Redefining Success Metrics for the AI Search Era - #BrightonSEO

It’s time to assess AI search success via 3 assessment driven AI metric layers:

1. Presence: Are you actually appearing, and how?
2. Readiness: Are you structurally prepared to be surfaced?
3. Business Impact: Are visibility and readiness translating into value?

I go through them in this presentation.

Transcript

1. Redefining Success Metrics for the AI Search Era Aleyda Solis Orainti / SEOFOMO / Finchling @aleyda | Speakerdeck.com/aleyda
2. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 The old organic search measurement model built around clicks is breaking with AI Ranked list Traditional Search Stable position Click-first One-engine mindset Synthesized answers AI Search Volatile outputs Influence without clicks Platform fragmentation
3. https:/ /www.tryprofound.com/blog/ai-shopping-journey-2025 Purchasing decisions are made in AI, but users are converting elsewhere
4. AI referral traffic is the floor, not the ceiling BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 DIRECT CLICK BRAND RECALL AI SEARCH REVENUE AI SEARCH TRAFFIC OFF PLATFORM INFLUENCE DECISION IN AI PLATFORM WEBSITE VISIT INDIRECT & MULTI-TOUCH AI SEARCH VISIBILITY PURCHASE IN AI PLATFORM CHECKOUT & AGENTIC COMMERCE
5. https:/ /hub.seofomo.co/surveys/organic-search-trends/ This is also today’s biggest search problem: Attribution & trust as AI keeps clicks
6. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 It’s time to change this by assessing AI search success via 3 AI metric layers Metric Layer Why it matters KPI Roles 1. Presence Are you actually appearing, and how? Replaces traffic-only thinking with visibility and representation measurement Visibility KPIs Optimization and monitoring 2. Readiness Are you structurally prepared to be surfaced? Explains why visibility is weak, strong, or unstable; the diagnostic layer Diagnostic KPIs Diagnosis and prioritization 3. Business Impact Are visibility and readiness translating into value? Connects AI search activity to commercial outcomes without overclaiming attribution Outcome KPIs Executive reporting and decision-making
7. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 1. Presence Is the brand actually appearing in the AI answers that matter, and how is it being represented when it does?
8. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Create an AI presence measuring protocol focused on commercial value & action Prioritize top 2–3 AI platforms based on AI traffic referral in vertical, audience usage, and commercial relevance. Create prompts libraries reflecting the kinds of constraints real buyers actually use in AI platforms Prioritize analysis around high commercial intent and high influence prompt groups Look for patterns over time, not single-run results, because AI outputs vary by session and platform Translate each visibility gap into a likely readiness diagnosis
9. Semrush Identify and focus on the top 2-3 AI platforms driving more traffic to your competitors vs your own site
10. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Avoid a poorly representative prompt library that distorts what you measure and don’t drive the needed value Treating prompts like keywords without context Only tracking "best X" prompts Tracking too few prompts Not tracking each product line / market / persona individually
11. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Build prompts from how buyers actually discover, compare, validate, and choose your products or services Source prompts from non-brand demand data, sales-call transcripts, support conversations, reviews, community language, prompt sample data from AI tools and Bing Webmaster Tools Group prompts by their target market / language, product/service line, stage of the customer journey, audience/persona and buyer constraints Add realistic persona, product-line, market, and buyer constraint variants
12. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Prompt libraries should reflect the kinds of constraints real buyers actually use in AI platforms Constraint dimension Examples Price band free, under $X, enterprise Team or company size freelancer, small team, mid-market, enterprise Industry or vertical B2B SaaS, ecommerce, healthcare, financial services Integration needs tooling the buyer already uses (Slack, HubSpot, Salesforce, etc.) Geography and market country, region, language Use case or job-to-be-done the specific problem being solved Compliance or trust requirements SOC 2, GDPR, HIPAA, industry certifications
13. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Use these constraints across each product line, persona and user journey stage Stage Prompt focus Persona Key constraints in prompt Example prompts for Finchling Top of funnel Category discovery, educational prompts, broad solution prompts PR agencies Team size, number of clients, industry focus, integration needs, price band What are the best PR opportunity tools for a 10-person digital PR agency managing 15 B2B tech clients that needs Slack integration? In-house PR teams Company size, industry, geography, integration needs, compliance needs Which tools help an in-house PR team at a healthcare company find timely media opportunities while supporting stricter compliance needs? Mid funnel Comparative, evaluation, use-case, and trust prompts PR agencies Team size, vertical, integration needs, budget, workflow needs Finchling vs Google Alerts for a small PR agency managing multiple B2B SaaS clients: which is better for finding relevant opportunities faster? In-house PR teams Industry, company size, geography, workflow needs, trust requirements What is the best PR tool for an in-house communications team that needs trustworthy, relevant story opportunities for a mid-market SaaS brand in Europe?
14. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Apply probabilistic sampling rather than exhaustive coverage to keep the prompt set manageable, prioritizing the highest value prompts Brand profile Rough library size Single product, loose persona segmentation 30–60 prompts across key journey stages with a small set of high-priority constraints Single product, strong persona segmentation 50–100 prompts across personas, journey stages, and selected buyer constraints Multi-product or multi-service brand 100–250+ prompts segmented by line, persona, journey stage, and prioritized constraints Enterprise or holdco with multiple verticals 250+ prompts across multiple lines, personas, markets, journey stages, and constraints; dedicated tooling becomes essential
15. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Group prompts per topics to track and assess your visibility share at a topical level, not individual prompts, due to their dynamic nature
16. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Measure your brand AI presence using these 5 minimum KPIs for key questions AI Presence KPIs What it measures Prompt coverage Are we showing up where we need to? Measures whether the brand appears at all across the prompts that matter. Recommendation rate Are we being endorsed, or just included? Measures whether the brand is actively recommended when it appears, rather than simply mentioned in a list or cited in passing. Linked citation rate Is this visibility capable of driving visits or purchases? Measures how often the brand is not only mentioned but also cited with a clickable link or linked source. Comparative win rate Are we winning the shortlist when users compare options? Measures how often the brand is framed as the stronger or preferred option in prompts where multiple brands are evaluated against each other. Representation accuracy Are we being understood properly, or misrepresented? Measures whether the brand is described correctly when it appears: what it does, who it is for, and why it is relevant.
17. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Here’s an example of what each of these 5 AI search presence KPIs should look like AI Presence KPIs Example Prompt coverage Are we showing up where we need to? If you track 100 relevant prompts and your brand appears in 42 of them, your prompt coverage is 42%. Recommendation rate Are we being endorsed, or just included? Out of 40 prompts where your brand appears, the AI explicitly recommends your brand in 18. Your recommendation rate is 45%. Linked citation rate Is this visibility capable of driving visits or purchases? Your brand appears in 30 answers, and in 12 of those the AI includes a clickable link to your site. Your linked citation rate is 40%. Comparative win rate Are we winning the shortlist when users compare options? Across 20 comparison prompts such as “What’s better for digital PR teams, Finchling or Google Alerts?”, the AI favors Finchling in 11 responses. Your comparative win rate is 55%. Representation accuracy Are we being understood properly, or misrepresented? The AI mentions Finchling in 25 answers. In 20, it correctly describes it as a platform that helps PR teams find reactive and proactive story opportunities. In 5, it incorrectly frames it as a generic media monitoring tool only. Your representation accuracy is 80%.
18. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Select the KPIs to lead your dashboard depending on your business model Should lead with linked citation rate and comparative win rate. Revenue depends on click-capable mentions and on winning selection-stage prompts such as “best running shoes under $150” Transactional sites (ecommerce, marketplaces) Should lead with recommendation rate and comparative win rate. The buyer journey is consultative: being actively endorsed for provider-selection prompts such as “best PR agencies for SaaS” Lead gen and service sites (agencies, local services) Should lead with recommendation rate, comparative win rate, and representation accuracy. The category is usually crowded and comparison-heavy, so being framed correctly is as important as being surfaced. SaaS and product-led businesses
19. Similarweb You can build your own dashboard sourcing these metrics from AI monitoring platforms
20. Semrush AIO Or use these platforms if they allow you to track the 5 KPIs & answer key questions
21. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 What’s important is that your AI search presence dashboard helps answer these key questions Where does the brand appear, and where is it silent? Which platforms, journey stages, personas, product lines, or markets show the widest gaps? When the brand appears, is it genuinely recommended or merely listed among alternatives? Are mentions click-capable, or do they stay trapped inside the AI answer with no link? In head-to-head or shortlist prompts, does the brand win, tie, or lose; and against whom consistently? Is the brand being described accurately, or is it misframed, outdated, or confused with another product? Which third-party domains shape the outcomes, and where is the source ecosystem working against the brand?
22. 2. Readiness Are you optimized to be surfaced in AI Answers? BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026
23. https:/ /www.aleydasolis.com/en/ai-search/ai-search-winning-brands-characteristics/ AI search winning brands have 10 key characteristics tied to AI presence that should be assessed and monitored for optimization. These are your readiness KPIs.
24. Readiness explains the structural optimization reasons behind AI visibility gaps Weak category visibility often points to Corroborated, Differentiated, or Useful gaps Weak recommendation rate often points to Credible, Corroborated, or Differentiated gaps Poor representation accuracy often points to Recognizable or Consistent gaps Weak commercial visibility often points to Transactable, Extractable, or Useful gaps BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026
25. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Can the relevant pages be reached and fetched reliably by AI crawlers? Accessible Does the content solve the user need competitively — better than what else is on the first page of AI answers? Useful Are brand and entity signals explicit (name, category, founder, HQ, funding, product lines) and machine-readable? Recognizable Are key answers, positioning, and differentiators easy to parse and summarize from the page? Extractable Do those entity signals match across site, Wikipedia/Wikidat a, LinkedIn, review sites, and press? Consistent Do multiple independent third-party sources reinforce the same positioning and claims? Corroborated Do the sources that reinforce the brand carry weight (recognized publications, analyst coverage, peer-reviewed or primary data)? Credible Is the positioning clear, specific, and ownable — or is the language interchangeable with competitors? Differentiated Is the content recent enough (publish/update dates, current facts, live pricing) to remain credible and citable? Fresh Are pricing, plan logic, feature comparisons, and evaluation surfaces clear enough that AI systems can answer “which plan fits my case” questions? Transactable Audit the 10 AI readiness characteristics by asking these questions
26. https:/ /www.aleydasolis.com/en/ai-search/ai-search-winning-brands-characteristics/ Here’s an AI Search Readiness Checklist to use, with tools & how to verify each
27. Profound, Semrush Use the AI presence data to focus the analysis to sites influencing your visibility
28. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Key AI characteristics When to prioritize What to learn / action Accessible When content appears hard to fetch or pages are missing from cited outcomes Low scores suggest crawl, fetch, rendering, or access barriers that can suppress visibility Extractable When the brand is mentioned but rarely linked or summarized cleanly Low scores suggest the content is hard for AI systems to parse, summarize, or cite Useful / Fresh / Differentiated When category visibility or recommendation is weak Low scores suggest the content does not solve the question well enough, is stale, or lacks clear positioning Recognizable / Consistent When the brand is misdescribed or inconsistently framed Low scores point to entity clarity and message consistency problems across surfaces Corroborated / Credible / Transactable For trust, shortlist, and commercial prompts Low scores often explain weak recommendation, weak comparison performance, and weak commercial visibility You’ll get the prioritized AI characteristics to optimize tied to your AI visibility gaps
29. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Here’s an AI search readiness assessment outcome example, tied with an action Your Presence dashboard shows the brand appears in 70% of “best PM tools for engineering” prompts but in only 12% of “[brand] vs competitors” head-to-head prompts, and in that 12% it’s framed as “a newer alternative” rather than on its actual differentiators. That’s not a distribution problem since visibility exists upstream. It’s a Differentiated + Corroborated + Credible gap. The brand is surfaceable but not positioned strongly enough in the third-party sources AI platforms weigh at the comparison stage. So Layer 2 work should focus on comparison-site pages, analyst coverage, and positioning consistency across G2/Capterra/review sites.
30. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 3. Business Impact Are AI visibility and readiness efforts translating into value?
31. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 The goal of this stage is not perfect attribution, it’s an honest reporting model to support budget, planning, and prioritizing decisions without overclaiming
32. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Metrics from platforms passing a referrer or a UTM. Highest confidence, lowest coverage. Eg. AI referred sessions, AI conversion rate, revenue per AI visit, AI-assisted conversions. Directional signals from your own analytics or tools that sample AI traffic across the web. Medium to medium-low confidence, broader coverage. Eg. 1. Own: branded search lift, direct/unattributed lift, demand for pages known to be surfaced in AI answers, survey-based discovery. 2. External: Similarweb AI traffic behavior vs. competitors, prompt samples per page, etc. Estimates produced by applying assumptions to observed and proxy data. Lowest confidence, used for planning, never for proof. Eg. influenced pipeline, influenced revenue, incrementality estimates. Observed Proxy (own & third party) Modeled Assess AI biz impact via 3 different metrics layers with unique confidence levels
33. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Here’s an example showing how these KPIs coexist and can be useful in different ways 1,820 AI referred sessions, 6.1% trial start rate, 2.4x the organic benchmark. 1. Own: Branded “Finchling” search +22% QoQ; direct traffic to /features/reactive-pr +38% QoQ. 2. Third party: Similarweb estimates ~4,200 AI sessions (~2.3x GA4); AI traffic share in the PR tools peer set at 6% vs. Muck Rack 41%, Prowly 22%. Applying a 30% AI attribution assumption to the branded lift, estimated influenced pipeline of ~€14K ARR for the quarter. Caveat band attached. Observed Modeled How many users clicked and converted from an AI answer? Highest confidence, lowest coverage. Is there evidence users are seeing us in AI answers even when they do not click? Medium and Medium-low confidence, broader coverage. If we assume X% of branded search lift is AI-attributable, what is the implied pipeline? Lowest confidence, used for planning, never for proof. Proxy (own & third party)
34. https:/ /datastudio.google.com/u/0/reporting/49c18cc1-d73b-4944-ba69-f37e45e93914/page/EPR4C Build your observed tracking layer using analytics + CRM data to segment AI activity
35. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 AI Sessions by platform, landing page, device. Engagement rate and average engagement time versus the organic benchmark. AI conversion rate and revenue per visit, segmented by platform where volume allows. AI assisted conversions (data-driven attribution model in GA4, or multi-touch in the CRM). Top AI landing pages: The pages that are being cited visited by users. Track these observed metrics to measure and assess your AI impact
36. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 The observed layer is the floor. Proxy signals fill in some of the ceiling. To be valuable, there should be a pattern consistent with an AI driven story. From your own analytics, higher trust but inward-looking. Lower trust but the only window onto competitors and prompt level behavior. Own Proxy Signals: Third Party Tools Signals:
37. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Category How to do it Branded search trend GSC query report filtered to brand terms, or the native branded/non-branded toggle. Track week-over-week and month-over-month. Direct and unattributed traffic trend GA4 Direct channel, especially to pages that are not shared in email or paid campaigns. Demand for frequently-surface d pages Impressions and direct/organic traffic to the pages you have verified are being cited in AI answers. Survey-based discovery One question added to signup, demo, or post-purchase flows Bing Webmaster Tools AI Performance First-party citation counts, cited URLs, and grounding queries for Microsoft Copilot and Bing AI summaries. The only first-party citation data available from any AI ecosystem today. Here are a few potential “own site” proxies signals you can use to identify AI driven patterns
38. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Eg. Add one AI survey discovery question and use incrementality selectively: “Did you come across your brand in an AI assistant before buying?”
39. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 A rising “Yes” rate in the survey to users attributed to Direct or Branded Organic is the strongest proxy that AI influence exists beyond what analytics shows Users who arrive via branded search with a high “Yes” rate are the invisible AI influence. They are attributed to Organic in GA4 but would not have searched the brand without an AI mention. Users who arrive via Direct with a high “Yes” rate are the mobile ChatGPT copy-paste cohort. GA4 attribution is entirely blind to them. Users who arrive via the AI Search channel itself but answer “No” are usually mis-attributed. Cleaning these out sharpens the observed layer.
40. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Third party proxies like Similarweb or Semrush data can directionally fill competitors & prompts behavior for relative reads
41. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 For example, here are 3 third-party proxies reads worth running monthly 1. Prompt samples driving traffic to your top AI landing pages. Use it to: • Extend your Presence prompt set • Diagnose landing-page mismatch • Inform Readiness work 2. Competitive benchmarking of AI traffic share, top landing pages, and top prompts per page. Use it to assess: • AI traffic share trend vs competitors over time • Which pages competitors are getting AI traffic to and which are distinctively yours. • Prompt overlap per page vs competitors 3. AI platform mix over time, benchmarked. Which AI platforms refer traffic to your site and to competitors. Use it to assess: • Platform specific decay • Platform specific wins • Catch category shifts early
42. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Finally, build the business impact modelled layer to estimate what you can’t measure directly (Incremental branded clicks, visits, leads, or pipeline above baseline) × (stated AI influence assumption %) = modelled influenced value
43. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 A planning number that accounts for AI influence invisible to observed tracking. A way to translate directional proxy signals into commercial terms leadership can use for budget conversations. The modelled layer should be treated as a planning construct, not as attributed revenue. A disciplined alternative to either ignoring AI influence because it can’t be measured cleanly, or overclaiming it by crediting AI for every branded search lift.
44. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 For example, here’s what you can track through your modelled layer Modelled influenced pipeline or revenue, stated as a range with inputs documented. Attribution percentage applied over time, tracked alongside survey discovery rate so the two move together. Sensitivity band: What the number looks like at ±10 percentage points of attribution %, so leadership sees how much depends on the assumption.
45. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Branded search lift from GSC: the clearest proxy most brands will have. Direct traffic lift to cited pages: useful where mobile to direct AI journeys are common. Here are a few inputs to combine for your modelled estimates Survey AI discovery rate: often the strongest first-party anchor for the AI influence assumption, because it grounds the estimate in observed user reported behavior. Historical conversion value per visit or lead: to translate sessions into commercial terms.
46. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Build a dashboard with these business metrics layers with levels of confidence to help to assess AI search biz evolution & drive action
47. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Metric How to select it What to learn / action Low readiness + low visibility Structural conditions are holding the brand back. Prioritise access, extractability, entity clarity, corroboration. High readiness + low visibility Brand is underdistributed or underrepresented in the source ecosystem. Focus on source presence, distribution, trust ecosystem, competitive disadvantage. Visibility improving + impact flat Brand is appearing but not memorably, persuasively, or on the right pages. Improve recommendation quality, linked citations, memorability, landing-page fit. Strong informational + weak commercial visibility Visible early in the journey but not winning shortlist or selection moments. Improve commercial prompt coverage and transaction-ready surfaces. High visibility + strong recommendation + weak representation accuracy Being talked about but described wrong. Entity and source correction: Wikipedia/Wikidata, schema consistency, review sites, supplier pages, analyst briefings. One segment strong, another weak Issue is segment-specific, not brand-wide. Run a segment-specific readiness and source-ecosystem review. Tying your AI presence, readiness & biz KPIs is what will help to drive relevant action
48. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Eg. The AI metrics layers assessment outcome and next steps for a SaaS AI Metrics Layers Status: • Presence dashboard shows 58% prompt coverage in ChatGPT for discovery prompts but 11% recommendation rate in shortlist prompts. • Readiness assessment shows Differentiated and Credible scoring well, but Corroborated scoring low (few third-party reviews, limited presence on roundup sites). • Business Impact shows flat AI referral traffic and slightly rising branded search. The Assessment and Next Steps: • Matrix read: “high readiness + low visibility” at the commercial end of the funnel. • Diagnosis: the structural work is mostly done. The bottleneck is source-ecosystem presence at the comparison stage. AI models have nowhere to learn about Finchling in the context of selection prompts because Finchling is not in the sources they cite for those prompts. • Move: concentrated effort on getting Finchling onto software roundup pages, G2 and Capterra category pages, and reactive-PR tool comparisons. Not more content. Not more technical SEO. The lever is external corroboration.
49. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Metric Layer Why it matters KPI Roles 1. Presence Are you actually appearing, and how? Replaces traffic-only thinking with visibility and representation measurement Visibility KPIs Optimization and monitoring 2. Readiness Are you structurally prepared to be surfaced? Explains why visibility is weak, strong, or unstable; the diagnostic layer Diagnostic KPIs Diagnosis and prioritization 3. Business Impact Are visibility and readiness translating into value? Connects AI search activity to commercial outcomes without overclaiming attribution Outcome KPIs Executive reporting and decision-making It’s time to use the 3 AI metric layer to assess, understand & drive impact
50. https:/ /bit.ly/4tOjbyJ A lot information, too little time? Don’t worry, check out the guide going through it with resources on my site
51. BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Thanks! Questions? SEO Consultant & Founder ❏ International SEO Consultant & Founder of Orainti ❏ Co-founder of Finchling ❏ Creator of the SEOFOMO, Marketing FOMO, AI Marketers and Trending Campaigns Newsletters ❏ Maker of LearningAIsearch.com & LearningSEO.io ❏ Author of SEO, Las Claves Esenciales ❏ Spoke at +200 events in +30 countries BY ALEYDA SOLIS FROM ORAINTI / SEOFOMO / FINCHLING AT BRIGHTONSEO UK APRIL 2026 Speaker and author