We’re sitting down with Zainab Hussain, an e-commerce strategist specializing in customer engagement and the evolving digital landscape. As artificial intelligence fundamentally reshapes how consumers discover products and information, brands are scrambling to understand a new set of rules. Today, we’ll explore the critical, yet often overlooked, role of third-party content in this new AI-driven search ecosystem. We’ll discuss the significant risks of focusing solely on owned media, the data-driven strategies for identifying influential external sources, how to measure the real-world impact of offsite placements, and the operational decisions brands face when implementing these new tactics.
With up to 85% of brand discovery in AI search reportedly coming from third-party content, what are the biggest risks for brands that only focus on their own websites? Can you provide an example of how this blind spot could negatively impact a company?
The biggest risk is, quite simply, invisibility. When you realize that a staggering 85% of how AI systems discover and recommend brands is shaped by external sources, you understand that your own website is just a small piece of a much larger puzzle. AI models are designed to find a consensus and get the “full picture” by looking across the web—they read listicles, comparison guides, and community discussions. If you’re not mentioned in those places, for all intents and purposes, you don’t exist in that part of the conversation. Imagine a new direct-to-consumer luggage brand. They might have a beautifully optimized website, but if AI answers for “best carry-on luggage for international travel” are pulling from ten different travel blogs that don’t mention them, they will be completely absent from the recommendation. They’ve essentially been ghosted by the very technology their future customers are using.
Your Offsite platform uses a proprietary “Influence Score” to rank publishers. Could you walk us through the key factors that determine this score and explain how it helps teams prioritize their efforts more effectively than traditional outreach guesswork?
Absolutely. The “Influence Score” is what transforms this process from a shot in the dark to a precise, data-backed strategy. We moved beyond simple domain authority and looked at what AI systems actually value. The score is built on three core pillars: first, citation frequency, meaning how often a source is actually referenced in AI-generated answers for a specific category. Second is topical relevance, ensuring the publisher is a true authority in that niche. And third is overall influence, a broader measure of its authority. This data-driven ranking allows a team to immediately see which publishers will have the most significant impact. Instead of just guessing which blogs might be good to pitch, you can confidently say, “This publisher is cited in 30% of AI answers for our key topics; they are our top priority.” It replaces guesswork with a clear, actionable roadmap.
Once a brand secures a placement on a high-influence site, how can they measure the direct impact on their visibility in AI-generated answers? What specific metrics should a team track over time to prove the ROI of these offsite efforts?
This is where the loop closes and you can prove tangible value. Measurement isn’t about vanity metrics; it’s about tracking direct causation. After securing a placement, the key is to monitor how that specific mention affects citation patterns for relevant prompts and topics over time. The primary metric to watch is your brand’s citation rate within AI-generated answers for your target queries. For example, if you get featured in a prominent financial tech publication, you should be tracking prompts like “top budgeting apps for families.” Before the placement, your citation rate might have been zero. After, you can watch that number climb. This provides a direct line between the offsite placement and your increased visibility, giving teams a single, unified view of performance and a powerful way to demonstrate a clear return on investment.
You offer both a self-serve platform and a fully managed service. What specific internal resources should a brand have to succeed with the self-serve model, and at what point does it become more practical for them to choose the managed service instead?
The choice really comes down to a team’s internal capacity and strategic priorities. For the self-serve platform to be successful, a brand needs an in-house team with the bandwidth and expertise to manage the entire process. This means having dedicated personnel who can analyze the data to identify citation gaps, use the platform to prioritize those high-value publishers we discussed, and then handle all the outreach and relationship management directly. It’s perfect for teams that have established content or PR functions. However, it becomes much more practical to opt for the fully managed service when a team is looking to move faster or simply wants to avoid adding complex new workflows internally. If a company wants the results without building out the function themselves, our managed service handles everything end to end—from outreach and coordination to the final reporting.
What is your forecast for AI search?
My forecast is that the traditional silos between on-site SEO, content marketing, and off-site public relations are going to completely dissolve. Winning in the age of AI search won’t be about mastering one of these areas, but about executing a unified content engineering strategy. Brands will have to view the entire web as their potential footprint, not just the pages they own. The future is about ensuring your brand’s story and value are authentically woven into the third-party sources that AI systems trust most. Success will be measured not just by your own website’s rank, but by your presence in the broader digital ecosystem that ultimately shapes how AI discovers, understands, and recommends you.
