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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

In this episode of The Digital Marketing Podcast, Daniel Rowles is joined by Kendall Breitman from Riverside for a practical conversation about how marketers can use video without needing a full production team, a huge budget or a complicated workflow. Together, they explore why video has become so central to modern marketing, how brands can use it to strengthen SEO and answer engine optimisation, and why authentic, human-led content is outperforming overly polished corporate production. The discussion also dives into practical ways to build a video strategy from webinars, podcasts, customer conversations and expert interviews, then repurpose that long-form content into clips, blog posts, newsletters and more. It is a useful episode for any marketer looking to make video simpler, smarter and more sustainable. In This Episode Why video is becoming essential for SEO, AI overviews and discoverability across search and social platforms How authentic, human-centred video is replacing overly scripted and expensive marketing production Why social media works better when brands behave more like people than polished corporate entities How marketers can use customer interviews and user conversations as both research and content Why webinars, podcasts and expert interviews can become the foundation of an entire month of content How long-form content can be repurposed into medium-form YouTube videos, short-form social clips, blogs, newsletters and gated assets Why medium-form video is often overlooked, despite being highly effective for search visibility and website engagement The most common mistake brands make with video: failing to record valuable conversations and insights in the first place How video testimonials and advocacy content can support trust, social proof and brand visibility Where AI is genuinely helping video production, from transcripts and clips to editing support and audio clean-up Key Takeaways Video works best when it feels useful and human, not overly produced. A single long-form recording can power a much broader content strategy than a series of disconnected short-form posts. Customer conversations are valuable twice over. They generate insight for the business and content for the brand. Video strategy should be tied to business goals, whether that is awareness, education, trust or loyalty. Searchable, practical content such as straight-to-camera answers and product walkthroughs can deliver strong SEO and AEO value. AI should support storytellers, not replace them. The best use of AI is reducing friction so people can create more authentic content. Brands that build confidence in video now will be better placed as the format becomes even more central to digital marketing. The future of video is likely to be more accessible, more efficient and more human, not less. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/practical-video-marketing

What happens when you combine practical digital marketing experience with behavioural science? In this episode, Daniel Rowles is joined by Phil Agnew, host of the Nudge podcast and a specialist in behavioural science, to explore eight psychology experiments and principles that can help marketers create more effective campaigns, stronger customer experiences and more persuasive messaging. Phil shares the original studies behind concepts such as social proof, loss aversion, anchoring and the peak-end rule, then shows how they can be applied in real marketing scenarios, from Reddit ads and SaaS websites to loyalty programmes, pricing pages and customer journeys. The result is a highly practical episode for marketers who want to sharpen their thinking and make better decisions in a world full of noise, automation and increasingly generic content. In This Episode Social proof: why it still works, why specificity matters, and why implying popularity can be more powerful than simply claiming it Loyalty and endowed progress: how giving customers a sense of momentum can make them more likely to complete a journey and stay engaged Loss aversion: why messages framed around what people stand to lose can outperform those focused only on gains The pratfall effect: how showing a flaw, when paired with clear competence, can make a brand or person more likeable Distinctiveness: why standing out matters even more in an AI-saturated content landscape Anchoring: how the first number, comparison or frame people see can radically shape how they judge value The peak-end rule: why customers often remember the emotional high point and the ending of an experience more than everything in between Visible effort: why people value products, services and content more when they can see the work behind them Real examples from digital marketing: including Reddit ad testing, website messaging, social proof banners, pricing psychology and travel search UX Key Takeaways Behavioural science is most useful when it is translated into practical tests, not treated as abstract theory Social proof works best when it feels natural and contextual, rather than overly promotional Small shifts in wording can have a major effect on click-throughs, conversions and retention Customers do not always judge experiences rationally. They remember moments, contrasts and endings Showing some humanity or imperfection can make brands feel more credible and relatable Distinctive positioning is becoming more valuable as AI makes average content easier to produce at scale Helping customers feel progress, momentum or visibility into effort can improve engagement and loyalty Marketers should revisit core psychological principles before chasing every new platform or tool 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/8-psychology-experiments-for-marketers

What happens when clicks get harder to earn, paid media gets more expensive, and audiences spend less time engaging with static content? In this episode of The Digital Marketing Podcast, Daniel Rowles explores why gamification is moving from a nice-to-have tactic to a serious strategic opportunity for marketers. This episode is split into two parts. First, Daniel sets out the business case for gamification in today's digital landscape, where AI overviews, rising cost per click and declining organic reach are all putting pressure on traditional marketing performance. Then he is joined by Luke Santamaria from PlaySpark, a specialist in branded interactive games, to look at how brands are using mini-games, playable ads and reward mechanics to build attention, loyalty and conversion. In This Episode: Why gamification is becoming more relevant as search, social and paid media become less predictable How owned engagement can become a stronger asset than rented attention What gamification really means in a marketing context, beyond simply making things fun How progress tracking, reward loops, challenge and achievement create stronger audience engagement Why quizzes, assessments, calculators, onboarding journeys and email programmes can all benefit from gamified thinking How interactive content can outperform static pages on engagement, return visits and conversion Why the rise of AI tools and vibe coding has made it far easier to create interactive experiences at lower cost How branded mini-games can support both loyalty and acquisition strategies What Luke Santamaria from PlaySpark has learned from building interactive campaigns for retailers, hospitality brands and loyalty programmes Why prizes and rewards can dramatically increase participation and completion rates How playable ads work across platforms such as Meta and mobile gaming environments What marketers should measure when testing gamified campaigns, from play rate and completion rate through to redemption and sales How personalisation could shape the next phase of gamified marketing experiences Key Takeaways: Gamification is not just about entertainment. It is a structured way to design more engaging marketing experiences. As AI overviews and no-click behaviour reshape digital marketing, brands need to offer experiences that search engines and summaries cannot replicate. Interactive content can help improve conversion efficiency, especially when traffic is harder or more expensive to acquire. Simple mechanics such as progress bars, unlockable content, rewards and challenges can strengthen repeat engagement. Gamification can work across B2C and B2B environments, particularly when the incentive is meaningful to the audience. Mini-games and playable ads can hold attention for longer than traditional ad formats and create more active brand involvement. Measurement matters. Success should be judged using both game metrics and commercial outcomes. New AI-assisted creation tools are lowering the barrier to entry, making experimentation far more accessible for marketing teams. The biggest opportunity may be in combining gamification with owned channels such as email, apps, learning platforms and loyalty programmes. Brands that start experimenting now may benefit from a genuine early-mover advantage. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/gamification-for-digital-marketing

In this episode of The Digital Marketing Podcast, Daniel Rowles is joined by Emma Tronson, Deputy Director of Marketing at Aston University, for a practical, honest and refreshingly structured look at how to move beyond AI experimentation and into real organisational adoption. Many teams are stuck in what Daniel calls "pilot purgatory". Everyone is testing tools. Everyone is experimenting. But very few organisations have truly embedded AI into day-to-day operations. Workloads have not reduced. If anything, they have increased. This episode explores what it actually takes to embed AI into a complex organisation with multiple stakeholders, legacy systems, risk concerns and competing priorities Emma shares her step-by-step journey of launching an AI task force, securing leadership buy-in, aligning with institutional strategy, and creating a structured framework for adoption that delivered tangible outcomes within a year.In This Episode: From career setback to AI champion Emma explains how missing out on a promotion led her to proactively position herself as indispensable by embracing AI learning Mission AI Impossible How she pitched a structured AI task force aligned to Aston University's 2030 strategy and secured budget, time and executive support. Why time matters more than money Resource allocation and protected "lab time" proved more critical than financial investment. The six focus areas model Data and analytics, general work support, copywriting, visual and creative, SEO and web, and social and community. Each task force member owned one domain. Start small and build confidence Select staff were upskilled first before launching more widely across the department. Align AI to real use cases Experimentation was always tied to live marketing and admissions challenges rather than abstract testing. Governance before scale Clear internal guidance helped build confidence in responsible AI usage. The system integration challenge Legacy systems and risk aversion slowed deeper embedding, a common challenge across many organisations. Cohort Two and scaling adoption Expansion into wider teams with three new priority workstreams: personalisation, data, and custom builds. Why AI will not replace marketing teams Instead, it will reward those who actively upskill and adapt. Key Takeaways: AI adoption requires structure. Align initiatives to business strategy from the outset. Start with a focused pilot group, then scale. Protected experimentation time is essential. Bring sceptics into the process rather than excluding them. System integration and governance are often the biggest blockers. Innovation must be baked into everyday work, not treated as a side project. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/how-to-drive-ai-adoption

In this episode of The Digital Marketing Podcast, Daniel Rowles sits down with Kipp Bodnar, CMO of HubSpot, to discuss what may be the most disruptive year in marketing history. Kipp believes that 2026 could represent the biggest single wave of change our industry has ever seen. Weeks feel like months. Channels are fragmenting. Discovery is shifting. AI agents are entering workflows. And traditional attribution models are starting to break down. From Answer Engine Optimisation to AI agents, rising ad costs to workflow automation, this conversation explores how marketers can stay ahead when the pace of change is accelerating. In This Episode: Why 2026 may be the biggest year of change in marketing history Kipp explains why discovery, personalisation and team workflows are being reshaped simultaneously. Answer Engine Optimisation vs traditional SEO The shift from short keyword queries to ultra long-tail, conversational prompts of 40 to 60 words changes everything. Mentions vs citations in AI search Why brand visibility in ChatGPT, Gemini and Claude is more complex than link-based SEO ever was. The first mover advantage in AI discovery Early adopters can make exponential gains because competition is still low and optimisation is immature. Why AI agents are thriving in customer service but lagging in marketing Marketing problems are less formulaic and more complex, making agent adoption slower but highly promising. The practical AI workflow hack every marketer should try Record yourself completing a repetitive task, upload it to Google Gemini, and ask how to automate it. A simple but powerful shortcut to AI adoption Why attribution is becoming harder again The "golden age" of clean click-to-conversion tracking is fading as AI intermediates discovery. Rising ad costs and the need for new growth channels With paid media inflation increasing, marketers must adopt emerging channels such as AEO and AI-enabled creative optimisation. The importance of strategic conviction AEO cannot be treated as a side project. It must be embedded as a core capability. HubSpot's approach to AI and context Positioning HubSpot as the context layer for AI, enabling agents and assistants to work from real customer data. Key Takeaways: Discovery is changing faster than most organisations are adapting. Answer Engine Optimisation requires different content structures, including FAQs and machine-friendly formatting. Early adoption in AI search offers outsized returns. AI-assisted workflows are often more impactful than fully autonomous agents in marketing today. Marketing teams must bake experimentation and innovation into daily operations. The biggest risk is not AI itself, but failing to evolve working practices alongside it. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/the-hubspot-cmo-interview-kipp-bodnar-on-ai-answer-engine-optimisation-and-the-future-of-marketing

AI is accelerating change at a pace most organisations have never had to manage before. For marketers and business leaders, this is not just another wave of innovation. It is a fundamental shift from linear change to exponential change, where familiar planning cycles, transformation programmes, and long-term roadmaps start to break down. In this episode of The Digital Marketing Podcast, Daniel Rowles is joined by Geoff Tuff, Consulting Principal at Deloitte and co-author of Hone: How Purposeful Leaders Defy Drift. Together, they explore why traditional transformation initiatives fail so often, and what leaders can do instead to build organisations that continuously adapt without losing their sense of direction. Drawing on real-world examples, behavioural science, and lessons from Geofff's latest book, the conversation reframes change as an ongoing discipline rather than a one-off event, and offers a practical way to respond to the disruption created by AI and other exponential forces . In This Episode Why AI represents a shift from linear to exponential change, and why this makes traditional planning models fragile Why large-scale transformation programmes fail so frequently, and the hidden costs they create for organisations The concept of "drift" and how companies slowly move away from their original purpose without realising it The difference between sharpening and honing, and why constant realignment beats radical reinvention How leaders can "bake in" innovation so it becomes urgent, not just important Why behaviour, not technology, is the real engine of change inside organisations The role of management systems as the invisible drivers of culture and decision-making What Minimally Viable Moves are, and how they help organisations adapt without overcommitting Lessons from Amazon and Jeff Bezos on designing systems that reinforce the right behaviours How informal signals from senior leaders can unintentionally shape priorities and outcomes Key Takeaways Exponential change makes waiting for the perfect moment to transform a dangerous strategy Most organisations drift off course through accumulated small decisions, not dramatic failures Honing is about continuous, incremental alignment rather than destructive overhauls Leadership power lies less in vision statements and more in the systems that guide everyday behaviour Small, testable moves reduce risk and keep organisations responsive in uncertain environments What leaders praise, question, or ignore can matter more than formal strategy documents Staying aligned to purpose requires constant attention, not periodic transformation projects 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/dealing-with-exponential-change-leading-in-an-ai-driven-world

The generative AI landscape has shifted again, and at speed. In this update episode, Daniel Rowles breaks down a rapid sequence of releases from OpenAI and Google, explaining what has actually changed, what genuinely matters, and how marketers and business leaders should respond. Covering everything from ChatGPT 5.x and Gemini 3 through to image generation breakthroughs, AI-powered apps, and the rise of accessible 'vibe coding', this episode is a practical briefing rather than hype-driven commentary. Daniel focuses on real-world capability, usability, and where these tools are already changing how marketing teams work, build, and analyse. Rather than asking which model is "winning", the episode reframes the question around task-based selection. Different tools now excel at different jobs, from coding and image editing to workflow automation and reporting. Understanding those strengths is quickly becoming a competitive advantage In This Episode How the AI model race intensified between OpenAI and Google at the beginning of 2026 What actually changed with ChatGPT 5.1 and 5.2, beyond the headline claims Why Gemini 3 represents a major leap in reasoning and coding capability How image generation tools have crossed a new threshold in photorealism and editing What Nano Banana Pro means for branded, on-style visual creation Why ChatGPT connectors becoming "apps" is more important than it sounds How tools like Canva, Adobe, and Figma now integrate directly into AI workflows What vibe coding really is, and why it matters for non-technical marketers How canvas mode transforms the way code, content, and interfaces are created Why AI-powered browsers and agent modes could redefine reporting and analysis Key Takeaways No single AI model is best at everything, and choosing by task is now essential Image editing and brand-consistent visuals are becoming dramatically more accessible Marketers can now create interactive tools and content without traditional development AI-assisted coding is shifting from novelty to practical everyday use Workflow automation and reporting are emerging as some of the biggest wins Understanding interfaces and deployment matters as much as model intelligence 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/generative-ai-update-what-the-latest-model-wars-mean-for-marketers

Search is changing fast. With AI overviews now appearing in the majority of Google results, the rules of visibility are being rewritten in real time. In this episode, Daniel Rowles explores what answer engine optimisation actually means in practice, and how marketers can adapt without chasing myths, hacks, or constantly shifting theories. Drawing on real testing from Target Internet over the past year, this episode cuts through the noise around GEO, AEO, and SEO to focus on what genuinely works. The result is a practical framework for improving visibility both in traditional organic search and within AI-powered results, particularly Google's AI overviews. If you are worried about falling click-through rates, changing user behaviour, or how large language models decide what to reference, this episode provides clear, grounded guidance. Rather than treating AI search as a black box to be cracked, Daniel reframes it as a probability system influenced by clarity, credibility, and consistency. The five techniques covered here are designed to future-proof your content strategy while still delivering value to real users today. In This Episode Why the industry cannot agree on GEO vs AEO, and why the distinction matters less than you think What current data tells us about AI overviews, click-through rates, and changing search behaviour How question-led content aligns with the way people now search using AI Why inverted pyramid writing is suddenly more important than ever How digital PR and brand mentions influence entity reputation in AI systems The growing role of advocacy, reviews, and online sentiment in visibility Why multimodal content is becoming a core ranking signal, not a nice-to-have How schema markup removes ambiguity for search engines and large language models Where to focus your effort while Google's AI results are still evolving rapidly Key Takeaways Answer engine optimisation overlaps heavily with traditional SEO, so doubling down on fundamentals is still smart Clear, direct answers to specific questions improve your chances of being referenced by AI Brand visibility is no longer just about links, but about being mentioned in the right contexts Positive sentiment and third-party validation increasingly influence AI recommendations Publishing content in multiple formats strengthens your overall search footprint Structured data helps machines understand your content, and humans engage with it more easily 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/answer-engine-optimisation-aeo-how-to-optimise-for-google-ai-overviews

In this episode of The Digital Marketing Podcast, Daniel Rowles speaks with Phil Treagus-Evans, author of the new book Human First Marketing, to explore why now, in a world dominated by AI, automation, and synthetic content, marketers must rediscover their humanity. As generative AI reshapes how content is produced, and digital channels become flooded with bland, algorithmically generated material, brands risk losing the very thing that makes them relatable: their people. Phil offers a timely and practical guide for putting humans back at the heart of your marketing strategy, from customer research to team culture and executive presence. Drawing on his work at Giraffe Social, Phil breaks down a four-part framework for building trust, fostering connection, and standing out in a sea of sameness. If you've ever felt like your content is just noise, or you're posting because you're "supposed to", this episode will help you hit reset. In This Episode: Marketing isn't working like it used to Phil opens with a bold claim, and backs it up with data on ad saturation, trust erosion, and content homogenisation due to AI. What is Human First Marketing? A new approach that prioritises people over platforms, empathy over efficiency, and purpose over vanity metrics. The four pillars of Human First Marketing People: Deep customer understanding and employee advocacy Authenticity: Real voices and honest content Connection: Interactions over transactions Purpose: Strategy that means something — and gets results Why you should ditch customer avatars Phil introduces his EMPATH framework for audience research that avoids reductive stereotypes and leans into emotional understanding, patterns of behaviour, and real team insight. The power of visibility and vulnerability From CEOs posting on LinkedIn to sharing the full journey of writing his book, Phil explains how authenticity builds trust, and drives results. Why trust is the new marketing currency In an era of deepfakes and automated outreach, being seen as real, consistent and trustworthy is the ultimate competitive edge. AI isn't the enemy, lazy AI is Phil and Daniel agree: AI is a fantastic tool, but it's the human decisions around how, when and why we use it that matter most. How Human First Marketing improves SEO As Google leans into E-E-A-T (Experience, Expertise, Authoritativeness, Trust), human-centred content becomes not just ethical, but algorithmically rewarded. Key Takeaways: Human content performs better - Whether it's lo-fi video or founder-led storytelling, people trust people more than brands. Leaders must be visible - Exec comms and internal advocacy aren't optional anymore, they're how culture scales and trust grows. Culture comes first - You can't fake authenticity. If your team doesn't believe in what you do, neither will your audience. Strategy before content - Don't just post because you should. Post with intent, empathy, and impact. There's power in transparency - Sharing your struggles, your processes, and your people builds emotional equity. Use AI as a creative partner - Not a content machine. Challenge your thinking, spark ideas, don't outsource your voice. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/human-first-marketing-why-trust-purpose-and-people-matter-more-than-ever

In this episode of The Digital Marketing Podcast, Daniel Rowles and Ciaran Rogers dive into one of the most significant shifts in digital marketing: the rise of non keyword signals and what it means for search, privacy, and the future of campaign targeting. With AI-powered ad platforms like Google's AI Max and social algorithms driven by behavioural data rather than search intent, the old model of keyword-driven marketing is rapidly giving way to something more opaque, more personalised, and potentially more invasive. Daniel and Ciaran explore how AI systems now use a vast array of signals, from your browsing patterns and email content to your location history and viewing habits, to anticipate what you might want before you even search. But what does this mean for marketers who've spent years honing their SEO and PPC strategies? And what does it mean for user privacy in a world where your chatbot conversations may be fuelling ad targeting? In This Episode: What are non keyword signals? Understand how platforms like Google and Meta are using behaviour, context and historical activity instead of just search terms. Why your impressions are up but clicks are down Learn how AI summaries in Google Search are reducing organic click-throughs, even when your rankings are strong. AI Max campaigns and keywordless targeting Discover how Google Ads is shifting towards AI-led campaigns that rely on intent and engagement signals rather than keyword triggers , and why some brands are seeing 20–30% uplift as a result. What Meta's CMO said about disconnected content Hear how Alex Schultz explained the shift from connected (likes, follows) to disconnected content (Reels, Stories, Shorts), and what that means for social strategy. Privacy concerns and grey areas Explore the implications of Meta's new terms allowing AI chat interactions to inform ad targeting, and whether AI chat data could be inadvertently shared or commercialised. Real-world scenario: Chatbots used for lead capture Daniel raises a powerful example, what if you offered a free AI chatbot to other brands, then harvested user intent data for your own ad targeting? Platform security, data visibility and agent risks From OneDrive access errors to AI agents managing your logins, the hosts discuss the human errors that AI amplifies, and the need for robust security practices. Key Takeaways: We're entering a post-keyword era Platforms are using AI to interpret intent through broader, richer data sources, including chat behaviour, video viewing and app usage. Organic traffic is being cannibalised by AI summaries Even if your visibility improves, actual user clicks may continue to decline. Privacy risks are growing Terms and conditions are changing, and conversations with AI tools may no longer be as private as they seem. Marketing teams must audit how they use AI Understand what data your AI tools are accessing and how that data could be used, now and in the future. Join the conversation This isn't just a podcast - it's an open debate. Daniel and Ciaran want your input, and they're giving away prizes, books and merch to contributors. 📥 Access the show notes, tools, and links at: https://targetinternet.com/resources/the-big-debate-non-keyword-signals-ai-chats-privacy Join the debate: https://www.linkedin.com/feed/update/urn:li:activity:7403862051749564417