The short answer: GEO runs on two timelines. Tactics aimed at live retrieval — citable pages, corrected third-party sources, structured data — can shift AI answers in days to weeks, because engines re-crawl and re-ground continuously. Tactics aimed at the training-data footprint — sustained coverage, reviews, authority building — compound over one or more model release cycles, typically quarters. Plan for both, and measure weekly so you can attribute movement to what you shipped.
The fast lane: days to weeks
Most consumer engines ground commercial answers in live web search (retrieval-augmented generation). That makes the answer only as old as its sources, and it's why the fastest GEO wins look like publishing rather than campaigning:
- A direct, citable page answering a question your buyers ask can be crawled, indexed, and read into answers within days of ranking — the craft is covered in content strategy for AI search.
- An updated third-party page that engines already cite propagates on the next re-crawl. Getting added to one heavily-cited comparison post frequently beats months of on-site work.
- Structured data and entity cleanup remove ambiguity immediately for every subsequent retrieval.
- Unblocking AI crawlers in
robots.txt, if you were blocking them, is the rare same-day fix.
Realistic expectation: measurable movement on retrieval-heavy prompts within two to six weeks of shipping, arriving unevenly — Perplexity and other retrieval-first engines respond first, memory-heavy engines lag.
The slow lane: quarters
What a model says from memory reflects its training data, frozen at a knowledge cutoff. No amount of publishing today changes what the current model has already memorized. Footprint work — digital PR, review volume, consistent entity facts, presence in reference sources — pays out when providers train the next model version on a fresher web snapshot. Those releases arrive on cycles of several months to a year, and each one re-scores your accumulated record.
This lane rewards starting early and never pausing: the coverage you earn this quarter is the memory of every model trained after it.
Why measurement cadence decides attribution
AI answers are stochastic — the same prompt can name you Monday and skip you Wednesday. Spot-checks therefore can't distinguish a real gain from noise, and monthly checks can't tell you which of four shipped initiatives caused a jump. The working setup is a fixed prompt panel swept on a schedule across engines, giving you trended mention rates with dated snapshots on either side of every launch. That's the loop sweeps automate, and it's what turns "the score went up" into "the comparison-page placement worked; the press push hasn't landed yet."
A realistic composite timeline
- Week 0: baseline every engine — a free audit does this in one pass — and fix crawler access.
- Weeks 1–6: ship citable content and pursue placements in cited sources; expect first movement on retrieval-heavy prompts.
- Months 2–6: compound coverage and entity work; watch the competitor gap trend rather than single-week readings.
- Next model releases: memory-based mentions catch up to the footprint you built.
The tactics themselves, ordered by speed and leverage, are laid out in how to improve AI visibility — and the two-clock mechanics behind these timelines in how often AI models update their knowledge.