AI Ad Placements: How to Use Them & Are They Worth It?

This month’s Ask the PPC question cuts to the core of a topic I’ve been watching closely since 2024: AI ad placements. They’ve been quietly appearing in chat experiences and search results, yet they remain shrouded in uncertainty for many advertisers. The question at hand is simple but loaded: “Ads are starting to show up in AI chat experiences. How should advertisers think about these new placements – and are they worth the budget?”

Because I work at Microsoft, I can’t directly compare competitor value. But I can offer a general framework for understanding AI ad inventory, how to measure its impact, and how to budget for it effectively.

How To Access AI Ad Inventory

You have two primary paths to purchase AI ad placements: buy directly through an AI-first platform or access them through your existing paid media buys on major ad networks. Neither approach is inherently superior, but they demand distinct strategies.

Direct buys guarantee your entire media spend goes to that specific AI surface. This makes it easier to tailor creative and measurement to that environment. These placements are often sold on a CPM or CPC basis, varying by platform and market.

On the other hand, when you access AI surfaces through broader campaign types like Performance Max, Shopping, or Search, your creative gets adapted dynamically to fit the AI experience and user intent. This fluidity is critical. Rigid creative requirements, including pinning, can hinder performance because AI needs flexibility to match the human conversation.

If your brand has strict language or term constraints, most platforms are testing ways to add human oversight. However, if you require absolute control over creative, you may not fully capitalize on AI surfaces. AI-assisted campaign types like AI Max and Performance Max tend to perform best here due to their creative flexibility, broad matching, and dynamic audience mapping. Standard Search and Shopping formats can also qualify depending on the experience, market, and query intent. Some platforms even incorporate rich multimedia formats when they meet relevancy and policy standards.

Remember, AI surfaces extend beyond assistants like ChatGPT and Copilot. They also include AI modules on the search engine results page, such as AI Overviews and Answer Card Formats on Bing.

The user experience is paramount. AI recommendations must feel organic, not purely sponsored. That’s why many platforms clearly separate citations from paid placements and enforce a high ad relevancy bar. Structured commerce data,accurate pricing, availability, shipping, returns, and customer service details,helps AI systems surface trustworthy options and builds user confidence.

How To Think About AI Placement Metrics

A common mistake is viewing AI as either purely discovery or purely bottom-of-funnel performance. In reality, AI can compress consideration cycles dramatically, sometimes moving a user from discovery to conversion in under 30 minutes. Internal Copilot analyses show these placements can achieve up to 25% greater relevancy compared to similar SERP placements for the same intent.

However, AI placements also impose a stricter relevancy standard than conventional SERPs. This can raise questions about volume and whether they represent a viable stand-alone investment.

AI is blurring the line between brand and performance media buys. That’s why conventional ROAS and CPA goals may not be the best metrics for these surfaces. Some AI experiences allow you to build audiences from engagement signals, while others function more like awareness buys, focusing on exposure and consideration rather than immediate conversions. Judging them solely on last-click data will often undervalue their true impact.

Adopting data-driven attribution,already standard on Google,gives you a fuller picture of how different engagements contribute to conversions. But to fully leverage AI placements, you must also incorporate brand sentiment, citation share, and other awareness metrics.

Use AI visibility tools and on-site behavior analytics to understand how often AI systems reference your brand and what users do after landing. Session replay and UX analytics tools like Microsoft Clarity, Hotjar, and FullStory can help identify friction, intent mismatches, and content gaps that matter across all traffic sources.

When reporting is limited, focus on directional measurement: compare conversion quality, watch assisted conversion paths in data-driven attribution, and run structured tests like geo splits or budget-in/budget-out experiments to estimate incremental lift. Pair this with brand-aware signals such as direct traffic, branded search demand, and share of citation in AI answers to avoid undervaluing upper- and mid-funnel impact.

Building Budget For AI Placements

So, are AI placements worth it? If a 194% better conversion rate,based on Microsoft internal data,appeals to you, then the creative flexibility AI requires is a worthwhile trade-off. If your brand cannot accommodate that flexibility, the value proposition collapses because rigid compliance will limit where and how creative can adapt. That’s why major ad platforms continue to offer options that honor strict creative and policy constraints.

As noted, major platforms often provide access to AI surfaces through existing campaign types. Pricing has appeared directionally similar to comparable non-AI inventory once you normalize for intent and competition, though actual costs vary by market, query class, and supply.

AI-first platforms may price inventory differently, reflecting limited supply and stricter user-experience constraints. Practically, you need enough daily budget to exit the learning phase and generate meaningful signals before judging performance. Instead of anchoring on a universal CPC, build a test budget based on your category’s typical costs, your conversion rate, and the minimum volume needed to make a decision.

Budget also includes time for managing creative, targeting, and outcomes. AI creative offers options to inform users about your product or service before they click through or allow the agent to complete a transaction within the AI experience.

Final Takeaways

Keep these key points in mind about AI ads:

• They don’t always serve. When they do, it’s because the platform is highly confident the ad will benefit the user.