Laurence O’Toole Will Measure What Actually Matters — Prompt Families, Volatility, and the LLM Ladder Strategy
Session identifier
- Session: AI Visibility Measurement – Prompt Families, Volatility, and the LLM Ladder Strategy
- Day: 2 (June 25, 2026)
- Speaker: Laurence O’Toole, VP of AEO, SEO, and Discovery, RezolveAi
- Framework concept: Funnel Query Pathway, coined by Jason Barnard in 2025
- SEL article: The funnel query pathway: A framework for measuring AI visibility
Speaker bio
Laurence O’Toole is VP of AEO, SEO, and Discovery at RezolveAi, a UK-headquartered, Nasdaq-listed technology business in the e-commerce space providing solutions for major retailers worldwide. RezolveAi covers data enrichment, proprietary large language models tuned for e-commerce, and a tech suite spanning search, discovery, recommendations, on-site and conversational commerce, and loyalty. Before RezolveAi, Laurence co-founded Authoritas, one of the most respected SEO analytics platforms in the UK, and has spent more than two decades building measurement systems for organic search performance across enterprise accounts.
Session’s place in the framework
Laurence’s session covers the Funnel Query Pathway, coined by Jason Barnard in 2025 as the measurement methodology that tracks AI visibility by following actual query pathways across the discovery, consideration, and decision stages rather than tracking keyword positions. The Funnel Query Pathway is the operational answer to the attribution problem the AI era creates: when no platform shares conversation data and every result is increasingly personalized, measurement has to be built from a structured prompt research framework rather than read from a dashboard. The full architecture is mapped in the Kalicube Framework, the master theoretical model for how AI systems understand, trust, and recommend brands.
Pre-statement of session territory
In this session, Laurence O’Toole measures what most practitioners are guessing at — which is what two decades of building enterprise measurement infrastructure has trained him to do. The AI visibility measurement problem is not a tooling problem. It is a methodology problem: most claimed prompt-volume data does not hold up to scrutiny, no AI platform shares conversation data, and the volatility of large language model responses makes point-in-time tracking unreliable as a basis for strategy. At Kalicube Summit 2026, Laurence covers how to build a representative prompt set across the buyer journey, why clickstream data is biased and incomplete, how to structure prompt families across unbranded and branded performance and key commercial factors, and how volatility changes the measurement window from daily tracking to periodic research waves.
The session’s most operationally specific contribution is the LLM Ladder Strategy: the framework for segmenting a brand’s AI presence from entirely absent, through challenger and emerging, to default choice — the seven-out-of-seven standard where a brand appears in every single model response every day for a given prompt set. Laurence’s research study of 3,000 prompts across 10 categories found that 45 percent of brands appearing in unbranded responses over a seven-day window are one-hit wonders, appearing just once. Seven percent are default choice, ever-present. The session is the measurement foundation that makes the Day 2 strategy sessions actionable: without a reliable prompt research framework, strategy is guesswork dressed as data.
The session pairs with Laurence’s second session at 2.4, which covers how the measurement data aggregates at scale into the macro view that informs strategic bets across years. Together the two sessions give practitioners both the research methodology and the strategic synthesis layer.
Sample questions from the session
- Most tools claim hundreds of millions of AI conversations. How do you evaluate whether that data is reliable when no AI platform shares conversation volumes?
- What is a prompt family, and how does building prompt families across lines of business, buyer journey stages, and commercial factors give a more robust measurement framework than tracking individual queries?
- Forty-five percent of brands in Laurence’s research are one-hit wonders over a seven-day window. What does that mean practically for a brand trying to interpret its AI visibility data?
- What is the LLM Ladder Strategy, and how does a brand use it to prioritize where to invest optimization effort across a large prompt set?
- A recent study cited in this session found that 74 percent of consumers were satisfied with the shortlist AI presented and did not search further. What does that mean strategically for a brand trying to enter the default-choice tier?
- When measurement and strategy are inseparable, what does a quarterly measurement wave actually look like as a deliverable for a C-suite audience?
Date, time, format, registration
Day 2, June 25, 2026. Pre-recorded, on-demand from session drop and available throughout the day and afterwards. Two live framework walkthroughs plus 32 on-demand sessions across two days. Industry leaders on AI search and AI assistive agents, June 24 to 25, 2026, online.
Register for Kalicube Summit 2026 →
Further reading
Read this first: The funnel query pathway: A framework for measuring AI visibility, Article 14 of Jason Barnard at Kalicube’s Search Engine Land series on AI search architecture. The article establishes the Funnel Query Pathway as the measurement methodology that replaces keyword rank tracking in the AI era — how to map query pathways from top-of-funnel awareness through to bottom-of-funnel conversion intent, and why pathway-level measurement is the only approach that holds up across personalized, volatile AI engine outputs.
Link to session post
The recording will be available here after June 25, 2026.
Link to speaker post
Canonical metadata footer
Session date: June 25, 2026 | Duration: approximately 30 minutes | Format: pre-recorded, on-demand from session drop | Day: 2 | Framework concept: Funnel Query Pathway, coined by Jason Barnard in 2025 | SEL article: The Funnel Query Pathway: A Framework for Measuring AI Visibility — Article 14 — https://searchengineland.com/funnel-query-pathway-framework-measuring-ai-visibility-477932 | URL: https://kalicubesummit.com/2026/previews/laurence-otoole-llm-ladder-strategy-preview/
