There is no reliable universal deadline for GEO results. You can agree when an agency will publish improvements, but that does not set a date when ChatGPT, Gemini or Google will recommend your business. A first citation, repeated recommendations and a qualified enquiry are also different outcomes.
For planning, consider an initial 90-day review schedule with checkpoints at days 30, 60 and 90. This is a suggested management schedule, not a Dubai performance benchmark, minimum contract or promise of results within three months. Adjust it to the work involved and your decision deadline. Require evidence at each checkpoint rather than simply waiting for time to pass.
What can happen first, and what takes longer to establish?
The first thing you should be able to verify is whether the agreed work happened: an access problem fixed, an inaccurate service page corrected or useful content published. These are delivery milestones. They matter, but they are not yet evidence that AI recommendations have improved.
Platform processing is a separate dependency. Google says crawling can take a few days to a few weeks and warns that requesting a crawl does not guarantee inclusion in search results. That is Google crawling guidance, not a timetable for AI recommendations or enquiries. Repeated requests for the same URL do not make it crawl faster. Google's recrawl documentation explains this boundary.
A recommendation can appear in an early check, but one answer cannot establish a lasting improvement. Repeated comparable observations are needed before describing a pattern. Even then, a change after publication does not by itself prove the publication caused it.
Business results need their own assessment. A relevant recommendation may lead to a visit, a later direct enquiry or no action. If a prospect requests a site visit and then compares proposals, the commercial decision follows a different timetable from a same-day booking. Use your own sales process and records rather than an assumed GEO payback date.
Keep delivery dates separate from observation time
Record the engagement start date, the dates material changes went live and the dates of subsequent checks. Each answers a different question:
- Engagement start: How long have we been paying for and expecting delivery?
- Publication or deployment: How long has this particular change been available to be discovered?
- Observed processing or appearance: What has a platform actually shown us, and when?
Do not restart the entire accountability clock whenever a page changes. A late publication may explain why there is little post-change evidence, while still being a delivery problem that needs addressing.
Consider a hypothetical Dubai commercial-cleaning company targeting office managers in Business Bay and JLT. It wants accurate English and Arabic information about its actual service coverage and out-of-hours availability. The dates below are invented to illustrate scheduling, not a case study or expected platform behaviour.
| Milestone | Illustrative date | What it means at the day-45 review |
|---|---|---|
| Engagement begins | Day 0 | The delivery relationship has run for 45 days |
| Approved English page goes live | Day 10 | That version has been live for 35 elapsed days |
| Approved Arabic page goes live | Day 35 | That version has been live for 10 elapsed days |
| First formal comparison | Day 45 | The two versions have had different amounts of time online |
Neither publication interval tells us when a crawler processed the page or whether it was selected for an answer. It would be misleading to claim that both versions had received a full 45-day test. Equally, the later Arabic approval should be visible in the delivery report, with a responsible person and an explanation.
The local planning issue is concrete: service-area facts and language versions need usable approvals. A website should not claim coverage or operating hours the company cannot provide merely to finish a content calendar sooner.
A practical 30-, 60- and 90-day review schedule
Use these as proposed review appointments. They are not predicted stages of AI adoption, and they do not require you to wait until a checkpoint to investigate a known problem.
Day 30: check what is live and what is blocked
Review the starting observations, the agreed priority questions and the work actually published. Ask which changes remain in draft, which need client approval and which require a developer or another supplier.
If the agency has delivered reports but none of the agreed improvements is live, the issue is delivery. More time alone will not make unpublished work visible. Reset responsibilities and dates before expanding the programme.
A larger implementation may legitimately need longer. The important distinction is between a documented dependency and a vague explanation that AI needs time.
Day 60: check the evidence after deployment
Compare like-for-like observations where possible: the same buyer questions, language and product conditions, with account context and collection failures recorded. Look at whether the business is described accurately, not just whether its name appears.
If a page is inaccessible, investigate access. If it is available but the business is not being selected in the sampled answers, review whether the public evidence addresses the actual buying need. These are different explanations requiring different work.
Keep questions that produce no recommendation in the record. Quietly replacing them with easier branded questions would make progress look stronger without answering the original business problem.
Day 90: decide what deserves another period of work
Review delivery, observed visibility and commercial evidence separately. Set a specific next objective and review date if you continue. That might be correcting an inaccurate service description, testing a narrower buyer need or evaluating whether relevant visits become qualified enquiries.
If there is no supported explanation for the lack of progress and no credible next test, consider changing the scope or ending the work. Money already spent does not justify indefinite renewal. Conversely, a single empty answer should not automatically cancel a programme with sound delivery and a clear, testable next step.
When should you wait, investigate or change the approach?
| Current evidence | Appropriate next decision |
|---|---|
| Agreed changes are not live | Resolve delivery, approvals or scope; do not call it a platform delay |
| Changes are live but access or eligibility is unresolved | Investigate the specific technical issue and verify the fix |
| Content is accessible, but sampled answers still miss the business | Reassess buyer fit, factual support and competing evidence; agree a bounded next test |
| Recommendations appear but describe the wrong offer or location | Prioritise accuracy; a misleading mention is not a success |
| Relevant appearances increase but qualified enquiries do not | Review demand, enquiry routes, sales follow-up and attribution before simply producing more content |
| Progress remains unclear and there is no useful next hypothesis | Reconsider scope, provider or continuation rather than relying on elapsed time |
These are management decisions, not proof of a platform's hidden selection process. Assistants can cite external sources when discussing a business, so the absence of a citation to your own page does not establish that the brand is invisible.
Do you need to wait for AI model retraining?
Do not accept “we must wait for the next model update” as a universal explanation for a delayed searched answer. OpenAI distinguishes OAI-SearchBot, which supports search, from GPTBot, which concerns potential model training. Their controls are independent. Current web discovery and model training are different mechanisms. OpenAI's crawler documentation describes those roles.
This distinction does not provide a fixed ChatGPT refresh schedule or guarantee that a newly accessible page will be cited. Likewise, asking an assistant to open a supplied URL is a different test from seeing whether it selects your business for an unbranded customer question.
For Google's generative AI features, a page must be indexed, eligible for a search snippet and included under the relevant Search generative AI setting. Meeting the requirements still does not guarantee that Google will serve it. Google's AI optimisation guide makes that limit explicit. There is no basis for turning technical eligibility into a promised recommendation date.
What can make your plan shorter or longer?
An existing useful page that needs a factual correction is a different project from a new website with no clear service information. The latter has more work to complete before you can fairly assess the published improvements. That does not establish a fixed speed advantage or a guaranteed citation window for either business.
Access to the website, approval of claims, language review and coordination with developers can all change the delivery schedule. External profiles and publisher pages may require someone else's action. Record those dependencies instead of putting every delay under the heading of GEO.
Platform, competitor and answer changes can also complicate comparisons. Treat the evaluation as an ongoing observation process, while keeping the scope of paid work and each next decision clear.
If you need confirmed demand for a launch next month, a GEO forecast should not be the only basis for that plan. Keep the launch acquisition plan separate from an experiment whose recommendation timing you cannot control.
What should an agency commit to?
Ask for concrete commitments to work and reporting: which facts or pages will change, who must approve them, when they should be live, what will be observed and what would justify continuing. A three- or six-month contract is a commercial term, not evidence that a platform needs that exact period to respond.
Lunasol's GEO service combines relevant-question and competitor work with improvements to content and online presence. That connection is useful when you need to relate actual published changes to later observations. The scope and dependencies should be agreed for your business, and the service does not guarantee recommendations or a fixed results timeline.
Start by defining the first changes that can go live and the evidence you will review afterwards. That gives you a timetable you can manage without confusing an agency deadline with an AI platform promise.