it?Because real agility was never about buying faster tools. It's about having an operating model that can actually turn a tool's potential into results, and keep doing it as the tools keep changing.Today we're digging into the phase that quietly decides whether any martech investment pays off: what happens after the contract is signed with things like implementation and adoption. We'll get into:- Why rollouts stall — and why the real cause is usually the operating model, not the tool.- What it takes to move a team from "trained" to genuinely changed.- How to measure real value without fooling yourself with usage metrics.- And what shifts once the platform starts doing some of the work itself.To help me discuss this topic, I'd like to welcome, Kate Dalbey, Chief Client Officer at Qualified Digital.About Kate DalbeyAs CCO, Kate leads client management and growth initiatives at Qualified Digital, bringing over 20 years of digital marketing experience from both client-side and agency-side roles. She's responsible for driving organic and net new growth by leading multi-disciplinary teams who turn light-bulb ideas into fantastic outcomes for the brands QD works with. Kate is guided by the client philosophy that "we grow because they grow." Kate has consistently delivered year-over-year growth in her portfolio within fast-paced, high-growth start-up environments. She passionately believes that digital experiences can change people's lives for the better and works diligently to see these outcomes arrive in real time. In her daily life at QD, Kate enjoys what she calls "the relentless pursuit of better business results" and takes pride in building the teams that can create them, from idea to strategy to plan to execution and measurement. Because what's next isn't enough, it's what's north that really gets her excited. Kate's career experience is deeply rooted in healthcare, across provider, payer, and life sciences. The client roster she's managed includes category powerhouses CVS Health, CommonSpirit Health, Mayo Clinic, Kaiser Permanente, Cigna, Aetna, Sunrise Senior Living, Gelesis, UNCH, Jefferson Health, Cedars-Sinai, Spaulding Rehabilitation, UHS, and Wedgewood Pharmacy. She's also led digital transformation for AmeriGas, Hitachi Vantara, Thomson Reuters, ScanSource, U of Pennsylvania, and the Am. Board of Internal Medicine. When Kate isn't delivering her unique brand of magic at QD, she's with her husband and 2 sons. She loves cheering on Philly sports teams, coaching youth soccer with her husband, and traveling with her family.Kate Dalbey on LinkedIn---------- Resources ---------- Qualified DigitalThe Agile Brand podcast is brought to you by TEKsystems.We're proud to be a media partner for #MAICON26 - Oct. 13-15! Learn how AI can power your marketing and business and help you grow smarter. Use code AGILE150 to save!Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit!Chaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other GenAI tools. Use code AGILEBRAND for a 3-month free trial (normal trial is 14 days).The most influential minds in software, AI, and engineering leadership will be at WeAreDevelopers World Congress North America, September 23-25 in San Jose. Start building your own apps with Replit and get $20 off.Enjoyed the show? Tell us more at and give us a rating so others can find the show.Connect with Greg on LinkedInDon't miss a thing: get the latest episodes, sign up for our newsletter and more.Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology.The Agile Brand is produced by Missing Link. Hosted on Acast. See acast.com/privacy for more information.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stop AI from turning your writing into repetitive slop and replace it with authentic human voice. You will discover why AI drifts into repetitive phrasing and how to stop it. You will learn to measure your unique writing style with simple numbers that lock in your voice. You will apply a structured editing process that transforms machine drafts into polished content. You will gain confidence to command AI tools without wasting hours on endless revisions. 00:00 – Introduction 02:15 – The AI writing frustration 06:30 – Measuring your voice 12:45 – The five performance steps 18:20 – Building your writing blueprint 24:10 – Call to action Take the new course at: https://academy.trustinsights.ai/courses/ai-for-writers Watch this episode to finally break free from generic AI output and start writing with total control. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-how-ai-writes.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, let’s talk about AI for writing and for writers. We have seen no shortage of people talking about AI watermarking and all this stuff and how you can tell whether somebody’s using AI for writing or not. And we at Trust Insights have put together a new course, Trust Insights AI for Writers, for how to get AI to write better and not coincidentally, help you as a human also become a better writer. So, Katie, to start off, what are the things that when you are writing with the assistance of AI, what are the things that sort of you wish AI would do better? Katie Robbert: You know, I wish it would listen better. And by that I mean I feel like you can craft a really strong prompt. You can say, here are my writing samples, here are things that I don’t want you to do. And it kind of just like freewheels and does its own thing anyway. And I feel like that is frustrating for a lot of people. So, you know, I really try to write the first draft of things myself as the human and then know, and I’ve talked about this in the newsletter and on pod on our podcast where I bring in AI is to double check with like our ICPs or to use it as an editing tool. But then the editing gets carried away. You know, I’m not an editor and grammar is. I would like to say that the public school system failed me. You know. So I’m like a half decent writer. I have good ideas and I write in a stream of consciousness. And so I need tools or human editors to help me clean things up. And this is where I look to generative AI because the team doesn’t always have time to like fully edit my stuff. But then when I read back what the edits are or what the suggested edits are, I’m like, where did this come from? Or why did you make up a whole anecdote that never happened, but you’re saying it authoritatively? So I feel like the hallucination is the big thing. And the, you know, depending on the large language model, each model has its quirks in terms of the way that it writes or the way that it, you know, quote unquote, articulates thoughts. And so I think one of the things that you’ve shared about Claude, for example, is it likes its, you know, things in threes, like three punchy points. And it’s very much a tell that, like, oh, that’s a Claude thing. And I found that when I have, you know, an AI assistant help edit my stuff, it turns it into a lot of that, like the individual sentences, the three punchy points, like the this and this, then this or this or, you know, those kinds of ways that it writes. And that’s not what I, the human had put in. And I’m just sort of sitting there exhausted, like. But I just, I need this edited and I need it coherent and is it good enough? But did it change too much of my own human writing? So I think that, you know, when I think about using AI for writing, that’s what I’m personally struggling with is, you know, I still want to be the one writing it, but, like, I need someone to help me polish it. And the polish is like subpar. Christopher S. Penn: Why do you think AI does that? Why do you think AI behaves the way it does and turns original writing into something that sounds like slop? Katie Robbert: Oh, gosh, if only there was a course that was going to tell me the answer to this question. Christopher S. Penn: There’s something else that will also tell you the answer to that question. And that happens to be the fifth P. Katie Robbert: I should have guessed that one. Sneaky, sneaky. The framework at Trust Insights is purpose, people, process, platform, performance. Chris, you’re specifically talking about performance. And I think that where a lot of us get caught up in prompting these large language models is we think we’re being clear on the performance, but we’re not as clear as we could be. And that’s where the frustration sets in and that’s where we want to throw up our hands. And so the perfect purpose could be, I need you to edit this, you know, five thousand word essay. I need you to look for spelling and grammar and, you know, a cohesive thread like all those things. People, here’s my audience, here’s my authoritative voice, here’s my samples process. I want you to go through this and just list out the changes. Don’t change it for me, platform. This is going to be published on my blog, which is hosted here, and I’m going to add images in these places. And then performance. We typically think of performance as did we get the polished thing from our purpose? But it sounds like we are missing a lot of opportunity in the performance part of the 5Ps to really spell out what we need. Christopher S. Penn: And that is the premise of the new course. The biggest chunks of the new course that we have are twofold. One, we spend a lot of time on research because good research leads to better outcomes typically. And two, we spend a lot of time on math, which is your average writer is like, But I started with the premise for this course, that writing is code. If I put nonsense words together, you’re like, did you just get hit in the head? Like, what happened? Did you actually put decaf in the coffee maker this morning? If I don’t say words in the right order in a statistically predictable pattern, you have no idea what’s going on. You might say, these tests, coverage, adding branches, empty. Like, what? What does that mean? That’s word salad. Language follows patterns, and those patterns are predictable. And the reason why AI writes the way it does is because it’s choosing the most probable patterns, even when it doesn’t sound like you. So the first thing that we have to do is give AI performance, right? To say, this is what success looks like. And it has to be in a tangible form. The percentage of passive voice that you use in a text when you write as a human, how much passive voice do you use? The number of sentences that begin with a noun or a pronoun. What percentage of your copy is that? Is that the number of EM dashes that you use naturally in your text as a human? What is that? Our friend Anne Hanley says, I use the EM dash because I’m an actual writer, but I don’t use it in every sentence. And where AI typically goes off the rails is when it knows that a construction is probable, like using EM dashes, like using triadic rhythm, like using bicolon or isocolon. And it says, hey, I’m going to use the most probable things. But it has no concept of frequency, so it overuses it. And you get, it’s not this, it’s that in every single sentence or in, you know, Claude in particular loves bicolon. It’s. It sounds like a drum beat. One and two and one and two. And you’re like, could you please vary the beat? Katie Robbert: Right? Christopher S. Penn: When you look at a, like a slide deck Claude generates, everything is bicolon, all the headings, you know, this and this, sharp insight and this. And you’re like, my God, this is so mind numbing to read. If we give AI analysis of how our writing to begin with and say success looks like this set of numbers, now go right, then check your work and compare what you wrote versus what the blueprint is and it will go, oh, I didn’t do this at all. Like, I use 82% passive voice. Yeah, go back and fix it. But the fifth P in the 5P framework by Trust Insights is so important. We have to establish what success looks like for it so that it mathematically can go back and fix its code. Katie Robbert: So let me ask you this question though, because I will give Claude like samples of my writing and say this is what it’s supposed to sound like. Am I doing it wrong? Because I have all of these samples from like literally years and I feel like Claude or a large language model, you know, is trying to evolve my writing so that my writing fits its format, not its editing, to my writing. Like, I feel like that’s where I’m struggling. Christopher S. Penn: You are not doing anything wrong except you are asking it to count and it can’t count. And so even though it will analyze your writing as a language model, it has no clue of how to count. One of the things that’s in this course is probably worth the price of admission alone is a Python script that it has to run on a writing sample you provide that will do that fingerprint mathematically not letting the language model try to count, because language models can’t count. The Python script goes through and it counts in your original sample, this is the percentage of passive voice that you use and it writes it down as a fingerprint, as a file in your language model of choice. It works in ChatGPT, it works in Copilot. It tested in all the systems. It can then rerun that script on its output and say, initial sample, 4% passive voice, my work, 18% passive voice revision loop. I need to go back and keep revising until I hit this number. But it can’t count that by itself. It needs the support of actual code to do it. And that’s what’s in the course is pre baked. Nobody has to be coding, no coding involved. It’s bundled in. But you would drop that into your Copilot or your ChatGPT or your Claude and say, this is how you’re going to measure yourself. Yourself, you’re going to do the fingerprint and you’ll reuse that fingerprint and then you will go back and you will count using this script. And that’s, you know, again, you’re not doing anything wrong. It’s just the average non technical user doesn’t think, hey machine, I remembered you can’t count well. Katie Robbert: And I say, okay, so that’s an interesting distinction because so up until now, you know, I’ve been saying like, hey, this is the sample of my writing. And you’re right, it absolutely, it takes what I give it and it like, is like, oh, you said to do this, let me make sure that I do this. And by making sure that I do this, I’m going to do it 16 times out of the 18 sentences, but I’m also going to do this in 15 of the 18 sentences. And so it’s taking everything it knows about my style of writing and trying to jam it all into one sentence. And I’m like, whoa. Like, yes, grammatically it’s correct, but now it’s garbage because that is not at all what I wrote. And I sort of. I feel, it’s not that I feel like my hands are tied because I can’t fix it myself, but like, the reason I turned to a system for help is because I’m not an expert editor. And so I miss things that an editor would find. And I need that, like, for me, I need that kind of support of like, hey, you started a point over here and then you dropped it halfway through and you made a different point at the end. Like, you gotta pick a story and stick with it. Christopher S. Penn: And this is where these tools, these AI tools simply by themselves cannot do that. Like, they just do not understand. How do I, how do I even count? So I’ll show you an example of one of the things that is bundled in the course. And again, the average user does not need to look at this. The average user is not going to. You will just drop the file and say, machine, off you go. But it will look at thing. There’s functions in this. It says like, look at the rate per word of the this kind of word. For example, it’s often said that good writing relies on relatively few adverbs. Adverbs are words that end in ly, you know, actually, etc. AI loves adverbs, obviously, actually, and stuff like that. Which also sounds condescending. Katie Robbert: Yeah. Christopher S. Penn: And so if your writing style uses almost no adverbs, when you do a fingerprint, it will say, hey, your adverb rate per 1000 words is like 1%. And so when it goes back and counts, it’s revision, it’s edits that you had it make. And it goes, oh, I used 9% adverbs in my revision. But the target, the performance says 1%. I need to go back and fix my work. It’s a diagnostic. And that’s what’s missing from all of our prompts, because it can’t do that. That’s what’s missing from every single AI for Writers course I’ve ever taken or every session I’ve ever sat in. Nobody thinks of writing as a system of measurement, of analytics. And therefore, when AI just follows its own internal process programming the probabilities that it generates, we’re all like, why is this keeps coming out like slop? Why can I not prompt this thing to sound like me? It’s because it can’t count. And so the cornerstone really, of this entire course that we’ve created is let’s give AI the tools it needs to count, let’s give it the measures that it needs to count, and then let’s give it clear guidelines. You know, one of the things that is in the toolkit is a. Again, this is another piece of code the user, you. The user will not use. This is built into a skill that you just install, and there’s instructions on how to install it. But it will say, if you have this cadence, don’t do that. Here’s how you do it instead, right? If you do short, long, short, long cadence over six consecutive sentences, you’re writing us. You’re writing a dead drum beat. Dead. Don’t do that, do this instead. Things like that. So, for example, if you look at AI writing ChatGPT, Claude, Gemini, and you just take a step back and you look at the page, all the paragraphs are about the same length. They’re all. They all kind of look like the same gray rectangles on a page. If you were to step back and stop looking at the letters and just look at the shape. If you look at your human writing, there’s a good chance that it’s. There’s some paragraphs are real short. Maybe it’s even one word, like an emphasis one like, no, don’t do this. Right? And that’s just its own paragraph. That frequency of change in paragraph length is something that you can measure. Machines don’t know to look for that. Machines don’t even think about that. And so if we give them the tools, the counting tools to go. Katie tends to alternate her paragraph length. Sometimes her paragraph length is 11 sentences, other times it’s one. I should replicate that general pattern. Because once you give AI a pattern, it’s like, oh, I know how to do patterns. I can do this. And it goes off and it creates it. Katie Robbert: I mean, I have a lot of thoughts and comments, you know, and so like, the big elephant in the room is, you know, why are we teaching people how to train the models to write when there’s real writers out there? So, I mean, that’s a big question. So I’m just going to like, put that, like, stick it up here for a second. When I think back to like high school and middle school, for example, we generation were taught the five paragraph writing. But for a lot of us, this is like, this is how were taught to write. So the first paragraph is your opening argument. The second, third and fourth paragraph are your supporting reasons for your argument. And the fifth paragraph is your conclusion. And so a lot of us who were, you know, that was like drilled into our heads on our like yellow piece of paper with the green lines, like that’s how you’re supposed to write. And so I think what’s often frustrating for someone who writes their own stuff is because were taught to structure our writing in a certain way. It can come across as well. AI must have written that because of the structure. It’s like, no. My sixth grade middle school English teacher, when slapping rulers on tables was legal, scared the bejesus out of me and told me, this is how you have to write. And so I write the way that AI writes because it was drilled into my brain. This is how you write. And I guess I’m wondering, so when you’re saying like pattern recognition, like it learned these patterns from us. We taught it the patterns it did. Christopher S. Penn: But it is averaged together everything. And that’s why it often comes out so different than the way an individual writes. Because everyone has their own pattern distortions. Everyone has words they like, everyone has words they don’t like. Everyone has words, life experiences that will show up in your writing. AI is averaged all of that together into. And then what it does is it spits out the highest likely probability except for when it’s using watermarking. And so the five paragraph essay and that kind of blocky set. Yeah, it’s going to do that because the majority of writing it has seen on a bell curve is exactly that. If you look at the, you know, an earnings report or a press release, it is exactly that dead metronome of boring writing. The thing about writing to that. And we say this in the beginning of the course and we’ve said this in many different places. Good creative work that’s interesting is low probability. Right. You, the way you write should be surprising and different than what the way that middle school teacher taught you to write. Right? I will. There’s all sorts of expressions I’ve used for this. But if you say, you know, this is a, this works like a Prius and other people the average is this works okay. Right. This works okay. It’s boring. This works like a Prius, has a very specific connotation and A mindset behind it. And so what we want AI to recognize with the tooling in our course is recognize how the. In our individual style looks and replicate that specific pattern, not the general patterns. You’ve been trained on the general patterns that you will generate without these very rigid mathematical guardrails. Katie Robbert: Okay. One of the things that I’ve noticed and I’ve seen, and actually, this is a phrase that you use a lot. And so I guess I’m sort of asking, like, have you adopted this from AI or is it a phrase that people use and AI has adopted it is something that I see a lot in my conversations with a large language model is. And here’s the shape of the thing, and here’s the shape of it, and here’s the shape of the problem, and here’s the shape of the challenge. Well, that’s not the same shape as it was before. And I’m like, why are we talking about shapes? And to be fair, Chris, you say that a lot, but you are not in my instance of a large language model. And so I guess my question is, have you. No, you’re not. Have you brought that from working with large language models? Because that’s not a phrase that you used to say before. But I find that, like, there’s these little ticks and, you know, quirks that these large language models have, and each one has a different one of ways that it phrases things. So you’re talking about, like, the 1, 2, 3, the cadence, but then there’s also these descriptors, and I find that really interesting. And I feel like if a human is not paying attention, that very easily slips into a lot of your work. Christopher S. Penn: It. And it slips into how you think, too. Like, I find myself when I’m writing now, I go, oh, that is negative parallelism. And I will even call that out. Like, in a LinkedIn post I published this morning, I say, and to quote one of Claude’s favorite constructions, it’s not this, it’s that. Because negative parallelism, which is a form of bicolon, is. Is something that the language models use. But writing is a lot like. It’s a lot like nutrition. You become what you eat. So if you are reading AI generated text all the time and. And you start seeing that’s a sharp insight, that’s a sharp angle. It’s going to influence you. And so, yeah, I probably have picked up things because I’ve spent so much time wrestling with these tools and trying to diagnose how they do their constructions that, yeah, some of it is going to influence how I write and speak and even think. And one of the things that linguists, if you read this one, the discourse online, the, one of the things that linguists are very concerned about with AI generally is that it’s sort of a flattening of language, that the language, the English language itself is morphing because of the influence of AI on it. When you, when I saw, I think the stat was two out of three pages on the Internet are now written solely by AI. That is going to have a, a shaping effect on how we read and how. And when you see, you know, I think it was something 10 out of 1, 9 out of 10 new books submitted on Amazon was written solely by AI. That’s going to change your language. That’s going to change how you read and how you write is how you think. Katie Robbert: Yeah, it’s interesting. So let’s say I’m a brand new learner. I want to sign up for the AI writers course. What are some of the big things that I’m going to learn? I believe you’re introducing a new framework which is called craft. Can you speak a little bit about what the craft framework does and if I’m someone who’s trying to help my writing, what that means? Christopher S. Penn: Well, okay, so the CRAFT is a subset of the 5P framework, right? So the whole course is Craft fits in process. It’s a breakdown of process. But fundamentally the whole course itself is actually structured on the 5P framework. I’m the major lessons are literally right out of the 5P framework by Trust Insights because it’s the best framework for pretty much everything. CRAFT is a subset of process, which stands for create. You need to come up with the idea because AI is not going to do a good job. Then you need to do a buttload of research to inform the idea. Then you need to architect the work itself and we’ll talk about process decomposition, which is which we borrow from software development. I basically took the learnings of some software development and turned it into a writing course. Then you have what’s called fabulate, which is basically a machine. Here’s how to go and do this. How you build the. The plan for a tool to go do it. And then the last part, which is the hardest part, is the tuning part where the machine and it’s not hard for you, the user, it’s hard for the machine. You say, hey machine. Remember here’s the fingerprint that we did of how I write. You need to retune your work to match it. And then I as a human editor review and go, you missed this. These constructions are still off. And the machine’s like, okay, I’ll go fix it. You will see that in the course content to watch the tuning process go, oh, I can’t be hands off. I of the human still have a role to play as the editor saying, machine, you missed this. Katie Robbert: And I think that’s an important point because I think there’s a lot of misunderstanding as to where AI for writing fits in to an everyday process and how it can be the most beneficial. And so we’re not teaching you, hey, AI is going to replace your writing. We’re trying to teach you to more smartly and thoughtfully. Clearly I’m not a writer today to more pragmatically use AI in your writing, not just say, hey, AI do my writing. Because that’s where the slop comes from. To your point, these are word prediction machines. And so, you know, you often give examples of like cliche statements. So like, if I say it rains, AI is likely going to say it pours, you know, and so we have to be more creative than that. And that’s really where the human side of this comes in, is being very thoughtful about, you know, how can I break out of that Prius version of writing so that it’s, you know, something more interesting? Like one of my favorite song lyrics that my husband is convinced is just gibberish, which is the comfort of the knowledge of the rise above the sky, but could never parallel a challenge of an acquisition, which is not something that I think AI would necessarily come up with, but it’s a real song lyric. And it’s like that always sticks with me. Not only because when I was a teenager, me and my friends thought it was amazing to be able to recite it on command because we thought were so cool, but because it just strikes me as such an interesting way of saying something very simple. It’s an over complicated statement, but it’s not something that AI would come up with. And so I like, that’s where my brain goes, I’m writing is like, is there a more interesting way to say this other than, well, when it rains, it pours. Christopher S. Penn: And in the course you’ll actually see that in the first two steps of the CRAFT framework. The first is, you know, the creation, you, the human have to come up with the idea. And the second is the research. Because the more better research you do, the more unique the words are going to end up because you will dig deep into Subjects that maybe you didn’t even know about. So in this course I teach about building a short story and I build a 20,000 word short story in there about time travel and the research process for that, for this short sci fi story ended up pulling like five years worth of quantum physics papers and looking at the eight different forms of, you know, how physicists understand the very fabric of space time that works into the story and that creates a much richer story than just winging it. Because the research process digs super deep into low frequency words, things like Cartesian planes and Lorentzian manifolds and all these things. I just read the research outputs and going, wow, I need a PhD just to understand what the research is about. But it creates better writing. And to your point, and you know, kind of the whole point of the course is if we really, if I were to distill it down, this course is about how do you slap better guardrails on AI so that when it’s writing it follows instructions better and it brings to life the things that you want. And so the course will become available on the this coming. Well, by the time you listen to this podcast, it’s out. It will come out yesterday by the date the course is out Exactly. And you can find it at TrustInsights AI for writers. It’s 397 US and with it you get the course. You get the deep research toolkit for doing the research. You also get the writer’s suite which contains a writing planning skill, which by the way is fantastic. It will really force you, the human to think a writing fingerprint skill and a writing QA skill that will take any piece of existing writing and QA it like software against the original requirements document that you wrote for the writing. So Katie, you will see a lot of very strong parallels of the things that you’ve been writing and talking about for years about how do you do great build, great software. If writing is code, we’re going to apply the same practices. Katie Robbert: I think that’s great. And you know, I love more guardrails for AI. So I’m excited to test all of this out. Christopher S. Penn: Exactly. I hope, I hope you do. And if you’ve got some thoughts about how you have been helping AI write better and you want to share and pop by our free Slack group, go to Trust Insights AI analytics for marketers, where you and over 4,700 other marketers are asking answering each other’s questions every single day, including whether pizza sauce should be sweet or not. Katie Robbert: The answer is no. Christopher S. Penn: Wherever does you watch or listen to the show. If there’s a challenge you’d rather have it on, we are probably there. Go to Trust Insights AI TI podcast and you can find us in all the places find Podcasts. Podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity. Aiming to help organizations make better decisions and achieve measurable results through a data driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence and machine learning to drive measurable marketing ROI. Trust Insight services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and Martech selection and implementation and high level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall-E, Midjourney, Stable Diffusion and Metalama. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams beyond client work. Trust Insights actively contributes to the marketing community sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What Livestream webinars and keynote speaking. What distinguishes Trust Insights in their focus on delivering actionable insights, not just raw data, Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling this commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data driven. Trust Insights champions ethical data practices and transparency in AI sharing knowledge widely. Whether you’re a Fortune 500 company, a mid sized business or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever evolving landscape of modern marketing and business in the age of generative AI, Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

AI can make existing work faster, but its greatest value may come from helping organizations rethink the work altogether. In this episode of Leader Generation, Tessa Burg talks with Chris Colangelo, AI leader for Verizon Business Group, about how leaders can move beyond incremental improvements and use AI to solve bigger customer and employee challenges. Chris shares practical lessons from leading AI transformation at scale, including how to find the right starting points, build momentum with willing teams and help employees focus on the value they bring rather than the tasks they perform today. You'll also hear how AI is changing marketing through campaign simulation, smarter personalization and closer coordination between marketing and sales. Listen to learn why AI should start with a real problem rather than a technology, how faster processes can elevate human thinking and why understanding customer rejection may be just as important as measuring conversion. About Chris Colangelo: Chris Colangelo is the Associate Vice President of Business Technology and AI at Verizon Business, where he directs the enterprise AI strategy, technology operations, and platforms powering global sales, marketing, and customer engagement. In this role, he leads the deployment of generative AI capabilities to transform corporate workflows while managing the integration of core systems like Salesforce, Adobe Marketing Cloud, and Pega. His strategic focus centers on leveraging automation to deliver highly personalized customer journeys and accelerate large-scale digital transformations. Throughout his career, Chris has specialized in driving commercial growth through technology, notably leading large digital transformations that doubled digital sales and scaled Verizon's B2B multi-billion-dollar revenue platforms. His expertise includes sales, MarTech and RevTech innovation, e-commerce strategy, and digital customer experience. He is passionate about delivering value out of bold ideas and fighting through tough executions. About Tessa Burg: Tessa is the Chief Technology Officer at Mod Op and Host of the Leader Generation podcast. She has led both technology and marketing teams for 15+ years. Tessa initiated and now leads Mod Op's AI/ML Pilot Team, AI Council and Innovation Pipeline. She started her career in IT and development before following her love for data and strategy into digital marketing. Tessa has held roles on both the consulting and client sides of the business for domestic and international brands, including American Greetings, Amazon, Nestlé, Anlene, Moen and many more. Tessa can be reached on LinkedIn or at Tessa.Burg@ModOp.com.

Demandbase's Vice President of Product explains why they deleted a fully-approved AI architecture two months before launch — and how the rebuild surpassed some of their customer's highest expectations.Topics Include:AWS's Achint Naveen introduces Demandbase's VP of Product, Chad HoldorfDemandbase unifies sales, marketing, and revenue data into one viewNovember's architecture used many specialized agents, all committee-approvedThat design failed constantly — only a 30% conversation pass rateOn May 11th, the team deleted the entire architectureRebuilt in May with AWS Strands: one simpler, flexible agentPass rate leapt from 30% to 94% almost overnightWeek two retention rose from the low 20s to upper 80sWeekly active users grew 45% week-over-week after launchReal customer interviews play, calling the new AI a "dream"One user cut an hour-long report down to fifteen minutesCustomers now trace ad impressions directly to closed dealsHoldorf's advice: delete and rebuild when architecture gets too complexAWS's Naveen walks through Bedrock, AgentCore, and StrandsAgentCore Memory highlighted as Demandbase's next area of explorationParticipants:Chad Holdorf – Vice President of Product Management, DemandbaseAchint Naveen – Sr Account Manager, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Text us your thoughts on the episode or the show!After nearly 250 episodes and more than five years of conversations, Ops Cast is signing off, at least in its current form.In this final episode, Michael Hartmann is joined by longtime co-hosts Mike Rizzo and Naomi Liu to look back at how Ops Cast began, how the Marketing Operations profession changed alongside it, and what they learned from hundreds of guests who shared their experiences, ideas, challenges, and careers with the community.Rather than focusing on technology, analytics, or another Marketing Ops framework, this conversation reflects on the people behind the profession. The hosts revisit episodes that tackled leadership, career development, mental health, parenthood, workplace pressures, and the personal realities that inevitably become part of professional life.In this episode, we discuss: How Ops Cast grew from an experiment in 2021 to nearly 250 episodes  The conversations and guests that stayed with Michael, Mike, and Naomi  Why understanding human psychology remains an important Marketing Ops skill  Giving first-time podcast guests a platform to share their experiences and build confidence  Some of the more personal conversations around parenthood, mental health, career challenges, and life outside work  How remote work changed the separation between professional and personal life  The growth of Marketing Ops, RevOps, and the wider operations community during the life of the podcast  How the Marketing Ops podcast space expanded over the past five years  What the hosts learned from CMOs and other executives about how they view Marketing Operations The episode is also a reminder of what made Ops Cast work in the first place: there was never one prescribed path into Marketing Operations or one correct way to approach the work. Across hundreds of conversations, guests brought different backgrounds and opinions while repeatedly returning to the importance of curiosity, authenticity, human connection, and learning from one another.While this marks the end of Ops Cast in its current form, the conversations, relationships, and community built around it continue.Thank you to every guest who shared their story, every person who helped produce the show, and everyone who listened over the years.Episode Brought to You By MO Pros The #1 Community for Marketing Operations ProfessionalsSupport the show

Clean data beats clever algorithms in AI marketing. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years of enterprise go-to-market experience to the data-versus-AI debate. She argues AI functions as an operating system, not a standalone tool, becoming the intelligence layer powering interconnected agent workflows. Wong stresses that AI output quality depends entirely on data hygiene, making clean data infrastructure a prerequisite for effective AI-driven decision-making.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Clean data beats clever algorithms in AI marketing. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years of enterprise go-to-market experience to the data-versus-AI debate. She argues AI functions as an operating system, not a standalone tool, becoming the intelligence layer powering interconnected agent workflows. Wong stresses that AI output quality depends entirely on data hygiene, making clean data infrastructure a prerequisite for effective AI-driven decision-making.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketing teams keep paying for tools they no longer need. Katrina Wong, Chief Marketing Officer at New Relic, explains why AI is replacing entire categories of MarTech software. Her team canceled a competitive intelligence subscription after building roughly 85 AI agents to gather and organize the same data. The output now feeds one-pagers and an interactive Gemini gem, giving sales real-time answers without waiting on a team or logging into a clunky dashboard.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Marketing teams keep paying for tools they no longer need. Katrina Wong, Chief Marketing Officer at New Relic, explains why AI is replacing entire categories of MarTech software. Her team canceled a competitive intelligence subscription after building roughly 85 AI agents to gather and organize the same data. The output now feeds one-pagers and an interactive Gemini gem, giving sales real-time answers without waiting on a team or logging into a clunky dashboard.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rising AI costs are quietly eroding marketing's bottom line. Katrina Wong, Chief Marketing Officer at New Relic, has led seven successful exits and now factors AI spend directly into cost of goods sold. She breaks down the buy-versus-build decision for AI agents, explains why New Relic won't automate end-to-end campaigns yet, and shares how her team is orchestrating agents across marketing handoffs to control costs.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rising AI costs are quietly eroding marketing's bottom line. Katrina Wong, Chief Marketing Officer at New Relic, has led seven successful exits and now factors AI spend directly into cost of goods sold. She breaks down the buy-versus-build decision for AI agents, explains why New Relic won't automate end-to-end campaigns yet, and shares how her team is orchestrating agents across marketing handoffs to control costs.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Summer Remix: Album 8 Track 12: Win the Room - Accelerate Your Career w/Bernard BellHow do you go from "Step Zero" to a Senior VP role at a major cable network? Bernard Bell, known as "The Confidence Professor," joins the podcast to explain why Executive Presence is the ultimate career accelerator.In this candid conversation, Bernard breaks down the difference between networking and "engaging," the importance of leading like a "shepherd who smells like his sheep," and why culture will always trump strategy. He also shares the high-stakes story of a $55 million programming misstep and the valuable lesson in humility and due diligence it taught him. Tune in to learn how to curate your personal brand and win the room before you ever open your mouth.In this episode, you'll learn:The F.O.R.M. Framework: A tactical guide to engaging anyone, anywhere.The 8-Slice Pizza of Success: Why your degree is only one slice of your professional value.Ego Management: Why Bernard keeps his ego in his back pocket to lead effectively.The $55 Million Mistake: A vulnerable look at what happens when instinct replaces market research.Whether you're a junior marketer looking for your first break or a Director aiming for the CMO chair, this episode is your blueprint for career acceleration.Don't forget to subscribe, rate, and share with a fellow Brand Nerd!Instagram | LinkedIn

AI adoption is upending marketing budgets. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years leading marketing and go-to-market strategy across enterprise tech companies including Twilio Segment, Hired, Zuora, Salesforce, and SAP. She discusses experimenting with OpenAI's ChatGPT app program despite unproven ROI, treating emerging AI platforms as a budget hypothesis rather than a guaranteed channel, and rethinking SEM investment as LLMs become a default search interface.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI adoption is upending marketing budgets. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years leading marketing and go-to-market strategy across enterprise tech companies including Twilio Segment, Hired, Zuora, Salesforce, and SAP. She discusses experimenting with OpenAI's ChatGPT app program despite unproven ROI, treating emerging AI platforms as a budget hypothesis rather than a guaranteed channel, and rethinking SEM investment as LLMs become a default search interface.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI development costs won't outpace developer salaries by 2028. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years of enterprise marketing leadership and AI-driven go-to-market expertise. She predicts market competition and open models will keep AI tooling affordable, while token costs won't scale to $250,000 per developer per year. Wong also points to one-to-one personalized marketing and selling as the next frontier AI will unlock.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI development costs won't outpace developer salaries by 2028. Katrina Wong, Chief Marketing Officer at New Relic, brings 20+ years of enterprise marketing leadership and AI-driven go-to-market expertise. She predicts market competition and open models will keep AI tooling affordable, while token costs won't scale to $250,000 per developer per year. Wong also points to one-to-one personalized marketing and selling as the next frontier AI will unlock.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI is helping marketers move faster, but not always smarter. Katrina Wong, Chief Marketing Officer at New Relic, explains how her team turns raw data into decisions leadership can trust. She covers using AI to analyze recorded sales calls for unbiased win-loss insights, deploying real-time in-product signals to personalize the self-service funnel, and building a human-in-the-loop fact-checking process to catch AI drift before it erodes trust in the data.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

AI is helping marketers move faster, but not always smarter. Katrina Wong, Chief Marketing Officer at New Relic, explains how her team turns raw data into decisions leadership can trust. She covers using AI to analyze recorded sales calls for unbiased win-loss insights, deploying real-time in-product signals to personalize the self-service funnel, and building a human-in-the-loop fact-checking process to catch AI drift before it erodes trust in the data.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Text us your thoughts on the episode or the show!AI is changing how people search for information, evaluate options, and make buying decisions. But what happens when customers stop searching for information themselves and start asking AI to solve the problem for them?In this episode of Ops Cast, Michael Hartmann is joined by Len Ward, Managing Partner and Head of AI at Commexis, to discuss the shift from a traditional “search and retrieve” model to a “solve my problem” model and what it could mean for Marketing Operations.Drawing on more than two decades of experience across traditional marketing, digital, analytics, and AI, Len explains why the rise of LLMs could challenge long-standing assumptions about websites, SEO, content, lead generation, and the customer journey.In this episode, we discuss:How Commexis evolved from traditional and digital marketing into analytics and AI What the shift from “search and retrieve” to “solve my problem” means for marketers How LLMs could change the role of websites, content, SEO, and owned channels Why traditional search and lead-generation models may come under increasing pressure Len's prediction about Google and what declining search dependence could mean for marketers What organizations should consider building as traditional acquisition models change How agent-to-agent marketing could reshape the way buyers and companies interactWhat Marketing Ops leaders should prioritize over the next 12 to 18 months The conversation also explores a much bigger question for Marketing Operations. If AI increasingly handles research, comparison, and decision support for customers, how should companies rethink the systems and processes they have spent years building around the traditional buyer journey?For Marketing Ops and RevOps leaders trying to prepare for what comes next without chasing every new AI development, this episode offers a thought-provoking look at where AI may have its biggest impact and why the goal should be solving customer problems better, not simply doing existing work faster.Episode Brought to You By MO Pros The #1 Community for Marketing Operations ProfessionalsSupport the show

Most marketing campaign workflows will disappear within three years. Brendan Farnand, Co-founder and Chief Evangelist at Knak, spent the past year talking with over 100 enterprise marketing teams—including OpenAI, Google, Stripe, and AT&T—to learn where AI helps campaign production and where it breaks. He explains what separates companies experimenting with AI from the ones actually shipping better marketing, and how campaigns should function in the AI era.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.