BrainLabs

Google is reportedly alpha-testing channel-level controls within Performance Max, which could give advertisers a new way to influence how Google values individual channels including Search, YouTube, Display, Discover, Gmail and Maps. Google has not publicly confirmed the test. Search marketing advisor Heidi Sturrock was among the first to identify the functionality and share it on LinkedIn, so the details may still evolve before any broader rollout.

TikTok is the latest platform to bring agentic AI directly into advertising workflows with the launch of TikTok Ads MCP and Agentic Hub. And while the MCP connector itself is important, the more interesting development might be what TikTok is building on top of it.

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I came away from Affiliate Summit East thinking about an assumption we’ve made about affiliate marketing for years: that its primary job is to capture demand that’s already there. That made sense when the affiliate journey was easy to see. Someone was considering a purchase, encountered a publisher, comparison site or offer, clicked through and converted. Affiliate sat close to the transaction, so we built programs, budgets and measurement around that role. But that journey is changing.

Most marketing organizations have clear owners at either end of the funnel. Brand teams build awareness and performance teams convert demand into revenue. The middle tends to be less clear. Responsibility is spread across media, content, social, CRM, creators and brand, while each team works towards its own objectives. That becomes a problem when awareness isn’t translating into enough people who seriously consider buying.

Your platform numbers say a channel is delivering strong ROAS. Your MMM suggests you’ve probably overspent there. An incrementality test indicates a positive lift. Which number should determine where the next dollar goes? The instinct is often to deem one of these as the ‘Source of Truth’, but in every likelihood all of these models are wrong to a certain degree. Each measurement method is giving you an answer, but most likely to different questions.

AI Search has quickly moved from an emerging behavior to a critical channel marketers know they need to prioritize. But deciding how much to invest is complicated by a more fundamental problem: AI Search doesn’t fit neatly into the way marketing budgets are structured. Winning visibility across LLMs requires technical SEO, content, digital PR, organic social, influencer and measurement to all work together. Yet most of those capabilities already have their own budgets, owners and objectives.

AI is already making more of the day-to-day decisions behind media execution, from monitoring performance to optimizing spend. The temptation is to treat that growing capability as a roadmap toward full autonomy: if AI can eventually do something, the goal should be to remove the human from it. We think that’s the wrong target. The best media operating model isn’t fully automated or fully manual.

Marketing teams have gotten very good at testing. New audiences, channels, creative formats, bidding strategies, landing pages and platform features (too long a list) can all enter the experimentation roadmap, often with an expectation that a mature marketing organization should always have more tests running. But the number of experiments you run tells you very little about how much you’re learning.

For enterprise brands with large physical footprints, localization creates an uncomfortable trade-off. Centralize too much and media loses the local context that makes it engaging and effective. Push too much control into individual markets and you create duplication, inconsistent measurement and an operating model that becomes increasingly difficult to wrangle as the business grows. The answer may seem overly simplistic – to separate what genuinely needs to be local from what doesn’t.