AI-driven discovery and the new SEO: How UGC shapes modern search

Jennifer Montague, Senior Director of GoToMarket at Verdane, argues that the SEO playbook is being rewritten by AI-driven discovery engines - and the new currency is authentic third-party conversation. When real customers talk about your brand through reviews, video tutorials, and social content, AI systems treat that signal as authority. For retailers, this shifts user-generated content from a nice-to-have social tactic into foundational infrastructure for how products get found.

The search landscape has shifted under everyone’s feet

Traditional SEO optimized for keywords, backlinks, and structured data. That playbook still matters, but it now operates alongside a fundamentally different discovery layer: AI systems that synthesize third-party signals rather than crawl first-party content.

Gartner predicted in early 2024 that traditional search engine volume would fall 25% by 2026 as AI chatbots and virtual agents absorb more discovery queries. That trajectory is already reshaping how consumers find products - and it goes a long way toward explaining why organic traffic patterns have been shifting without an obvious cause.

Jennifer’s view, formed across a decade working with B2B and B2C brands in the Nordic startup ecosystem, is that this shift demands a different question. The old question was “Do we rank for the right keywords?” The new one is “Are other people talking about us in ways that AI systems can interpret as trust?”

UGC is the new link equity

The mechanism Jennifer identifies is straightforward: user-generated content creates dispersed, authentic signals that AI discovery engines have learned to weight as credibility cues. In B2B, brands lean on third-party review platforms to accumulate that signal; in B2C, customers sharing tutorials, unboxing videos, and product testimonials produce the kind of distributed real-world content that AI systems are trained to surface as evidence of fit.

“UGC is gold for AI discoverability,” Jennifer notes in the episode - the AI wants to see other people talking about you, and that signal feeds directly into how brands get surfaced.

PowerReviews’ annual consumer research finds that 99.9% of shoppers read reviews at least sometimes before buying. With AI Overviews and conversational search now curating that content directly into results pages, the UGC a brand generates no longer just converts visitors who arrive at the product page - it shapes whether those visitors arrive at all.

This is what Jennifer means when she describes UGC as killing multiple birds with very few stones. The video review someone posts to earn a discount is not just social proof for the next shopper who finds the product page. It is a signal to AI systems that real humans consider the product worth discussing, and that signal feeds back into discoverability.

The practical levers she points to: loyalty programs, referral incentives, and review-for-discount campaigns. All of them activate the community and generate distributed content at the same time. Retailers that treat these purely as retention programs are leaving a significant discoverability benefit on the table.

The authenticity constraint

There is a condition attached to everything above, and Jennifer is direct about it: this only works if the brand deserves it.

Incentivizing customers to go online and talk about a poor experience accelerates reputational damage rather than discoverability. The community activation strategy she describes - loyalty nudges, personalized re-engagement, referral campaigns - assumes that the underlying product and service create experiences worth sharing. The new SEO is not a content production tactic. It requires actually being good enough for people to want to talk about you.

This shifts the strategic frame in an interesting way. AI discovery optimization, done seriously, creates pressure to improve the product and customer experience - because authentic experience is what generates the signals that feed the system. That is a different accountability structure than keyword optimization, which could be gamed without improving anything downstream.

Personalization at scale: Where “hollow” enters

After covering discoverability, Jennifer moves to what happens when a shopper actually arrives - and here she offers a critique that many retailers will recognize.

She is broadly positive about the direction of personalization. The “people who bought this also bought that” pattern, the outfit-completion suggestion, the abandoned-cart email with a 10% discount - these work because they are grounded in real behavioral signals and arrive at a moment of genuine relevance. Behaviorally triggered email flows operate on exactly this principle: they fire based on what the shopper actually did, not on a scheduled broadcast cadence.

But Jennifer draws a sharp line: “Personalization at scale, if it’s not done well, is hollow.”

The mechanics can all be in place - recommendation engine, email flow, retargeting - and the output still fails if it feels generic. Relevance erodes the moment a brand substitutes volume for precision. She uses B2B as her primary example - four emails a week about mission-critical software - but the B2C equivalent is equally familiar: irrelevant product suggestions that ignore purchase history, or retargeting campaigns that chase users with products they already bought.

Salesforce’s State of the Connected Customer research found that 73% of customers expect companies to understand their unique needs and expectations - yet a majority of those same customers report feeling treated as a number rather than as a person. The gap Jennifer describes lives exactly in that space: the infrastructure exists, but the discipline to deploy it with precision is still rare.

The thread connecting discovery and retention

UGC-driven discoverability and quality personalization share a common root: both depend on treating real behavioral signals as the primary input.

UGC works because it reflects genuine customer experience. Personalization works when it acts on genuine behavioral data. Both break down when brands substitute manufactured volume for authentic signal - whether that is incentivized fake reviews or over-broad email segments. Jennifer’s framing in this chapter essentially argues that the best investment a retailer can make in their AI search rankings is the same investment they should make in customer lifetime value: deliver experiences that are genuinely worth talking about, then give customers a reason and a mechanism to talk about them.

For the full conversation - covering traffic resilience, brand trust, the role of community across B2B and B2C, and where generative AI fits into authentic content strategy - watch the full Conversations episode with Jennifer Montague.