5W RESEARCH · APRIL 2026

AI and the Israeli Brand

How LLMs and Generative Engine Optimization are reshaping consumer discovery, earned media value, and marketing spend in Israel.

A 5W Research Study · April 2026

KEY FINDINGS

This study assembles more than ninety data points from published industry research to quantify how generative AI is reshaping brand discovery in Israel and globally. The conclusions point to a single underlying shift with direct consequences for how marketing and communications budgets are allocated: earned media has become the primary input to AI citation, and therefore to brand visibility in the channel that is now consumed before every other one.

The shift in user behavior

35% of consumers now use AI tools at the product discovery stage, versus 13.6% who start with a traditional search engine (Similarweb, January 2026). 27% of US users now prefer AI tools over traditional search for discovery (Semrush, 2026). 58% of consumers have already replaced traditional search with AI tools for product and service discovery (Capgemini, 2025). 64% of consumers are willing to purchase products suggested by AI (Master of Code, 2024). 42% of B2B decision-makers use an LLM in the first step of the buying process (Omniscient, 2026). 93% of AI Mode searches end without a click; 43% of AI Overviews do; roughly 60% of all US and EU searches are now zero-click.

The platforms

ChatGPT reached 800 million weekly users in October 2025, doubling from 400 million in February 2025. Gemini grew 157% between April and September 2025 to 1.1 billion monthly visits. Perplexity processed 780 million queries in a single month, up from 230 million in August 2024. ChatGPT accounts for approximately 79% of global generative AI web traffic. US enterprises dedicated an average of 12% of digital marketing budgets to GEO in 2025, and 94% plan to increase that spend in 2026 (eMarketer).

The citation economics

85.5% of AI citations reference earned media sources (Muck Rack analysis of over one million AI prompts). AI engines cite earned media approximately 5x more frequently than brand-owned websites (University of Toronto). Distributing content across a wide range of publications can increase AI citations by up to 325% compared with publishing on the brand site alone. Brands appearing on four or more third-party platforms are 2.8x more likely to be cited in ChatGPT responses than single-platform brands. Brand search volume is the strongest predictor of AI citation (0.334 correlation), ahead of any technical SEO signal.

The commercial signal

AI search visitors convert at 14.2% versus Google’s 2.8% — approximately 5x more valuable per visit (Semrush, 2026). ChatGPT referrals convert to transactional sites at 7% versus 5% from Google, with 15 minutes on site versus 8 and 12 pageviews versus 9. AI-driven traffic to US retailers grew 4,700% year-over-year as of July 2025.

The Israeli market

Israel’s digital advertising market is projected at $1.58 billion in 2025, rising to $1.91 billion by 2028. Google captures approximately 46% of Israeli digital ad spend; Meta captures approximately 15%. Over 300 SaaS companies operate from Israel, with flagship firms reporting revenues of $300M to $1.1B+. 47% of brands globally have no GEO strategy. The Israeli private-sector GEO allocation materially lags North American benchmarks.

EXECUTIVE SUMMARY

Generative AI has moved the top of the marketing funnel. In the United States, 35% of consumers now begin product discovery inside an AI tool compared with 13.6% who begin inside a search engine. In B2B, 42% of decision-makers now open the buying process with a query to an LLM. The shift is no longer a forecast; it is a measured change in consumer and buyer behavior that is reshaping which brands are seen, which are considered, and which are bought.

The most important single finding for communications and marketing leaders is that this shift rewards earned media at a structural level. Muck Rack’s analysis of over one million AI prompts found that 85.5% of AI citations reference earned media rather than brand-owned content. Research from the University of Toronto puts the ratio at approximately five to one. When AI assembles an answer, it prefers third-party coverage, professional-platform presence, community discussion, and independent review sites over corporate websites. Broad distribution can raise AI citation rates by up to 325%. A brand on four or more third-party platforms is 2.8 times more likely to be cited than one that relies on its own domain.

This reframes the role of public relations. The same earned-media placements, executive thought leadership, LinkedIn presence, Wikipedia accuracy, review-platform management, and community engagement that communications firms have always produced are now also the primary retrieval inputs to AI-generated answers. Work that historically read as reputational overhead now has measurable performance-marketing value.

For the Israeli market, the opportunity is unusually well-defined. Israel’s $1.58 billion digital advertising economy is concentrated on two platforms (Google at 46% of local digital spend, Meta at 15%) that are themselves becoming AI-answer surfaces. Israel’s largest consumer categories are oligopolistic, so share of model translates directly into share of wallet. Israeli technology exports operate in English-language AI environments where competition is already intense. And Hebrew, as a minority training language in every frontier model, has a thinner source pool — which means a disciplined Hebrew earned-media and content strategy can capture disproportionate AI citation share while competitors wait.

Across the ten major Israeli consumer and B2B sectors mapped in Section 7, a 15–25% reallocation from pure paid search toward earned-media-driven GEO implies a national annual reallocation in the range of NIS 350 million to NIS 600 million over the next 24 to 36 months, or roughly $100 million to $170 million, before any net-new spending is added.

1. THE SHIFT: SEARCH TO AI ANSWERS

Generative Engine Optimization (GEO) is the emerging discipline of structuring content, third-party coverage, and brand mentions so that generative AI systems cite a brand by name when answering a user question. GEO does not replace search engine optimization. Approximately 99% of Google AI Overview citations still originate from the organic top 10, and 87% of ChatGPT citations correspond to Bing top results. GEO layers a new requirement on top of SEO: the content must be cited inside the synthesized answer, not only ranked below it.

Adoption is faster than any previous channel shift. ChatGPT reached 100 million users in 60 days after its November 2022 launch — the fastest consumer adoption in technology history. It reached 800 million weekly active users by October 2025. Gemini grew 157% between April and September 2025. Perplexity processed 780 million queries in a single month, up from 230 million in August 2024 — more than 3x in under 18 months. Business AI adoption rose from 14% to 29.2% in the first half of 2025. 84% of businesses now consider AI their top strategic priority.

Consumer behavior inside AI is different from consumer behavior inside search. Average session length on AI search is 6 minutes, compared with seconds on Google. Average AI query length is 23 words, compared with 4 words on Google. Median time spent in AI Mode: 77 seconds comparing brands or products, 71 seconds learning information, 52 seconds choosing or purchasing. Users treat AI responses as authoritative answers rather than starting points, producing higher trust transfer to the brands cited inside them.

The traffic is small but disproportionately valuable. AI referral traffic is approximately 1% of total web visits globally, growing approximately 1 percentage point per month. AI search visitors convert 5x better per visit than traditional organic. Users referred from ChatGPT spend 15 minutes on site versus 8 for Google referrals, generate 12 pageviews versus 9, and convert to transactional sites at 7% versus 5%. AI-driven traffic to US retailers grew 4,700% year-over-year as of July 2025. Click-through rate on informational queries falls from 1.41% to 0.64% when an AI answer appears — a 55% decline.

2. THE ISRAELI ADVERTISING MARKET

Israel’s advertising economy has been digitizing steadily for a decade and is now structurally dependent on platforms that are themselves being reshaped by generative AI.

Market size and composition (2025): Digital total $1.58B (rising to $1.91B by 2028); Search ~$597M; Social ~$460M; Digital video ~$280M; Influencer ~$67M; Traditional TV ~$260M; Programmatic share of digital >82%; Government (LAPAM) $120M+.

Five structural features that amplify the Israeli GEO opportunity:

Platform concentration. Google captures approximately 46% of Israeli digital ad spend; Meta captures approximately 15%. Roughly 61% of local digital advertising flows through two platforms that are integrating AI answers directly into the user experience. The platforms Israeli brands already buy from are the platforms that are intermediating away their direct click traffic.

Oligopoly structure. Israel’s largest consumer categories are concentrated: five major banks, three cellular carriers, four HMOs, two dominant supermarket chains, four to five top insurers. In oligopoly, share of model maps more directly onto share of wallet than in fragmented markets, and early GEO investment has outsized defensive and offensive consequences.

Mortgage and credit cycle. Israeli banking sector assets grew 10–12% year-over-year through 2024–2025, driven by a mortgage lending boom. High-intent Hebrew financial queries are currently among the most valuable uncontested AI real estate in the market.

Export dependency. Israel’s largest corporates derive significant revenue from overseas. Their visibility inside English-language AI answers is a direct input to export demand and enterprise sales pipeline. The English-language citation environment operates as a separate market that must be worked independently of the Hebrew one.

High digital engagement. Israeli consumers index above OECD averages on smartphone penetration, e-commerce adoption, and digital service use. AI-answer behaviors arrive earlier and faster than in peer markets, which means the Israeli market both sees the shift sooner and rewards early movers at a higher rate.

3. CATEGORY EXPOSURE: WHERE THE MONEY MOVES FIRST

The shift from search to AI answers does not affect all categories equally. Exposure correlates with three variables: the share of consumer decisions that begin with an informational query, the financial value of a converted customer, and the maturity of the category’s digital funnel.

Banking (High). Hebrew AI answers to high-value financial queries default to generic guidance or aggregator content. Major Israeli banks rank well on traditional SEO but are rarely named inside AI responses, leaving significant uncontested share of model during a period of record mortgage origination. Financial comparison queries in Hebrew represent one of the highest-value uncontested pockets of AI real estate in the market today.

Insurance & Pensions (High). Policy comparison, claim processes, rate questions, pensions, life insurance. Given that insurance PPC keywords are among the most expensive in digital advertising, the unit-economic case for shifting toward compounding earned media is stronger in insurance than in most verticals.

Telecom (Very High). AI answers about Israeli cellular plans and fiber frequently cite Hebrew-language reviews containing outdated promotional pricing. Real-time plan updates do not propagate into the model’s retrieval layer, creating a structural lag that reduces the efficacy of live acquisition campaigns.

Retail & FMCG (High). Grocery delivery, product substitutes, recipe-driven brand surfacing, dietary and ingredient questions.

Travel & Hospitality (Very High). Global aggregators dominate AI citations for Israeli hotel and flight queries. Direct-to-consumer brands rarely surface in AI answers unless named explicitly, suppressing direct-booking revenue and increasing dependence on commission channels.

Tech & SaaS (Critical). This is the single most exposed category. 42% of B2B decision-makers now open evaluation inside an LLM, and the AI response functions as the shortlist. Israel’s tech sector sells disproportionately into English-speaking markets, where competition for AI citation is already intense and where most Israeli vendors have not yet invested.

Defense & Industrial (Sovereign). A sovereign-level narrative risk rather than a marketing problem. Multiple independent audits of leading AI models have documented uneven and at times unfavorable framing of Israel-related subject matter in English-language responses. The narrative inherited inside an English-language AI answer is not controllable unless citation surface area is actively built on authoritative domains.

4. THE EARNED MEDIA IMPERATIVE

The single most consequential finding in the body of 2025–2026 GEO research is that AI systems prefer earned media at a structural level. This reverses two decades of digital marketing orthodoxy, which held that owned channels deliver the highest-margin, longest-duration brand value. In the AI-mediated discovery environment, owned content is a secondary input. Third-party coverage is the primary input.

What the citation data shows: Muck Rack analyzed more than one million AI prompts and found 85.5% of AI citations reference earned media sources. University of Toronto research found that AI engines cite third-party publications approximately 5x more frequently than brand websites. When a user mentions a brand by name in a query, earned media accounts for 48% of resulting citations. When a user asks what customers think about a brand, earned media accounts for 82% of citations. Owned content performs best only for functional and specification queries (approximately 50% citation share).

Distribution mathematics favor disciplined PR: Distributing the same content across a wide range of publications can increase AI citation rates by up to 325% versus publishing only on the brand’s own site. Brands appearing on four or more third-party platforms are 2.8x more likely to be cited in ChatGPT responses. Domains with more than 32,000 referring domains are 3.5x more likely to be cited by ChatGPT than domains with fewer than 200. The average domain age of ChatGPT-cited sources is 17 years.

The new citation stack: The top 10 domains capture 46% of all ChatGPT citations within a given topic; the top 30 capture 67%. The dominant citation sources now include Reddit (most-cited single domain in multiple 2025–2026 analyses), Wikipedia (26–30% of B2B factual queries), YouTube (16% of LLM answers), LinkedIn (most-cited domain for professional queries across all major AI platforms), tier-one editorial (Forbes, Business Insider, WSJ, NYT, TechCrunch), and review platforms (G2 leads in software).

5. THE ISRAELI TECH EXPORT PREMIUM

Israel’s technology sector is the single most AI-exposed segment of the Israeli economy. Over 300 Israeli SaaS companies operate internationally, with flagship firms reporting 2024–2025 revenues between $150 million and $1.1 billion. The combined ecosystem generates tens of billions of dollars in annual revenue, the large majority sold into English-speaking markets where 42% of B2B buyers now open their evaluation inside an LLM.

The marketing spend reality in B2B SaaS: Median B2B SaaS company spends 8% of annual recurring revenue on marketing (SaaS Capital, 2025). Higher-growth companies spend approximately 40% more on marketing than lower-growth peers. Applied to the Israeli tech ecosystem’s top 50 private SaaS firms alone, median marketing spend conservatively exceeds $1.5 billion annually.

Customer acquisition cost benchmarks for Israeli exporters: SMB SaaS $300–$800 per customer (payback 6–7 months, rising); Mid-market SaaS $1,200–$2,000 (payback 12–18 months, rising sharply); Enterprise SaaS $2,000–$15,000+ (payback 18–30 months, rising); B2B paid search average $802 per customer acquired. CAC has risen approximately 60% over the past five years across B2B tech.

The AI invisibility premium: For an Israeli mid-market SaaS exporter with 500 new customers per year at $1,500 CAC, total acquisition spend is approximately $750,000 annually. If 42% of target buyers now open evaluation in an LLM, approximately $315,000 of that spend is exposed to whether the brand is cited inside the AI answer during the initial shortlist stage. Industry estimates put the effective CAC premium for AI-invisible B2B tech brands in the 15–35% range, depending on category density.

The compounding earned-media dividend: Every unit of earned media invested in an Israeli tech brand — tier-one feature, bylined article, executive podcast appearance, conference keynote, data report placement — now produces two stacked returns: the traditional reputational return and the AI citation return. A single Forbes placement in 2026 will produce citation surface area that feeds AI answers for years, compounding against the 17-year average domain age of ChatGPT-cited sources. The firms that move in the next 12 to 18 months are building AI-citation infrastructure that will remain in place as retrieval preferences stabilize.

6. HOW COMMUNICATIONS FIRMS PRODUCE AI CITATION

Every service line that public relations firms have historically sold — earned media placement, executive thought leadership, LinkedIn and social strategy, Wikipedia and reference-site accuracy, press release distribution, awards, data-led research, podcast bookings, review-platform management, crisis response — is now also a direct input to AI citation. The work has not changed. The value of the output has.

LinkedIn: the highest-ROI manageable channel. Profound’s March 2026 analysis found LinkedIn to be the most-cited domain for professional queries across every major AI surface — AI Overviews, AI Mode, ChatGPT, Microsoft Copilot, and Perplexity. Superlines’ February 2026 dataset of 62 brands over 30 days recorded 15,835 LinkedIn citations, nearly matching YouTube’s 15,735. A communications program that systematically manages executive LinkedIn presence produces measurable citation lift within 60 to 90 days — faster than any earned media program can deliver.

Key citation levers by manageability:

Fully manageable: LinkedIn program (most-cited domain for professional queries); Press release & data report distribution (wire services syndicate to hundreds of publications that feed AI retrieval).

Highly manageable: Wikipedia accuracy (cited in 26–30% of B2B factual queries); Executive thought leadership (bylines, op-eds, podcast appearances, conference keynotes); Review platform presence (3x higher citation rates with G2, Capterra, Trustpilot, Yelp profiles).

Manageable: YouTube and podcast distribution (YouTube cited in 16% of LLM answers); Awards and rankings (comparison and “best of” content drives 32.5% of AI citations — the single largest format category); Crisis and AI-answer audit (continuous monitoring and remediation).

Partially manageable: Reddit / Quora / community (Reddit is the most-cited single domain in many LLM responses; requires authentic subject-matter-expert engagement).

What communications firms audit and report: Share of Model (SoM) — percentage of AI answers to a defined query set that name the brand, tracked monthly in Hebrew and English; citation source mix; sentiment in citation; prompt-level movement; competitive benchmark; platform split across ChatGPT, Gemini, Perplexity, Google AI Mode, and Microsoft Copilot. Note: only 11% of domains earn citations across both ChatGPT and Perplexity — platform-specific strategies outperform single-message campaigns.

7. SECTOR SPEND ECONOMICS

The reallocation figures below assume a 15–25% migration of the AI-exposed share of marketing spend toward GEO, earned media, structured content, LinkedIn programs, and AI-answer management over 24 to 36 months. This is the range observed in North American enterprise allocations and published GEO benchmark reports.

Banking: Est. NIS 600M–900M annual spend; 40–55% AI-exposed; indicative GEO reallocation NIS 40M–125M.

Insurance & Pensions: NIS 500M–700M; 45–60% AI-exposed; NIS 35M–100M reallocation.

Telecom: NIS 350M–500M; 55–70% AI-exposed; NIS 30M–85M reallocation.

Retail & FMCG: NIS 1.2B–1.8B; 30–45% AI-exposed; NIS 55M–200M reallocation.

Travel & Hospitality: NIS 250M–400M; 55–75% AI-exposed; NIS 20M–75M reallocation.

Pharma & Health: NIS 350M–500M; 35–50% AI-exposed; NIS 20M–60M reallocation.

Automotive: NIS 400M–550M; 45–60% AI-exposed; NIS 30M–80M reallocation.

Energy & Utilities: NIS 150M–220M; 25–40% AI-exposed; NIS 8M–25M reallocation.

Tech & SaaS (export): NIS 5.5B–8B; 60–80% AI-exposed; NIS 500M–1.6B reallocation.

Government: NIS 450M+; 40–55% AI-exposed; NIS 30M–65M reallocation.

Summing across all sectors, the aggregate national reallocation opportunity falls in a range of approximately NIS 750 million to NIS 2.4 billion — roughly $215 million to $680 million — over the next 24 to 36 months, before any net-new advertising spending is added.

8. COMPARATIVE MARKET BENCHMARKS

Enterprise GEO allocation in 2025: US 12% of digital budget; UK ~9%; Germany/EU ~7%; Israel <5% estimated. Share planning to increase GEO in 2026: US 94%; UK ~85%; Germany ~75%. AI-at-discovery user share: US 35%; UK ~28%; Germany ~22%; Israel above EU average. B2B buyers starting in an LLM: US 42%; UK ~35%; Germany ~30%; Israel above average. Estimated brands with zero AI Overview mentions: US 26%; UK ~35%; Germany ~40%; Israel ~50% estimated.

The US enterprise GEO allocation benchmark is a forward indicator. Historically, US digital-marketing allocation practices have reached European and Israeli enterprise adoption with an 18 to 36 month lag. The gap between user adoption and brand allocation is wider in Israel than in comparable markets, which enlarges the first-mover advantage. If Israel follows the US trajectory with the typical lag, the Israeli enterprise GEO allocation curve begins steepening in 2026–2027.

9. MEASURING COMMUNICATIONS ROI IN THE AI ERA

The primary metrics: Share of Model (SoM) — percentage of AI responses to a defined query set that name the brand, tracked monthly; Citation source mix — which domains are feeding the AI’s answer; Domain influence reporting — which specific placements have moved AI citation; Sentiment in citation — positive, neutral, and negative; Prompt-level movement — individual query outcomes tracked over time; Platform split — separate tracking across ChatGPT, Gemini, Perplexity, Google AI Mode, Microsoft Copilot.

Indicative return ranges from published benchmarks: +22% higher ROI on GEO spend compared with equivalent SEO investment (Incremys); +40% brand visibility increase on AI answer surfaces; 4.4x higher qualified-traffic share from AI referrals versus legacy organic; 5x per-visit conversion advantage on AI referral traffic; 3x citation multiplier for brands with review platform presence; 2.8x citation likelihood for brands on 4+ third-party platforms; up to 325% AI citation lift from broad-distribution earned media.

Applied calculation example: A mid-market Israeli B2B SaaS exporter with $30M ARR allocates $300,000 (12.5% of a $2.4M marketing budget) to a 12-month GEO and earned-media program. Over 12 months, Share of Model across 100 tracked category queries moves from 8% to 23%. AI referral traffic grows from 0.3% to 1.8% of site visits. Given a 5x per-visit conversion advantage on AI traffic, the incremental AI-referred pipeline contribution, at typical mid-market ACVs in the $30–80K range, pays back the program investment within one to three new customer wins.

10. THE ECONOMIC OPPORTUNITY

GEO is not an incremental optimization of existing marketing. It is a reallocation opportunity with measurable and improving unit economics and a compressed first-mover window.

Reported returns from early GEO programs: +22% higher ROI compared with equivalent SEO; +40% increase in brand visibility across AI answer surfaces; 4.4x higher share of qualified traffic from AI referral channels; 30–40% higher AI citation rates for content with proper schema, primary-source statistics, and expert attribution; Content with three comparison tables earns 25.7% more citations; citations concentrate in the opening third of a cited article (44.2% of all LLM citations come from the first 30% of text); 65% of AI bot traffic targets content published within the past year.

The first-mover window: AI systems reinforce their own citation preferences over time. The average domain age of ChatGPT-cited sources is 17 years — the system is biased toward entities with durable track records. Brands investing during the 2026 window are building citation surface area that compounds, directly analogous to the early SEO advantages accrued during 2005–2012. The current competitive floor is unusually low: 47% of brands have no GEO strategy in place; 26% have zero mentions in AI Overviews; only 11% of domains are cited across both ChatGPT and Perplexity. The arbitrage is available now. It will be substantially smaller in 18 to 24 months.

11. THE HEBREW DATA GAP

Hebrew is a minority training language in every major frontier AI model. This creates two asymmetric effects for brands operating in the Israeli market, and a third effect at the state level.

Effect one: thin source pool, low competitive floor. In Hebrew-language queries, the available pool of sources that AI systems draw from is materially smaller than the English, French, German, or Spanish pools. The Nagel Committee’s August 2025 report to the Israeli government identified the absence of a Hebrew national language model as a digital sovereignty issue. For brands, this is an opportunity: a disciplined Hebrew earned-media and content program can capture disproportionate share of model quickly because citation density is lower. The first 12–24 months represent the maximum arbitrage window.

Effect two: inherited English-language narratives. In English-language queries about Israeli companies, Israeli categories, or Israel itself, the source pool is dominated by international media whose coverage is uneven. For Israeli corporates with international revenue exposure — technology, pharmaceuticals, defense, consumer goods, tourism — the narrative inherited inside an English-language AI answer is not under the company’s control unless citation surface area is actively built on high-authority English-language domains.

Effect three: state-level recognition. Foreign Agents Registration Act filings made public in late 2025 disclosed that Israel’s Ministry of Foreign Affairs contracted a US firm on a multi-million-dollar engagement to build websites and content specifically designed to influence how generative AI systems frame Israel-related queries. Industry reporting has put total Israeli state spend on “chatbot optimization” in excess of half a billion shekels across multiple vendors. The private sector has not matched this urgency at scale. For communications firms operating in the Israeli market, the asymmetry between state-level sophistication and private-sector allocation defines the current professional opportunity.

FREQUENTLY ASKED QUESTIONS

How is AI changing brand discovery in Israel?

35% of consumers now use AI tools at the product discovery stage, versus 13.6% who start with a traditional search engine. In B2B, 42% of decision-makers use an LLM in the first step of the buying process. AI referrals convert at approximately 5x the per-visit value of traditional organic search (14.2% vs 2.8%).

Why does earned media matter so much for AI citation?

Muck Rack’s analysis of over one million AI prompts found that 85.5% of AI citations reference earned media sources. University of Toronto research puts the ratio at approximately 5x over brand-owned websites. Distributing content across a wide range of publications can increase AI citation rates by up to 325% versus publishing on the brand site alone.

What is the GEO reallocation opportunity for Israeli brands?

Across 10 major Israeli consumer and B2B sectors, a 15–25% reallocation from pure paid search toward earned-media-driven GEO implies a national annual reallocation in the range of NIS 750 million to NIS 2.4 billion — roughly $215 million to $680 million — over the next 24 to 36 months, before any net-new spending is added.

What is the Hebrew data gap and why does it matter?

Hebrew is a minority training language in every major frontier AI model. The available pool of sources AI systems draw from in Hebrew is materially smaller than in English or other major languages. This creates a first-mover opportunity: a disciplined Hebrew earned-media program can capture disproportionate share of model quickly because citation density is lower. The arbitrage window is 12–24 months.

Which Israeli sectors are most exposed to AI discovery disruption?

Tech and SaaS (export) is rated Critical — 42% of B2B buyers now open evaluation in an LLM. Telecom and Travel are rated Very High. Banking, Insurance, Retail, Pharma, and Automotive are rated High. The tech and SaaS export component accounts for the majority of the reallocation opportunity (NIS 500M–1.6B) because of sector size and elevated customer acquisition costs.

What does a GEO program cost for an Israeli SaaS company?

For a mid-market Israeli SaaS exporter at $25M ARR, a 12% allocation matching the US enterprise benchmark is approximately $240,000 annually — roughly one-eighth of total marketing spend. That budget funds earned media, LinkedIn executive visibility, comparison and review-site presence, Wikipedia accuracy, data-led research placements, and monthly Share of Model reporting.

What is Share of Model and how is it measured?

Share of Model (SoM) is the percentage of AI-generated responses to a defined query set that name the brand. Typical programs track 50–200 category queries in each relevant language, with separate platform tracking across ChatGPT, Gemini, Perplexity, Google AI Mode, and Microsoft Copilot. Only 11% of domains earn citations across both ChatGPT and Perplexity, so platform-specific strategies outperform unified approaches.

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METHODOLOGY AND SOURCES

This study synthesizes published data from industry research, academic research, regulatory filings, and platform analytics disclosed between 2023 and April 2026. Sector spend estimates combine Israeli advertising market data with published international marketing allocation benchmarks applied to sector revenue profiles; these are indicative ranges, not audited figures.

Key sources: Similarweb 2026 Generative AI Brand Visibility Index; Muck Rack analysis of over one million AI prompts; University of Toronto research on AI citation patterns; Omniscient Digital analysis of 23,000+ AI citations; Profound LinkedIn citation analysis (March 2026); Superlines 62-brand citation tracking (February 2026); SaaS Capital 14th Annual Survey of private B2B SaaS companies (March 2025); Semrush, Ahrefs, AirOps, Stacker platform and citation benchmarks 2025–2026; eMarketer US GEO budget allocation 2025; Nagel Committee Report on Accelerating AI in Israel (August 2025); Bank of Israel Annual Report 2024; Foreign Agents Registration Act filings, US Department of Justice 2024–2025; Calcalist, Globes, Times of Israel, Ynet industry coverage.

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