The Digital Marketing Podcast

How to Fix AI Slop: Better Context, Stronger Brands and the Return of Human Judgement What happens when AI makes it easier to produce marketing content, but that content all starts to look and sound the same? In this episode of The Digital Marketing Podcast, Ciaran Rogers speaks to Evan Greally, founder and CTO of Pacey AI, about avoiding AI slop and getting back to the work that matters: understanding customers, developing ideas and building a distinctive brand. Introduced by Daniel Rowles, the conversation draws on Evan's experience as Head of Creative Tech and Innovation at Droga5 Dublin, part of Accenture Song, and more than 15 years working where creativity and technology meet. What begins with an AI-powered mint plant becomes a wider discussion about data overload, creative control and whether AI can develop something resembling taste. Along the way, Evan explains the thinking behind Pacey, with lessons that extend well beyond any single platform. In This Episode From a talking mint plant to a conversation with nature: Evan shares the story behind The Talking Tree, a project that combined environmental sensors, plant responses and AI-generated conversation. It is an unusual starting point with a serious creative question behind it: could technology encourage people to reconnect with the world around them, rather than spend more time staring at a screen? Why marketing needs to ask "so what?": Customer reviews, social comments, competitor activity and dashboards can all contain useful information. But what should a marketer actually do with it? Ciaran and Evan explore the gap between collecting data and making decisions, and why adding another tool can make that gap wider rather than close it. The value of "honest silence": Evan introduces Pacey's principle of surfacing what matters without manufacturing urgency. He describes a system that looks for support from three or more sources before highlighting an issue, but can also tell a team there is nothing significant to worry about that day. The aim is to focus attention, not create another stream of notifications. Can AI really have taste? Evan argues that AI can develop a useful form of brand-specific taste, while distinguishing it from the personal experience and judgement that shape human taste. He explains how approved copy and visuals help build a brand's taste profile, and why evaluating work from an audience member's perspective may be more revealing than asking a model to give it a numerical score. Would someone actually stop scrolling? Turning a daily briefing into useful action: From a short, podcast-style morning update to content creation, scheduling and performance feedback, Evan outlines how Pacey connects market signals with marketing activity. The discussion explores what changes when a team can move from understanding an opportunity to developing a response without repeatedly copying information between disconnected tools. Why editable creative matters: Ciaran highlights the importance of layered design files rather than finished images that are difficult to change. Evan explains how this approach supports human editing, reusable templates, a brand's own photography and different identities across multiple outlets. He also previews an editable video workflow using customers' own footage, rather than relying entirely on generated visuals. An orchestra, not another isolated instrument: Could marketers build something similar by connecting their own AI tools? Evan acknowledges the potential for prototypes, but explains why filtering data, maintaining context, applying design constraints and continually evaluating results are harder to reproduce. The conversation uses an orchestra analogy to explore how different models and specialist agents can work towards the same creative goal. Building trust before speeding things up: Evan shares a product-development lesson: giving people a login is not the same as helping them adopt a new way of working. Slower onboarding, visible sources and clear explanations of how conclusions were reached became central to building confidence. Throughout the discussion, people remain responsible for deciding what is right for the brand and approving what gets published. Key Takeaways Start with the marketing decision, not the dashboard. Before collecting another metric or adding another tool, ask what it will help you understand, change or prioritise. Give AI direction, not just information. Relevant customer signals, a clear brand perspective and a defined purpose matter more than handing a model an undifferentiated pile of data. Treat creative approval as a learning opportunity. Human choices about copy and design can help establish what good work looks like for a particular brand. That judgement should shape the process, not disappear from it. Keep the work editable and the reasoning visible. Teams need to be able to refine creative assets and inspect the evidence behind recommendations, rather than simply accept whatever a system produces. Use automation to make room for better thinking. The ambition discussed here is not to fill every channel with more posts. It is to give marketers more time to understand their audience, develop ideas and deliver the right message at the right moment. Can AI learn a brand's preferences well enough to demonstrate taste, or does that word belong to human creativity? Listen to the conversation and share your perspective with the team. As Daniel explains in the introduction, The Digital Marketing Podcast has no commercial connection to Pacey AI. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/how-to-fix-ai-slop

AI tools are evolving fast. One of the biggest shifts is the move from one-off prompts to reusable AI skills. Instead of rewriting the same instructions every time you use ChatGPT, Claude or Copilot, you can package your best workflows into portable skills that deliver consistent, repeatable results. In this special episode, recorded as the Digital Marketing Podcast celebrates its 16th anniversary, Daniel Rowles and Ciaran Rogers explore why AI skills could fundamentally change the way marketers work. They explain how skills fit alongside projects, agents and prompts, why they are becoming an emerging standard across the major AI platforms, and how to start building your own library of reusable expertise. Along the way, they also reflect on sixteen years of podcasting, discuss the rapid pace of AI adoption within organisations, and share practical advice for creating skills that become more valuable every time you use them. In This Episode How AI skills differ from prompts, projects and agents. Why reusable skills help create consistent marketing outputs across teams. How to build a brand skill that captures tone of voice, design principles and coding standards. Using skills to create websites, dashboards, articles and creative assets that stay on brand. Why updating a skill after every use makes it progressively more accurate. How skills can be shared between team members and even moved between AI platforms. The relationship between ChatGPT Agents, Claude Projects, Gemini Gems and Microsoft Copilot. How to turn your best prompts into reusable skills. Why marketers should organise AI conversations more deliberately as AI memory improves. Practical advice for creating libraries of reusable marketing workflows. Key Takeaways: Develop your best prompts first, then convert them into reusable skills. Treat skills as repeatable processes rather than one-off conversations. Build separate skills for different purposes, such as branding, content creation, web development or analysis. Use multiple skills together to combine different areas of expertise within a single task. Continuously refine your skills by asking AI to update them after each iteration. Think carefully about when to use a prompt, project, skill or agent depending on the task. Organise your AI workspace to avoid confusion as conversations and memories accumulate. Create shared skills that help maintain quality and consistency across your entire marketing team. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/how-to-create-ai-skills

Why do so many beautifully designed websites, ads and digital experiences still fail to convert? In this episode of The Digital Marketing Podcast, Daniel Rowles is joined by Kramer Reeves, CEO of EyeQuant, alongside neuroscientist Professor Peter König, one of the company's founders and a leading expert in visual attention and cognitive science. Together, they explore how neuroscience and AI are reshaping the way marketers, designers and brands understand attention. The conversation dives into the hidden mechanics behind how people actually experience digital content. From subconscious processing and visual saliency through to cognitive overload and design clarity, this episode challenges many of the assumptions marketers make about what users notice, understand and act on. The discussion also explores how AI-powered predictive attention modelling can help teams test creative assets before launch, reducing guesswork and improving performance across websites, ads, landing pages and social content. If you work in UX, creative, digital marketing or conversion optimisation, this episode offers a fascinating blend of neuroscience theory and highly practical design advice. In This Episode Why the human brain is poorly adapted to navigating modern web experiences What happens cognitively in the first few seconds after landing on a webpage Why designers often view their own work very differently from first-time users How "system one" thinking drives most user behaviour online What visual saliency means, and why it matters for conversion How cluttered creative and overloaded messaging dilute attention Why visual hierarchy, contrast and scale shape user behaviour The importance of creating a clear visual storyline through a page or ad How AI models can predict where users are most likely to look Why neuroscience is becoming increasingly important in modern marketing and UX How EyeQuant uses predictive attention modelling to help brands optimise creative before launch Why objective testing is becoming essential in an AI-generated content landscape Key Takeaways Attention is highly selective. You cannot make everything on a page the top priority simultaneously The brain processes visual information subconsciously long before conscious reasoning begins First impressions are heavily influenced by saliency and visual contrast Designers are often too familiar with their own creative to judge it objectively Reducing visual noise can significantly improve clarity and conversion potential Motion, scale and contrast naturally attract human attention because of evolutionary wiring Effective design is not just about aesthetics. It is about guiding behaviour AI-driven predictive testing can dramatically speed up optimisation workflows Clear intent and defined outcomes are becoming increasingly important inputs for creative analysis Objective attention data helps teams move beyond subjective design debates 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/design-that-sticks-applying-neuroscience-to-predict-attention

What happens when a global brand hands over its creative control to unfiltered customer feedback? In this episode of The Digital Marketing Podcast, Daniel Rowles speaks with Emily Barfoot, Head of Dove Beauty at Unilever, about a bold and unconventional campaign built on Reddit conversations. At a time when trust in polished marketing is eroding and AI is reshaping how content is discovered, Dove leaned into one of the internet's most unpredictable platforms. By amplifying genuine, unedited user reviews, both positive and negative, the brand not only drove awareness but also delivered measurable commercial impact. This episode explores how marketers can rethink control, embrace transparency, and tap into community-driven credibility. In This Episode How Dove identified an organic Reddit conversation and turned it into a multi-channel campaign Why Reddit's "unpolished" nature makes it a powerful platform for trust and insight The strategy behind publishing the first 50 real user reviews without editing or filtering How Dove balanced positive and negative sentiment to build credibility The role of social listening in shaping campaign direction and optimisation How Reddit content was extended into out-of-home, influencer, and experiential marketing Why transparency and respecting platform culture were critical to success How the campaign delivered over 260 million impressions and triple-digit growth in the product category The importance of many-to-many marketing in modern brand building How Reddit insights fed directly into product innovation and development Key Takeaways Authenticity is not a tactic. It is a strategic commitment that requires letting go of control User-generated content can outperform traditional creative when it reflects real experiences Reddit is not just a media channel. It is a rich source of customer insight and product feedback Brands must respect platform norms and community expectations to earn participation Balanced sentiment, including negative feedback, increases trust and credibility Social listening should be continuous and actionable, not just observational Experiential marketing can amplify digital engagement when it creates shareable moments AI-driven discovery makes community-led content more valuable than ever Strong products and existing brand trust are essential before taking transparency risks Marketing success increasingly depends on enabling conversations, not controlling them 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/reddit-marketing-case-study-with-dove-turning-real-reviews-into-real-growth

As AI rapidly reshapes how customers discover, evaluate, and engage with brands, marketers are being forced to rethink the fundamentals of the customer journey. In this episode of The Digital Marketing Podcast, Daniel Rowles speaks with Rachel Thornton, CMO of Adobe Enterprise, to explore what this shift really means in practice. Recorded just ahead of Adobe Summit, Rachel shares exclusive insights into Adobe's latest thinking, product direction, and how enterprise marketers should adapt to an increasingly AI-driven landscape. From evolving user expectations to the rise of AI as the primary interface, this conversation goes beyond the hype to examine the real implications for strategy, data, and customer experience. In This Episode How AI is transforming the traditional linear customer journey into something far more dynamic and unpredictable Why discovery and consideration are being reshaped by AI-powered interfaces and assistants What Adobe is announcing at Summit and how it reflects broader industry shifts How CMOs should rethink customer engagement in an environment where AI intermediates interactions The growing importance of first-party data and real-time personalisation How enterprise organisations are adapting their tech stacks to support AI-led experiences The challenges of maintaining brand consistency when AI is part of the customer interface What marketers often misunderstand about AI and where to focus instead How internal teams and workflows need to evolve to keep pace with change Key Takeaways The customer journey is no longer linear. It is fluid, fragmented, and increasingly influenced by AI-driven touchpoints AI is becoming the primary interface between brands and customers, which changes how influence and trust are built Marketers need to prioritise high-quality, structured data to enable effective AI-driven personalisation Speed and adaptability are now critical competitive advantages in digital marketing Organisations must rethink not just tools, but also teams, processes, and skills There is a growing need to balance automation with authentic brand voice and human oversight Experimentation is essential. Waiting for certainty in AI adoption is likely to result in falling behind 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/the-changing-customer-journey-in-an-ai-first-world-insights-from-adobe-cmo