Don’t “fix the whole site,” win a few high-stakes decision moments and scale the pattern.
When teams realize AI-shaped search is changing the game, the first impulse is to go big:
- full site audit
- massive content rewrite
- taxonomy overhaul
- CMS migration
- “AI-ready” replatforming project
That’s how you end up with a 9-month initiative, zero learning for six months, and a leadership team that loses patience before you can prove anything.
Mental Model #9:
The right starting move is almost never “fix the whole website.” It’s “win a small set of high-stakes decision moments, then scale what works.”
GEO is a moving environment. You need a strategy that creates evidence quickly, builds internal confidence, and avoids boiling the ocean.
Why “fix everything” fails in GEO
1) Time-to-learning is too slow
If you don’t learn within 30–60 days, you’re guessing. GEO rewards iteration.
2) You can’t measure what you didn’t isolate
When you change hundreds of pages, you can’t connect cause to effect. Your baseline becomes noise.
3) Most content isn’t decision-driving
A large portion of a site is informational, legacy, or low impact. GEO improvements should start where decisions are actually made.
4) Governance will collapse under scope
If you can’t operationalize change for a small set of entities and questions (Mental Model #6), you won’t sustain it across the entire site.
Define “decision moments” (the unit of GEO strategy)
A decision moment is when a person (or an AI system acting for them) is close enough to action that inclusion and framing matter.
Examples:
- vendor shortlist creation
- “best option for me” selection
- program selection and application confidence
- donation or membership justification
- product selection and configuration
- compliance and standards validation
- choosing an event or experience
Decision moments are where AI summaries and comparisons do the most work.
This is where you start.
The 3×3 starting framework
Pick a small set that is big enough to matter and small enough to manage.
Choose 3 decision moments
For example:
- Compare and choose (alternatives, shortlists)
- Validate and de-risk (standards, credibility, constraints)
- Plan and execute (steps, requirements, timelines)
Or tailor by vertical:
- higher ed: program fit, outcomes validation, application requirements
- industrial: standards compliance, compatibility, supplier comparison
- mission-driven: impact validation, giving confidence, event attendance planning
Choose 3 priority entities per moment
Typically:
- the brand
- one offering/program/product
- one location, segment, or use case
Now you have a defined surface area. You can govern it.
What you build for each decision moment
This is where Mental Models #2–#8 come together.
For each decision moment, you build a small “GEO kit”:
1) An intent bundle map (questions, not keywords)
List 5–10 real questions people ask AI at that moment.
2) A decision page (or upgraded existing page)
A page designed for retrieval and reuse:
- clear definitions
- criteria and tradeoffs
- who it’s for / not for
- constraints and requirements
- proof attached to claims
- next steps
3) Supporting proof assets
One to three assets that can be cited:
- outcomes, case studies, accreditation, standards, methodology
- ideally in clean HTML (not only PDFs), with dates and context
4) Entity alignment
Make sure the entity definitions are consistent across:
- the page
- your “about” and offering pages
- structured data
- key third-party profiles
This is the work that prevents misrepresentation and omission.
The success criteria: fast evidence, not perfect coverage
Your goal in the first cycle is not dominance. It’s measurable improvement in:
- presence in AI answers for your selected questions
- role (move from absent → mentioned → shortlisted → recommended)
- framing accuracy (reduce wrong or generic summaries)
- citation quality (get your best assets cited, not random pages)
This is Mental Model #5 applied with intention.
Once you can show movement in those signals, you can justify expansion.
A 30–60 day starting plan that teams can actually execute
Here’s a realistic plan that avoids “big bang” failure.
Week 1: Choose and baseline
- pick 3 decision moments
- select 15–30 questions total
- run baseline scoring (presence, role, framing, citation)
- identify which competitor sources show up most often
Weeks 2–4: Build the first GEO kit
- upgrade or create 1–2 decision pages
- tighten entity definitions and proof coupling
- align the top external surfaces (partners, listings, directories where relevant)
Weeks 5–6: Re-test and isolate impact
- rerun the same questions
- identify which changes moved inclusion and framing
- log what worked and what didn’t
Week 7+: Scale the pattern
- replicate the structure for the next decision moment
- expand question sets and entities gradually
- operationalize with a monthly cadence (Mental Model #6)
This creates a learning loop leadership can support because results show up in weeks, not quarters.
The “small bet” advantage
Starting with decision moments has three benefits that compound:
- You learn faster
- GEO is uncertain by nature. Fast iteration beats big plans.
- You build internal alignment
- It’s easier to get product, legal, and data teams to support a focused effort tied to a real decision moment than to support “fix the website.”
- You create reusable patterns
- Once you build one strong decision page + proof module + entity alignment pattern, you can reuse it across offerings, locations, and verticals.
That’s how capability emerges.
The GEO implication
GEO strategy is not “optimize everything.”
It’s:
- pick the decision moments that matter
- make your entities legible and provable there
- build retrieval-grade content for the intent bundles
- measure presence, role, framing, and citation
- scale only after you have evidence
Win a few moments, then expand. That’s how you get compounding visibility without boiling the ocean.
Next installment: Mental Model #10, the “citation flywheel,” and why the best GEO outcomes come from making your most credible assets the easiest to cite, not from chasing rankings.
Photo: Little Jumps, Suffolk County
