What changed in 2026

Zero-click search isn't a new phrase, but 2026 is the year the numbers stopped being marginal. Recent measurement puts the overall share of Google searches ending without a click at roughly 68% in early 2026, up from a 9.5-point decline in click-generating searches between 2024 and 2026 (Search Engine Land). Inside Google's AI Mode specifically — which Google confirmed at I/O 2026 has surpassed 1 billion monthly users, with query volume more than doubling each

quarter — the zero-click rate on measured search tasks approaches 93%, and separate analysis of 250 search tasks found a near-100% zero-click rate for research-and-comparison queries inside AI Mode itself (Pasquale Pillitteri).

AI Overviews — the summary boxes that appear above traditional results — now show up on more than 20% of all Google searches, and when they do, click-through rates drop by nearly 60%; 83% of searches that trigger an AI Overview end with no click at all (Search Engine Land; Triangle Direct Media).

The practical translation for ecommerce: a shopper researching a product — comparing features, reading reviews, checking prices — increasingly does that entire comparison shopping process inside Google's AI interface and only clicks through to a retailer's site once they're ready to buy (Capconvert). The top-of-funnel discovery and consideration stages, which used to generate a large share of a retailer's organic traffic, are being absorbed into the AI layer itself.

Why this hits Montreal ecommerce brands specifically

Montreal's ecommerce sector has spent the past several years building a real strength: a dense cluster of AI and data science talent supporting growth marketing agencies that combine performance media with agentic AI and advanced data engineering for acquisition (GoodFirms). Local agencies report actively working to strengthen site structure, content, and product data specifically so businesses "show up where customers are searching — on Google search, Maps, AI search, social media, and beyond," including surfacing in ChatGPT, Perplexity, and AI Overviews, not just traditional Google rankings (Semrush Agency Directory — Montreal).

That local expertise is an asset, but it also signals how competitive AI visibility is becoming in this market: if a growing share of Montreal ecommerce operators are already investing in AI search optimization, a brand that hasn't structured its product data for AI discovery is losing shelf space it doesn't even know it's losing. Unlike a traditional SEO ranking drop, a zero-click AI answer gives no warning — the traffic simply stops arriving, because the AI interface answered the shopper's question using someone else's product data instead of yours.

The executive questions this raises

For a CEO or founder: If 60-93% of research-stage traffic no longer reaches your site, is your revenue exposure concentrated in the wrong part of the funnel? The businesses managing this well are shifting measurement and investment toward being cited inside the AI answer — appearing in AI Overviews and AI Mode responses as the recommended product — rather than relying solely on post-click conversion optimization.

For a marketing head: Customer acquisition cost math changes when top-of-funnel clicks shrink. If fewer shoppers land on-site during research, paid acquisition increasingly has to work harder to capture bottom-of-funnel, ready-to-buy intent — which raises CAC unless organic AI

visibility is doing more of the early-funnel work for free. Is traditional keyword SEO still sufficient on its own? The data increasingly says no.

For a director of ecommerce operations: What should be prioritized — content volume or content structure? AI systems synthesize answers from structured, machine-readable product data: clean specifications, accurate structured data markup (schema.org/Product, pricing, availability, reviews), and content written to directly answer comparison questions. A product page built for human scrolling with unstructured copy is far less likely to be the source an AI system cites.

For a CTO or technical lead: This is fundamentally a website engineering problem before it's a content problem. Product feeds, structured data, page speed, and crawlability determine whether an AI system can even parse a Montreal retailer's catalogue accurately enough to recommend it. Sites with thin or inconsistent structured data are effectively excluded from AI-driven product discovery regardless of how good the products are.

Where the opportunity is

The businesses better positioned heading into the rest of 2026 are treating AI search visibility as infrastructure, not a marketing add-on: first-party product data that's clean and consistent, structured markup that AI systems can parse reliably, and site architecture engineered specifically to be legible to AI crawlers and answer engines — not just to human shoppers and traditional search bots. That is a different skill set than conventional SEO, and it's why "engineering the website for AI" is now a distinct line item from "optimizing the website for search."

This is the exact gap ZTS India's AI website engineering services for Montreal are designed to close — rebuilding product and site data so ecommerce brands are legible, citable, and recommendable inside the AI interfaces where an increasing share of Montreal shoppers now do their research before they ever reach a retailer's site.