March 19, 202612 min read

GEO for B2B: How Neural Networks Influence Corporate Procurement

How B2B companies can get into AI recommendations. Specifics of corporate procurement, the role of AI at the research stage, expert content, and case studies.

Владислав Пучков

Основатель GEO Scout, эксперт по GEO-оптимизации

According to monitoring data from geoscout.pro, B2B companies with published case studies and structured FAQ receive mentions in neural network answers significantly more often than companies with exclusively marketing content. The platform daily tracks how 9 AI providers — including ChatGPT, Claude, Perplexity, and Yandex with Alisa — recommend B2B solutions for corporate queries, and shows at which stage of the procurement funnel your brand appears or is absent.

The new reality of B2B procurement

Imagine this scenario: the head of IT at a large company is looking for a document management system. Ten years ago, they would have started with Yandex, browsed dozens of websites, and requested commercial proposals. Today, they open ChatGPT and type: "Which electronic document management systems are suitable for a company with 500 employees and branches in 3 cities? Need integration with 1C and compliance with 152-FZ."

AI generates a list of 5-7 solutions with descriptions of advantages, limitations, approximate prices, and reviews. This list becomes the basis of the shortlist that the manager brings to the meeting.

Numbers you cannot ignore:

• 51% of Russians already use neural networks for decision-making
• 88 million users of Yandex with Alisa, including corporate users
• 30% of users make a decision based on the first AI answer
• AI traffic grew 6x during 2025
• In B2B procurement, most decisions are formed before the first contact with a seller

If your company is not in neural network answers for relevant queries — you do not make it onto the shortlist. And in B2B, making the shortlist determines everything.

How the procurement committee uses AI

B2B decisions are not made by one person. On average, 5-11 people with different roles participate in the process. And each of them may turn to neural networks at their own stage.

Roles and typical AI queries

Each of these queries is a point where AI forms an opinion about your company. Or does not form one, if there is no data about you. Learn more about tracking brand mentions in the article How to Track Brand Visibility in ChatGPT.

Three critical stages

Stage 1: Research

The buyer formulates the problem and searches for categories of solutions. Informational queries work here: "how to automate warehouse accounting," "which CRMs are suitable for manufacturing." At this stage, AI must know that your product exists and solves a specific problem.

Stage 2: Shortlist (Evaluation)

From a long list, 3-5 solutions are selected for detailed comparison. Typical queries: "Top 5 ERP systems for mid-size business in Russia," "Compare Bitrix24, AmoCRM, and Megaplan for B2B sales." At this stage, position in the recommendation list and the tone of the description are critical.

Stage 3: Justification

The chosen solution needs to be "sold" internally. The buyer looks for arguments: "Advantages of [solution] for [industry]," "Implementation cases of [solution] in large companies." At this stage, AI helps collect justification, and your content must provide concrete facts.

How B2B GEO differs from B2C

Many companies try to apply B2C approaches to GEO in B2B and get zero results. The difference is fundamental.

Long queries — the main feature of B2B

B2B queries to neural networks are typically significantly longer than B2C queries. The reason: the buyer immediately provides context — industry, company size, requirements, constraints.

Examples of real B2B queries:

• "Which project management system to choose for an IT company of 200 people with a distributed team? Need integration with Jira and Confluence, support for Agile and Waterfall"
• "Compare cloud PBX systems for a call center with 50 operators with call recording, analytics, and CRM integration. Budget up to 100,000 rubles per month"
• "Which BI systems are suitable for retail with 200+ stores? Need real-time visualization, demand forecasting, working with 1C data"

The more specific the query — the more accurate the AI recommendation. And the more important it is that your content contains answers to exactly these kinds of detailed questions.

What content works for B2B GEO

AI cites content that matches the user's query in depth and expertise. For B2B, this means a fundamentally different approach to content.

1. Case studies with measurable results

Not "we helped company X improve processes," but specifically:

• Industry and company size
• Initial problem with numbers (losses, inefficiency)
• What was implemented and how
• Result in measurable indicators (ROI, timelines, savings)
• Implementation timeline and team

AI actively cites case studies when answering queries like "implementation cases of [solution] in [industry]." The more case studies you have published with concrete data — the higher the probability of appearing in recommendations.

2. Comparative materials

Buyers ask AI to compare solutions. If you publish an honest comparison of your product with competitors yourself — AI uses this data. The key word: honest. Not "we are the best," but objective tables with pros and cons of each solution.

3. Expert research and white papers

Content that demonstrates deep industry knowledge:

• Market overviews with proprietary data
• Industry benchmarks
• Methodologies and frameworks
• Forecasts and trends backed by numbers

4. FAQ addressing objections and technical questions

Buyers ask AI specific questions: "What are the risks of migrating from [system A] to [system B]?", "How much does it cost to implement [solution] for 500 users?". Detailed FAQs on your website give AI ready-made answers to cite.

5. Technical documentation

For IT solutions, technical documentation is one of the key sources for AI. Well-structured documentation with API specifications, integration diagrams, and setup guides increases credibility in the eyes of neural networks.

How to build B2B brand trust through AI

In B2B, trust decides everything. Contracts are signed for years, and the cost of mistakes is high. AI builds trust based on several signals.

E-E-A-T in B2B context

Experience: Publish case studies from your practice, describe mistakes and lessons learned, show the "inside" of your processes. AI distinguishes abstract content from content written by a practitioner.

Expertise: List authors with real titles and relevant experience. An article by "Marketing Team" carries less weight than "Andrey Ivanov, CTO, 15 years in ERP system development."

Authoritativeness: Mentions in industry media, rankings, directories. For B2B in Russia these are: vc.ru, Habr, CNews, TAdviser, specialized Telegram channels.

Trustworthiness: Schema.org markup (Organization, Product, Review), SSL certificate, real contacts, company legal information on the website.

External citation sources for B2B

AI generates recommendations not only from your website but from all available sources. For B2B, these are critical:

• Industry rankings (TAdviser, CNews, Tagline for digital)
• Solution directories (Startpack, Market.CNews, Rusbase)
• Professional platforms (Habr, vc.ru, RB.ru)
• B2B review sites (Otzovik, iRecommend — for mass products; G2, Capterra — for IT)
• Industry Telegram channels and podcasts

The rule: the more authoritative sources mention your brand in the context of solving a specific problem, the higher the probability of getting into AI recommendations. Read more about the role of citation sources in the article Cited sources in AI.

Step-by-step GEO plan for a B2B company

Month 1: Audit and baseline

1. Define 15-20 key queries that buyers ask at each stage (research, shortlist, justification)
2. Check brand visibility for these queries across at least 5-6 neural networks
3. Record baseline metrics: Mention Rate, position, Share of Voice, tone
4. Analyze who AI recommends instead of you and why

At this stage, it is convenient to use GEO Scout — the platform covers 9 AI providers including Yandex with Alisa and shows all metrics in a unified dashboard. The free plan allows you to check 3 queries across 3 neural networks without a card.

Month 2-3: Content and structure

1. Publish 3-5 detailed case studies with concrete result figures
2. Create a comparison page with an objective side-by-side of solutions
3. Add a detailed FAQ addressing typical buyer questions (30-50 questions)
4. Implement Schema.org markup: Organization, Product, FAQPage, HowTo
5. Check the technical side: robots.txt for AI bots, page load speed, mobile version

Month 3-6: Scaling and iteration

1. Expand presence in external sources: publications on Habr, vc.ru, in industry media
2. Create white papers and research based on proprietary data
3. Monitor visibility changes weekly, adjust content plan based on data
4. Track AI traffic to the website — this is a direct indicator that neural networks are recommending you

To understand whether neural networks recommend your company when procurement committees make queries, you need systematic monitoring. The geoscout.pro platform tracks responses from 9 AI providers to corporate queries daily — you can see whether ChatGPT mentions your company when asked "which ERP to choose for manufacturing," how often Claude recommends a competitor, and what the Share of Voice dynamics look like week over week.

Industry specifics: where B2B GEO works best

Not all B2B segments are equally influenced by AI. Priority industries:

Each industry has its own queries and its own authority sources. But the basic principles of GEO remain the same: expert content, structured data, presence in external sources, regular monitoring.

Learn more about GEO for SaaS companies in the dedicated article.

Checklist: GEO for B2B

Content:

• 5+ case studies published with ROI figures and implementation details
• Comparison page exists (your product vs competitors)
• FAQ contains 30+ questions typical for buyers
• White paper or research with unique data exists
• Content covers all stages: research, comparison, justification
• Each article has an author with real expertise listed

Technical basics:

• Schema.org markup: Organization, Product, FAQPage
• robots.txt allows access to AI bots
• Pages load in under 3 seconds
• h2/h3 structure is logical and matches search queries

External presence:

• Company profile in 3+ industry directories
• 2+ publications on Habr or vc.ru in the last 6 months
• Company mentioned in industry rankings
• Reviews exist on relevant platforms

Monitoring:

• 15-20 target queries defined for monitoring
• Daily monitoring set up across 5+ AI providers
• Share of Voice tracked relative to competitors
• AI traffic separated as a distinct channel in analytics
• Content plan adjusted based on monitoring data monthly

Key takeaway

B2B procurement is increasingly going through neural networks. A committee of 5-11 people uses AI at every stage — from initial research to preparing justification. Companies that are not visible in ChatGPT, Claude, Perplexity, and Yandex with Alisa answers simply do not make the shortlist. The basic principles of working with AI visibility are in the article What is AI Brand Visibility.

The fundamental differences of B2B GEO from B2C: deep expert content instead of overviews, case studies with ROI instead of reviews, coverage of all funnel stages instead of a single touchpoint. Start with an audit of current visibility, build a data-driven content plan, and monitor results daily — the Command Center will help set priorities and track progress. In B2B GEO, the winner is not who started earlier, but who works more systematically.