Challenges Brands Face With AI in Advertising

Mark Zuckerberg just outlined a future where Meta's AI handles everything from creative generation to campaign optimization to purchase decisions. His vision: businesses connect their bank accounts, state their objectives, and "read the results we spit out." The technical architecture he's describing would fundamentally reshape how advertising technology works. But there's a critical flaw in this approach that creates an opportunity for the next generation of advertising infrastructure. The trust problem isn't just about measurement transparency—though agency executives are rightfully skeptical of platforms "checking their own homework." The deeper issue is institutional knowledge transfer and real-time brand governance. Enterprise brands have decades of learned context about what works, what doesn't, and what could damage their reputation. This isn't just about brand safety filters. It's about nuanced understanding of seasonal messaging, competitive positioning, cultural sensitivities, and customer journey orchestration that can't be reverse-engineered from campaign performance data alone. If AI truly automates the entire advertising stack, brands will need their own AI agents—not just dashboards or approval workflows, but intelligent systems that can negotiate with vendor AI in real-time. Think of it as API-level conversation between two AI systems where the brand's AI has veto power over creative decisions, placement choices, and budget allocation. This creates fascinating technical challenges: How do you architect AI-to-AI communication protocols that maintain brand governance while enabling real-time optimization? How do you build systems that can incorporate institutional knowledge without exposing competitive advantages to vendor platforms? We're talking about building advertising technology that functions more like autonomous diplomatic negotiation than traditional campaign management. For platform companies pushing toward full automation, the question becomes whether they're building systems that enterprise clients can actually trust with their brands and budgets. For independent technology builders, there's an opportunity to create the middleware that makes AI-powered advertising actually viable for sophisticated marketers. The future of advertising isn't just about better algorithms—it's about building trust architectures that let those algorithms work together.

👩💻 AI in Advertising: Tool for Equality or a Bias Multiplier? There is a problem with AI transforming advertising. It learns from historical data, and that data isn’t always fair. AI can reinforce gender bias in ad targeting and creative decisions. 🔹 Ad targeting bias: Leadership roles often shown more to men than women. 🔹 Skewed AI-generated content: AI assumes men = executives, women = caregivers. 🔹 Image recognition flaws: Women, especially women of color, are underrepresented. 🚨 So what can advertisers do? 💡 Here’s how to fight AI-driven bias in PPC: 👉 Review AI-powered ad placements: Check if your campaigns actually skew toward certain demographics and adjust targeting manually. 👉 Diversify creative inputs: We power AI with our quality inputs so use diverse text, image assets, and targeting to avoid reinforcing bias. 👉 Advocate for fairness: Push ad platforms to improve transparency in AI-driven ad delivery and bias mitigation. This International Women’s Day, let’s make AI a tool for equity, not exclusion. 💜 What else can advertisers do to reduce AI bias in PPC? Drop your thoughts below! 👇 #IWD25 #googleads #ppc

🚨 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆? The customer journey is being rewritten by AI. 🚨 As Michael Cohen revealed in his insightful Los Angeles Times article, “After the Search Engine: Brand’s Enduring Value in the Age of AI-Directed Customer Journeys,” #AIAgents are now mediating how consumers encounter and choose brands. The funnel is collapsing, and the brand story risks getting lost in the algorithm. 👟 Enter Maya: Instead of browsing for new running shoes, her AI agent handles everything from turning discovery, evaluation, and purchase into a single, invisible step. No serendipity. No brand connection. Just an “optimal” choice. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: Brands risk becoming invisible, and consumers like Maya lose agency and delight as algorithms become the new gatekeepers. 𝗔 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Building on the LA Times article, my latest essay weaves in the latest California Report on Frontier AI Policy and fresh thinking from behavioral economics for marketing. I explore what CMOs must do to keep brands differentiated and meaningful: - investing in proprietary data - championing transparency - ensuring that delight and discovery aren’t casualties of automation. If you’re a marketing leader navigating this new landscape, this piece is for you. 🙏 Many thanks to Michael Cohen for the industry leading, thought provoking piece and being the inspiration for this Part 1 of 2 part series. Many many thanks so a few of the new subscribers to the newsletter: Cris B., Leigh-Brindeland (Brin) Moore, Pritam Gondkar. Curious what resonates with you. Let me know in the comments below what you think of the article. Would this help a colleague? Please share. I help CMOs and Marketing leaders figure this AI stuff out. Let's chat. #AIMarketing #BrandStrategy #BehavioralEconomics #CMO #3StandardDeviations

Mark Zuckerberg is building the future of advertising, and news flash: It doesn’t include a marketing team. In Meta for Business’s new AI ad engine, businesses won’t need creatives, strategists, or even agencies. You set a goal. Link your bank account. And the system does the rest. It writes the copy. Generates the creative. Targets the audience. Tests thousands of variations. Optimizes outcomes. It’s fast. Scalable. Frictionless. But here’s the tradeoff: When every brand uses the same system, running on the same logic, with the same optimization goals, what happens to brand identity? What happens to story? To meaning? Because performance might sell a product, but it doesn’t build a brand. And the more this system scales, the more we risk flooding feeds with generic, high-frequency AI slop. Ads that technically “work,” but leave no emotional imprint. That’s the real risk: a generation of brands that are efficient, but forgettable. The brands that will win in this landscape won’t reject AI. But they won’t hand over complete control to it either. They’ll use AI tactically to scale what resonates, not flatten it. To support the story, not replace it. Because when everything is optimized, the only thing that cuts through is anything that actually means something. #ai #digitalmarketing #socialmediamarketing