AI Search 2026 Checklist: The Weighted Grid to Optimize Every Piece of Content
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Why Aleyda Solis’s Checklist Isn’t Enough Without Prioritization
A client calls me on a Tuesday morning. He downloaded Aleyda Solis’s checklist, published on September 20, 2026. 12 steps. 3 layers. 5 presence indicators. He wants to check every box.
Except he has 1 200 product pages, one internal person, and two hours a day.
The checklist is excellent. It sequences actions. It leaves weighting out. It tells you what to do, not in what order.
I look at 15 sites a week. Half stall at step 3, the gap diagnostic. The other half rush to step 8, third-party sources. Nobody calculates the impact-to-effort ratio.
Aleyda Solis writes it herself: her checklist rests on a 3-layer framework. Presence, readiness, business impact. Each step passes a hypothesis to the next. It jams when you want to launch everything in parallel.
We exhaust the team. We check boxes. Citations stay flat.
At a pet food client, the team spent 4 months on step 5: building decision-support content. No presence measurement beforehand. Result: 47 pages produced, 3 AI citations observed.
A professional tools client showed me their document. 47 actions checked. No priority. The team felt progress. AI citations stagnated. The problem wasn’t content. It was the decision architecture.
Aleyda Solis’s checklist is not a work plan. It’s a diagnostic protocol. It answers one question: what hypothesis explains the observed pattern? To turn it into a work plan, you need a score.
The prioritization grid fixes that. Each action gets a score from 0 to 10 on three dimensions: impact, effort, confidence. The total sets the execution order.
That’s why I weight. A score creates a ranking. A ranking creates an execution queue. An execution queue creates measurable citations.
A checklist you endure, or a system that runs. How many pages do you need to fix first?
La grille tient en une seule formule : impact × confiance − effort. Voici comment trois notes de 1 à 10 deviennent un score de 69, qui propulse les données produit en tête des priorités.
Comment se construit un score de priorisation
Décomposition du score de l’action « données produit lisibles »
Building the Grid: Impact, Effort, Confidence
My grid fits in three columns. Estimated impact. Required effort. Confidence in the data.
Each action from Aleyda Solis’s checklist gets a score from 1 to 10 on these three axes. Then a score: impact × confidence − effort. That’s what sets the order of execution.
One concrete case from a pet food client. « Make product data machine-readable »: impact 9, confidence 8, effort 3. Score: 9 × 8 − 3 = 69. « Work the third-party source ecosystem »: impact 7, confidence 5, effort 8. Score: 35.
The conclusion is simple. Start with product feeds. Not with press relations.
This weighting forces choices. It eliminates 40-item lists where 38 stay unchecked.
Aleyda Solis’s checklist already provides diagnostics. It ranks by relative importance, not by speed of gain. My grid adds effort and confidence. Why? Because a high-impact action with low confidence often waits too long.
This table fills in 30 minutes in a simple Google Sheet. The impact, effort, confidence columns already exist in Aleyda Solis’s work. You just need to quantify them.
A real example, from an audit for a 1 740-page baby care site:
| Action from the checklist | Impact /10 | Confidence /10 | Effort /10 | Score |
|---|---|---|---|---|
| Machine-readable product data | 9 | 8 | 3 | 69 |
| Entity consolidation | 8 | 7 | 4 | 52 |
| Third-party source ecosystem | 7 | 5 | 8 | 35 |
The score cuts through. The team knew what to do. They needed the first move.
What happens when you know that action 4 drives more citations than action 1? You stop following the document’s order. You follow the score’s order.
The 4 E-commerce Actions to Tackle First
After 90 AI Search audits on e-commerce sites, I keep four actions that always rise to the top of the score.
First: make product data machine-readable. Feeds, schema markup, commercial attributes. That’s step 9 of Aleyda Solis’s checklist. On a 900-SKU catalog, the effort stays moderate. The gain is immediate: AI reads prices, stock, ratings.
Second: consolidate entities. Brand name, product naming, positioning. Step 7 of the checklist. If your manufacturer sheet says « salmon croquettes », your page says « salmon-flavored croquettes », and your feed says « salmon kibble », you fragment the entity. Decide.
Third: build decision pages. Comparison tables, buying guides, usage charts. These are the contents AI cites after answering. Step 5 of the checklist. On a baby care site, we produced 17 compatibility tables. Three weeks later, the brand appeared in 7 out of 12 AI answers for commercial queries.
Score for the tables: 74. Score for the buying guides: 61. The gap was enough to shift the team’s effort.
Fourth: isolate the pages that deserve a click. Aleyda Solis recommends it: measure citation and click separately. A page can be cited without generating a visit. Then rework the title, the promise, or the snippet.
This ranking isn’t universal. It depends on the catalog, the market, and the state of your data. The grid is for that: prioritizing your case.
How many actions on your current list score above 55?
Traps to Avoid: Copying, Forcing, Polluting
Aleyda Solis says one simple thing: don’t exploit the weaknesses of AI systems. Prompt injection, fake reviews, fabricated community signals, citation gaming. These tactics pay short term. They kill trust.
I see it in rushed e-commerce operators. They want to appear in AI answers at any cost. They fabricate reviews. They create profiles. The next day, rankings shift. The brand loses authority.
Second trap: copying a competitor’s page structure. Aleyda Solis’s checklist says it at step 5: don’t copy the page mix of another category. A furniture site shouldn’t be built like an eyewear site.
Third trap: believing citation is enough. A citation isn’t a click, and it isn’t a sale either. It strengthens presence. It doesn’t convert. You need two paths: the proof page and the destination page.
Fourth trap: treating every language in a market with the same grid. Aleyda Solis insists: localize by market, not just by language. A site in German-speaking Switzerland shouldn’t be weighted like a site in Germany.
A dietary supplement site in Dutch-speaking Belgium thought it could duplicate its French grid. Search volumes differed. Third-party sources differed too. Result: 6 weeks lost. The lesson: localize the scoring before localizing the content.
The prioritization grid bakes in these nuances. It forces you to document why you skip an action, not just why you do it.
Complete clarity on what you skip delivers more than a stack of checked boxes.
Why Checking a Box Triggers Dopamine (and How the DOSE Framework Explains It)
The DOSE framework, taught by Guillaume Attias at BMO Academy, breaks motivation into four levers: dopamine, oxytocin, serotonin, endorphins. A prioritized checklist activates two.
Endorphins come from closure. Checking a box closes a loop. The grid slices Aleyda Solis’s 12 steps into 25-minute sub-actions. Each checked box, a micro-satisfaction.
Dopamine comes from the score. When impact × confidence − effort jumps from 41 to 67 after correction, the brain registers measurable progress. That number is an immediate reward.
In my workshops, I ask teams to color boxes green when the score passes 55. Yellow between 25 and 55. Gray below. Watching green spread across the spreadsheet triggers serotonin: the pride of belonging to a project that’s moving.
Oxytocin arises when the grid is shared. One owner per line. A common review. The bond forms around an accepted order of priority, not around competing intuitions.
DOSE doesn’t replace the method. It accelerates it. A non-prioritized checklist is anxiety: you don’t know where to start. A scored grid is momentum.
Aleyda Solis built the skeleton. BMO Academy taught me to make it desirable. It’s the mix of both.
What happens if your score climbs but citations lag? That’s the signal to revisit the confidence you placed in the action, not to quit.
The Validation Loop: From Score to Measured Signal
An AI citation needs more than one number. Aleyda Solis recommends 5 presence KPIs: appearing, being recommended, being cited, winning comparisons, being described correctly.
I group them into two families. Presence: appearing and being described correctly. Recommendation: being cited, recommended, compared.
For an e-commerce site, the most reliable measure remains the percentage of target questions where the brand appears. Set a baseline before any correction. Observed example: 3 out of 12 AI answers for transactional queries. Six weeks after the priority actions: 7 out of 12.
That 4-point progression doesn’t tell the whole story. I cross it with generated clicks and assisted revenue. It gives a clear signal: the grid picked the right actions.
For fine measurement, I use a 0-to-3 scale per target query. 0: brand absent. 1: brand mentioned. 2: brand cited with link. 3: brand recommended or compared. This scale takes 20 minutes across 18 to 25 queries.
The validation loop runs on 14 days. Every two weeks, record the 5 KPIs, score each action, adjust if needed. That’s step 12 of Aleyda Solis’s checklist: a recurring validation and optimization loop.
The minimum viable setup: a Google Sheet with three tabs. Presence. Readiness. Impact. One owner per tab. A 30-minute review every week.
Reporting must not overstate. Aleyda Solis reminds us: correlation is not causation. We go layer by layer, with confidence levels.
Is your next action a presence action or a recommendation action?
Tout tient en cinq gestes simples. Noter, calculer, découper, mesurer, boucler : une fois lancé, le système tourne seul et les citations grimpent.
Le rituel du lundi matin en 5 étapes
De la checklist notée à une boucle de validation de 14 jours
What You Do Tomorrow Morning (and the Question That Changes Everything)
Open Aleyda Solis’s checklist. Take the 12 steps. Create three columns: impact, effort, confidence. Score each step from 1 to 10.
Second: calculate the score impact × confidence − effort. Sort from highest to lowest.
Third: slice the top three actions into 25-minute subtasks. One subtask, one checkbox.
Fourth: measure your AI presence before correcting. Count how many target questions cite your brand. That’s your starting point.
Fifth: launch the 14-day loop. Check, score, measure, adjust.
The system fits in one sentence: a good checklist becomes a sequence of decisions to execute.
I don’t sell the method. I show the pages.
How many AI citations did you miss last month because you lacked a priority order?
Prioritized AI Search Audit in 90 Minutes
I audit your pages, I weight your actions, and I hand you an actionable grid. Independent and direct, with full clarity on next steps.
Book a strategic call — 45 minFrequently Asked Questions
Should you apply all 12 steps of Aleyda Solis’s checklist?
No, never in parallel. Prioritize with a score per action: impact × confidence − effort. Start with the top three scores.
How do you weight impact and effort?
Score from 1 to 10. Impact: the number of AI citations the action can generate. Effort: the hours of work it requires. Confidence: the level of proof you have.
Which e-commerce content should you fix first?
Machine-readable product data and brand entities. Then decision pages, including comparison pages. Finally, destination pages.
How do you measure whether an AI citation is progressing?
I track 5 KPIs. One: the brand appears. Two: it gets recommended. Three: it gets cited. Four: I win comparisons. Five: it gets described correctly. Checked every 14 days.
How often should you update the prioritization grid?
At the end of each validation loop, every 14 days. If a score stays flat after actions, revisit the confidence or effort you assigned.
