In this episode of the Paid Search NYC Podcast, host Matt Shenton is joined by Navah Hopkins from Microsoft to break down what Copilot actually is, how it works, and what it means for marketers navigating an AI-driven search and discovery landscape.We go from high-level Copilot use cases and model selection, through to what AI shopping journeys look like in practice, why feed hygiene now matters more than ever, and how Microsoft is bringing Copilot into ad creation and campaign workflows.If you’re trying to understand how AI assistants are changing discovery, commerce, and paid media execution, this is a very practical place to start.🔑 Key Insights from the Episode- What Copilot is — and what it isn’t- The difference between Smart, Think Deeper, Study & Learn, and Search- How to think about model choice depending on the task- Why thread memory matters differently across Copilot modes- How AI assistants move from discovery to transaction in one journey- Why feed hygiene and crawlability are critical for AI commerce- What happens when product feeds aren’t accessible to AI systems- Why brand still matters in upper-funnel AI discovery- How ads show up inside Copilot — and when they don’t- What Microsoft Ads Copilot tools can do across creative and campaign workflows- How Ad Studio helps with brand-safe creative generation- Why AI should be treated as support for marketers, not a replacement⏱ Key Sections & Timestamps00:23 – Intro01:37 – What Copilot is and why it matters04:21 – Returning to threads and how Copilot memory works07:39 – Smart vs Think Deeper vs Study & Learn vs Search09:49 – Which Copilot modes work best with longer threads11:02 – Testing Copilot with a real prompt12:39 – Organic results, product discovery, and early shopping intent16:23 – Why these results are still organic20:13 – Discovery journeys, upper funnel behavior, and advertiser implications21:22 – Why brand and feed hygiene matter more than ever27:59 – Why some shopping results are organic, not ads29:26 – Where transactional intent changes the experience31:17 – What ad inventory can show inside Copilot32:01 – Reporting and measurement limitations today33:34 – Copilot Labs and creative use cases36:15 – Ad Studio, brand kits, and AI-assisted creative workflows40:19 – Using Copilot inside PMAX and campaign creation43:37 – Rewriting messaging and creative with Copilot44:25 – Different levels of control for different marketer workflows45:42 – Final thoughts: AI in service of humanity47:19 – Outro

Most conversations around Google Ads automation focus on tools.This episode is about the system behind it.In this conversation with Nils Rooijmans, we break down how to automate e-commerce Google Ads using scripts, AI and SOPs — and why most advertisers are still thinking about automation the wrong way.We cover how to structure your accounts, where automation actually adds value, and how to combine scripts with AI to scale performance without scaling headcount.Key Insights:- Why most Google Ads automation focuses on the wrong things- How to turn PPC into a system using SOPs- The role of scripts vs AI in modern PPC workflows- How scripts handle 60–80% of account work- Why Performance Max often optimizes for spend, not profit- The “exploration vs exploitation” approach to scaling- How to structure Standard Shopping campaigns for control- Using scripts for monitoring, alerts and decision support- Why AI should be treated like an “intern”- How to scale accounts without scaling teamsTimestamps:00:00 Scripts vs agents00:31 Introduction01:31 How Nils built a script-driven PPC agency03:19 Why you do not need coding skills to use scripts04:24 What the team actually does in a script-led agency08:25 Why SOPs matter before AI10:20 What Google Ads scripts actually are12:54 E-commerce Google Ads automation examples14:38 Profit-first conversion tracking17:07 Using return-rate prediction in e-commerce19:05 Feed optimisation and why Nils pushes back on Performance Max22:07 The standard shopping gatekeeper structure25:52 Exploration vs exploitation in Google Ads27:11 Bidding strategy within the gatekeeper setup29:18 Are scripts agentic?32:01 Why the future is scripts + AI33:31 Final thoughts

In this episode of the Paid Search NYC Podcast, host Matt Shenton is joined by Stewart Dunlop (Founder, PPC.io) to break down what AI is actually good at today - and where it still falls short.We go beyond the hype into real workflows: how to use tools like ChatGPT and Claude Code effectively, why context is the most important input, and how marketers can start building repeatable systems using AI - not just one-off prompts.Stewart also shares a behind-the-scenes look at building PPC.io - a platform designed to run AI agents across paid media accounts — and what it takes to turn AI from a novelty into something genuinely useful.🔑 Key Insights from the Episode- Why most AI outputs are weak (and how to fix it with better context)- The difference between chat-based AI and agent-based workflows- How tools like Claude Code enable faster, more scalable execution- Why “skills” and structured instructions outperform one-off prompts- How to turn your SOPs into repeatable AI workflows- Where AI adds the most value (and where it still needs human input)- Why documentation is the biggest unlock for using AI effectively- How agencies can use AI to scale work across multiple clients- The concept of “roundtable agents” and stress-testing decisions- Why execution is becoming table stakes — and where real value shifts⏱ Key Sections & Timestamps00:00 – Intro01:30 – AI hype vs reality: where it actually works05:00 – Why context is everything in AI prompting08:30 – Improving outputs with better inputs12:00 – Chat AI vs agent workflows explained16:00 – Introduction to Claude Code20:30 – Building workflows inside VS Code26:00 – Using AI for landing pages and reporting32:00 – Where AI still falls short36:00 – The importance of documentation and SOPs40:00 – From workflows to agents: the next step 45:00 – Building PPC.io (behind the scenes)50:00 – Testing agents, prompts, and models55:00 – Multi-agent systems and “roundtable” decision making59:00 – Final thoughts on AI in marketing