Why do AI answers about implementation matter if buyers eventually talk to sales anyway?

Because by the time they talk to sales, the buyer has usually already formed a belief about how hard your product is to adopt, using an AI chatbot as part of that research. G2's 2026 research found 69% of buyers ended up choosing a different vendor than originally planned based on AI chatbot guidance, and implementation effort is a core part of the vendor comparison buyers ask chatbots to help with. A sales team correcting a wrong assumption in a first call is playing catch-up against a belief the buyer already trusts.

What's the single highest-leverage piece of content for implementation-stage GEO?

A public, unauthenticated "Getting Started" or "Implementation Overview" page that states a specific, segmented time-to-value claim in the first two sentences. It's the page most likely to be crawled, the easiest for a model to extract a clean answer from, and the one most companies either gate behind a login or bury under marketing language instead of concrete numbers.

Should we publish a specific onboarding timeline even if it varies by customer?

Yes, published as a range broken into named phases rather than a single average. A phased range like "technical setup in 1 to 2 weeks, full team rollout by week 4 to 6" is both more accurate to how implementation actually happens and more useful to an AI engine than a vague claim like "fast and easy," which gives a model nothing concrete to cite.

How does Reddit end up shaping what AI says about our onboarding?

Reddit is consistently tracked as the most-cited domain across major AI engines, including ChatGPT, Gemini, and Perplexity, and its share of citations can shift sharply within weeks as these platforms change how they retrieve and rank sources. Implementation complaints on Reddit read as unfiltered and independent, so they carry weight even when they're outdated, which is why monitoring relevant threads and correcting stale claims matters as much as publishing your own docs.

Do FAQPage and HowTo schema still help even though the rich results are gone?

Yes. Google removed the visual FAQ rich result from search on May 7, 2026, and HowTo lost its rich result back in 2023, but both schema types remain valid schema.org markup. They still help AI engines parse structured facts, steps, and question-answer pairs out of your implementation and onboarding pages, even without a corresponding SERP snippet.

How is this different from optimizing a pricing page for AI research?

Pricing content answers "what will this cost," while implementation content answers "what will this actually take from my team," and buyers ask both questions to different depths at different points in evaluation. A pricing page built for AI-assisted buyer research and a strong implementation overview page work together, but neither substitutes for the other in what an AI engine can cite.

How long does it take to see AI engines cite our new implementation content?

Plan on four to six weeks at minimum before drawing conclusions, longer than a straightforward pricing or feature update, because models tend to corroborate specific timeline and setup claims against independent sources like reviews before treating them as reliable. Track a fixed panel of implementation and onboarding prompts across engines over that window rather than checking once and assuming nothing changed.

What should a migration guide include to actually earn citations?

Name the competitor directly, describe the real data export and import process, flag anything that doesn't carry over cleanly, and give a specific timeline for running both systems in parallel before cutover. Most vendors skip this entirely, which is exactly why a detailed, honest migration guide tends to become the source an AI engine reaches for when someone asks what switching actually involves.

How to Optimize for AI Answers About Implementation and Onboarding