Influence Orchestration in the GenAI Era: How LLMs Are Reshaping B2B Discovery

GenAI-powered discovery is now the first step in B2B buying. It's poised to become the largest real-time buyer intent channel in B2B marketing, and yet it's inadequately monitored and poorly optimized. To win in this new era, vendors must understand how ChatGPT and other LLMs rely on trusted, third-party signals to answer buyers' questions.

A collaborative report from Spotlight and Profound

September 2025

Executive Summary

The B2B buying journey has entered a new era – one shaped not by websites, SEO, or outbound campaigns, but by large language models (LLMs) like ChatGPT, Claude, Copilot, and Perplexity. Using these tools, buyers now ask open-ended questions like:

• “Who are the top vendors in this space?”
• “What’s the best alternative to my current solution?”
• “Which platforms are easiest to implement?”

In response, LLMs don’t return a list of links – they generate answers. Answers like:

• "According to analysts, the leaders in this space include ....."
• "Users who switch from X to Y often turn to....."
• "Platforms praised for ease of implementation include..."

These AI-generated responses are built from scouring the internet for trusted, credible information. That’s where your visibility is either won or lost.

Our research finds that there are 20 million prompts per day in ChatGPT related to B2B buying. This report analyzes how B2B buyers use GenAI tools to research, shortlist, and consider vendors. Drawing on 10,000 real prompts and 15,000 simulated prompts, we reverse engineer how LLMs surface vendor recommendations – to understand what sources the LLMs trust, which vendors receive mentions, and how sentiment shapes perception.

What we found is a fundamental shift in influence. Visibility is no longer determined by what marketers publish or what analysts report – it’s shaped by what GenAI trusts. That trust is earned through what we’re seeing as a new form of earned media: peer reviews, analyst-backed and vendor-promoted research, Wikipedia, Reddit, and publicly available third-party content that’s easily parsed and cited by LLMs.

Key Findings

• GenAI is now the first step in B2B buying. Buyers are using LLMs to form shortlists, compare vendors, and identify category leaders – long before they ever visit a website or talk to sales.
• Peer review platforms lead the way. G2, Capterra, TrustRadius, and Gartner Peer Insights are the most frequently cited sources across GenAI outputs.
• Ungated content is critical to GenAI visibility. GenAI tools prioritize structured, accessible, third-party content. Vendors that promote analyst recognition and review coverage publicly have a measurable advantage.
• Our Top 5 Vendor lists reveal who’s winning – and who’s missing. Rankings are based on GenAI citation frequency and sentiment across thousands of prompts in key B2B categories.

Why This Research Matters

GenAI isn’t just a new channel in the buyer journey – it has become the buyer journey.

In just two years, the adoption of GenAI by B2B buyers has accelerated dramatically:

• 2023: Only 21% of buyers were experimenting with GenAi for research or evaluation (McKinsey)
• 2024: That number rose to 32% (TrustRadius)
• 2025: Now, 89% of B2B buyers report using GenAI tools to support discovery and decision-making (RevSure/Forrester)

And this is not just behavioral change – it's behavioral change at scale.

According to GenAI usage estimates and our own analysis, we found that more than 20 million prompts per day on ChatGPT are tied to B2B buying decisions. Include activity across other models like Claude, Copilot, and Perplexity, and the volume exceeds 80 to 100 million prompts per day.

That makes GenAI one of the largest real-time buyer intent channels in B2B – and one of the least monitored or optimized by go-to-market teams.

Considerations:

• 90% of thier buying journey is already complete (6sense)
• 78% have defined their requirements (6sense)
• 81% have a preferred vendor in mind (AdWeek/Gartner)
• 84% of first-contact vendors win the deal (6sense)

So when a buyer finally reaches out to your sales rep, you’ve either made the shortlist – or you haven’t.

This is why visibility in GenAI matters.

Buyers are forming impressions and narrowing their options before your sales, marketing, or analyst engagement strategies ever have a chance to reach them. And those decisions are based on what LLMs synthesize from trusted sources – not gated PDFs or paywalled research.

The most frequently cited content in GenAI vendor discovery includes:

• Peer review platforms like G2, TrustRadius and Capterra
• Analyst-backed research across frims like Gartner, Forrester and IDC – specifically when vendor-promoted and publicly available
• Community insights from Reddit, Stack Overflow and other user forums
• Structured, open-source content from Wikipedia
• Trusted third-party media coverage and curated vendor summaries

If your brand isn’t present, accessible, and credible across these surfaces, GenAI will form its own opinion – and it may not include you.

This report helps GTM, Analyst Relations, and Product Marketing leaders understand how GenAI visibility is earned, and how to take control of the signals shaping early buyer decision-making.

About the Research

To better understand how GenAI tools are shaping vendor visibility and buyer perception, Spotlight and Profound partnered to conduct one of the most comprehensive analyses to date on LLM usage in B2B vendor discovery.

This research was designed not to study GenAI in theory — but to reverse-engineer how it actually influences real buyer behavior.

Scope of the Study

• Total Prompts Analyzed: 25,000 prompts across major LLM platforms
• 10,000 were collected from real B2B buyers — capturing how actual users interact with GenAI during discovery and evaluation
• 15,000 were generated by Profound using a controlled prompt framework to test:
• Which vendors are included
• What sources are cited
• What sentiment is used
• LLMs Evaluated:
• ChatGPT (OpenAI, GPT-4o)
• Claude (Anthropic)
• Microsoft Copilot (powered by OpenAI)
• Perplexity AI
• Google AI Overviews (select queries)
• Industries and Categories Covered: 6 B2B software categories:
• HR Technology
• Sales & Marketing Technology
• Data Security & Privacy
• Digital Commerce
• IT Services
• Digital Workplace

What We Measured

• Vendor Inclusion Frequency: How often specific vendors appeared in GenAI outputs across prompts and models
• Source Citations: Which domains and platforms were cited to support vendor mentions
• Sentiment Analysis: Whether vendors were described positively, neutrally, or negatively — and what patterns triggered those tones
• Model Comparisons: How different GenAI platforms respond to similar prompts — including variations in vendor inclusion, citation depth, and sentiment framing

Why This Approach Matters

Unlike traditional SEO or brand tracking studies, this research is designed for the GenAI era. It doesn't just ask who is getting visibility – it uncovers why they’re getting it, and from where.

By blending real-world buyer prompts with structured testing, this study reveals not only the vendors that dominate early GenAI discovery, but also the digital sources that drive that visibility, from review volume to analyst amplification to citation accessibility.

It also provides vendors, AR leaders, and marketers with model-by-model visibility insights, helping them better understand where they’re showing up, how often, and in what context – so they can act on what matters most.

The New Visibility Layer

The rules of visibility have changed.

In the GenAI-powered buying journey, visibility is no longer about what your brand says; it’s about what shows up when a buyer asks, “Who are the top vendors?”

When B2B buyers turn to GenAI tools to start their evaluation, LLMs don’t return ad placements or SEO results. They generate synthesized, trusted answers based on a specific set of signals – a visibility layer you don’t own, but you can influence.

This new visibility layer includes structured, accessible, and credible third-party inputs: review platforms, analyst research, open-source content, and community commentary. Inclusion isn’t something you buy – it’s something you earn through relevance, trust, and discoverability.

What GenAI Trusts

During our study, we ran 15,000 Profound-generated prompts across major LLMs to test how vendors were surfaced in early-stage discovery. We analyzed both the cited domains in responses and the unstructured mentions of third-party authorities. The top five most frequently cited domains were:

• g2.com – 7.5k citations
• gartner.com – 6.9k citations
• reddit.com – 6.2k citations
• capterra.com – 3k citations
• trustradius.com – 2.7k citations

While gartner.com ranks highly, the vast majority of those citations point to Gartner Peer Insights (GPI) rather than to traditional research like the Magic Quadrant. Peer review ecosystems dominate the citation landscape because they are public, structured, and trusted.

But citations alone don’t tell the full story.

We also analyzed the prompt responses themselves, uncovering LLMs’ use of publicly available research and perspectives from analyst firms. These weren’t links listed in the cited resources – they were language-based signals where the LLMs summarized analyst insights, referenced quadrant positions, or invoked ROI and benchmarking data. This reinforces the need for vendors to leverage analysts and customers alike to influence LLMs’ perception of them.

How GenAI Uses Analyst Research

LLMs referenced analyst firms in a variety of ways, beyond just citations. These mentions typically fell into recognizable usage patterns, including:

• Guiding shortlists by labeling vendors as Leaders, Visionaries, or Strong Performers based on well-known research frameworks (e.g., Magic Quadrant, Forrester Wave, MarketScape)
• Example excerpts: “Recognized as a Leader in the Gartner Magic Quadrant for …,” “Named a Leader in the Forrester Wave: …”
• Business value: Increases trust and reduces due diligence time by signaling third‑party validation.
• Referencing ROI data and business-case metrics from Forrester TEI or IDC studies
• Example excerpts: “Forrester TEI estimates a 200%+ ROI over three years,” “IDC study reports 50% lower operating costs and 90% less downtime”
• Business value: Strengthens budget approvals and accelerates buy-in with quantified outcomes.
• Drawing from analyst commentary to benchmark capabilities, identify differentiators, as well as assess risk and compliance
• Example excerpts: “Fastest-growing vendor in … according to IDC,” “Consult ISG/Constellation buyer guides for tailored picks,” “Strengths listed by Forrester include analytics depth and UX; watch-outs on pricing,” “Analyst assessments highlight compliance controls suitable for regulated industries”
• Business value: Helps buyers make faster, more confident decisions by validating vendor credibility, narrowing the evaluation scope, benchmarking capabilities against trusted criteria, and reducing perceived risk across security, compliance, and procurement teams.

These patterns reveal that analyst-backed research remains a powerful trust signal in GenAI discovery – but only when it's accessible, paraphrased, or promoted in visible channels.

The Trusted Source Stack

Across our prompt analysis, five source types consistently appeared in GenAI-generated answers – and are now shaping early-stage buyer perception:

Source TypeExamplesBehaviorCitation vs. InfluencePeer ReviewsG2, TrustRadius, Capterra, Gartner Peer InsightsMost frequently cited; foundational for early-stage vendor evaluationFrequently cited and directly feeds model outputsAnalyst ResearchGartner, Forrester, IDC (non-GPI)Referenced in-text; used to validate leadership, ROI, or differentiationHeavily paraphrased in response generation, but often not citedCommunity CommentaryReddit, Stack OverflowFrequently cited in developer-heavy and technical tool categoriesMix of direct citations and paraphrased peer feedbackOpen-Source KnowledgeWikipedia, comparison sitesUsed for neutral framing and product differentiationRegularly cited as trusted neutral framingPublic Vendor ContentBlogs, pages embedding analyst or customer proofCited when accessible, well-structured, and externally validatedOccasionally cited; more likely to influence when externally anchored

What GenAI Ignores

• Gated PDFs, paywalled research, or login-protected analyst content
• Generic vendor blogs lacking third-party validation
• Overly branded content that lacks external trust signals

If content can’t be crawled, parsed, or trusted – GenAI won’t use it.

The New Mandate for Visibility

For GTM, AR, and product marketing teams, GenAI has redefined the game:

• It’s not about being visible to your audience; it’s about being visible to their AI.
• You need to show up in the sources GenAI trusts most.
• Analyst mentions must be promoted in ways that LLMs can find and understand.
• Peer reviews and structured, third-party content are no longer optional – they’re foundational.

This visibility layer is already shaping shortlists. It’s time to influence it with the same level of intent you bring to campaigns, content strategy, sales enablement, and analyst relations.

Who’s Winning in GenAI Discovery

To understand which vendors GenAI tools are recommending most often — and why — we analyzed responses to 15,000 prompts designed to simulate how real B2B buyers conduct vendor discovery in the earliest stages of their journey.

These prompts included natural, intent-driven questions like:

“Who are the top vendors for [category]?”“What’s the best alternative to [vendor]?”“Which platforms are easiest to implement for [need]?”

The research focused on six key B2B software categories:

• HR Technology
• Sales & Marketing Technology
• Data Security & Privacy
• Digital Commerce
• IT Services
• Digital Workplace

Vendors were evaluated across two dimensions:

• LLM Visibility Score – How frequently they appeared in GenAI answers across ChatGPT, Claude, Copilot, Perplexity, and Google AI Overviews
• Sentiment Quality – Whether they were framed positively, neutrally, or negatively

We also tracked the top 5 most-cited domains per category to understand which sources GenAI relies on most when constructing vendor recommendations.

How LLMs Choose Their Sources

LLMs don’t “browse the web” like humans do. Instead, they synthesize responses from structured, high-trust content that is accessible, well-linked, and semantically rich.

The citation types that show up most frequently in GenAI responses include:

Source TypeWhy GenAI Uses ItPeer Review Platforms (e.g. G2, Capterra, TrustRadius, Gartner Peer Insights)Structured, trusted, and frequently updated — ideal for surfacing top-rated toolsCommunity Forums (e.g. Reddit, Stack Overflow)Treated as informal user review ecosystems, where buyers share experiences and ask for advice — often in vendor- or category-specific threads (e.g. r/ecommerce, r/shopify)Vendor-Owned Content (e.g. shopify.com, clickup.com)Often less frequently cited, but impactful when structured well — especially when it includes externally validated information (e.g., analyst quotes, awards, review scores, customer outcomes)Industry Publications (e.g. peoplemanagingpeople.com, theretailexec.com)Cited when they offer roundup-style comparisons or trusted third-party recommendationsComparison Blogs & Aggregators (e.g. zapier.com)Frequently used for side-by-side tool breakdowns — easy for LLMs to synthesize into shortlist answers

These domains represent the intersection of credibility and accessibility. If your brand doesn’t appear in these ecosystems — or isn’t described in a structured, indexable way — GenAI will likely pass you over when forming early-stage shortlists.

Who’s Winning – and Why – Across B2B Tech

GenAI isn’t surfacing vendors at random. These next six sections unpack the signals that drive visibility across high-intent categories like HR Tech, Security, and Commerce – including which vendors rise to the top, what third-party sources shape perception, and how sentiment influences inclusion. Each category offers a snapshot of what GenAI models are prioritizing – and what that tells us about how B2B buyers are thinking, prompting, and deciding.

What This Means for GTM Leaders

GenAI isn’t just reshaping vendor visibility – it’s restructuring the buyer journey from top to bottom. What used to be marketing-qualified leads and sales-driven education has become a self-guided, GenAI-curated shortlist built from trust signals that vendors don’t fully control – but can influence.

This shift has profound implications across the GTM engine: Analyst Relations, Product Marketing, Content Strategy, Customer Marketing and Sales.

Analyst Influence Still Matters – But Only If It’s Discoverable

Analyst insight remains one of the most powerful trust signals in B2B, but only when it’s findable and referenced in the places GenAI models draw from. While many analyst firms (most notably Gartner, IDC, and Forrester) keep the majority of their intellectual property behind paywalls, the LLMs prioritize analysts because of their trusted, independent expertise. The LLMs find analysts' perspectives via ungated sources, such as free reports, analyst blogs, media mentions, and vendor-licensed reprints of reports.

That means it’s not just Gartner, Forrester, and IDC shaping buyer perception. Smaller, independent, and category-specialized analyst firms – especially those publishing accessible buyer guides, strategic comparisons, or curated vendor lists – are also being surfaced in GenAI answers.

Crucially, the analyst firms that are publishing more openly, allowing vendors to cite their perspectives, or participating in peer review ecosystems (e.g., Gartner Peer Insights) are at a clear visibility advantage.

Analyst firms that continue to lock insights behind portals may retain institutional credibility – but they’re forfeiting discoverability in GenAI. Those that embrace open publishing, vendor amplification, and community relevance are far better positioned to influence future shortlists.

For vendors, the takeaway is clear: regardless of the firm’s size or brand recognition, only the analyst perspectives that are accessible and amplifiable will influence what buyers see first.

Assets That Feed LLM Responses Without Being Cited

GenAI often draws from trusted analyst research even when it’s not directly linked or cited. In these cases, insights are paraphrased or embedded in the LLM’s output. These invisible trust signals influence buyer perception just as much – if not more – than visible citations.

Peer Reviews Are Now a Core Visibility Channel

Across every category we studied, GenAI tools relied more on peer review platforms than on owned vendor content.

These platforms are favored by LLMs because they are:

• Structured and easy to parse
• Consistently refreshed with new user content
• Broadly trusted across categories

For GTM teams, this elevates the role of customer voice. Review coverage, sentiment, and accessibility are now core to vendor discoverability – not just buyer validation.

From Influence Input to Visibility Orchestration

In a traditional GTM motion, influence is often measured by activity-based metrics: briefings secured, analyst report mentions, campaign reach, thought leadership placements, review generation, and customer reference engagement.

In the GenAI era, those inputs only matter if they’re surfaced. Success now hinges on orchestrating how trust signals appear across the ecosystems GenAI pulls from. That means:

• Publishing and amplifying analyst insights in structured, public formats
• Promoting review milestones and customer stories on visible channels
• Ensuring product and comparison content is crawlable, linkable, and externally validated
• Monitoring prompt-level visibility and optimizing across models

This is the shift from credibility to discoverable credibility – and it changes what GTM teams must prioritize.

Visibility Requires Cross-Functional Ownership

GenAI visibility is not a function. It’s a layer – and it must be supported across teams:

FunctionNew Mandate in the GenAI EraAnalyst RelationsCite and promote analyst perspectives in GenAI-accessible thought leadershipProduct MarketingAlign category narratives with GenAI prompt patternsContent & SEOStructure pages and summaries for retrieval and citationCustomer MarketingSource, refresh, and elevate reviews where LLMs look firstSales EnablementReinforce buyer confidence – and actively address any negative or outdated narratives surfaced in GenAI responses – by ensuring the story buyers see aligns with the one sales delivers.

The Sales Rep’s Role Has Changed, Too

Buyers aren’t coming to sales to evaluate options – they’ve already made a decision.

GenAI has replaced the “education phase.” The sales rep is now a friction-remover, not a storyteller.

In this new reality, sellers are expected to:

• Validate what the buyer already believes
• Accelerate the transaction
• Minimize complexity and internal risk

This puts pressure on GTM teams to ensure that the GenAI narrative matches the sales experience – not only in tone, but in trust.

GenAI visibility is now the front line of buyer trust. It’s no longer a downstream effect of GTM execution – it’s a filter that determines who gets considered from the start.

Recommendations for GTM Leaders

This research confirms that GenAI-powered discovery isn’t a future-state shift; it’s already transforming how buyers build their shortlists and shape their perceptions.

B2B buyers are turning to GenAI to shortcut complexity and surface trusted recommendations – and GenAI is responding by amplifying vendors backed by accessible, credible, third-party validation.

To stay discoverable, trusted, and competitive, GTM leaders must treat GenAI not as a trend, but as a channel – one that requires active management, cross-functional ownership, and continuous optimization.

Treat GenAI Visibility Like a Strategic Channel

GenAI visibility should be approached the same way you manage a Voice of the Customer program or a partnership strategy:

• It reflects how the outside world sees you
• It surfaces the trust signals that shape consideration
• It requires alignment across marketing, product, sales, and advocacy teams

To operationalize GenAI visibility:

• Monitor prompt responses across top models (ChatGPT, Claude, Copilot, Perplexity)
• Track not just inclusion, but sentiment and source-level attribution
• Integrate insights into quarterly GTM planning and performance reviews

GenAI is already influencing buyer trust; the only question is whether it’s working for or against you.

Promote Analyst Recognition in LLM-Friendly Formats

Regardless of the firm – whether Gartner, a vertical specialist, or an trusted industry voice – analyst validation only influences GenAI when it’s:

• Publicly accessible (e.g., on blogs, podcast transcripts, product pages, or review platforms)
• Amplified in ecosystems GenAI trusts (e.g., Reddit, Wikipedia, G2)
• Structured and specific, not buried in PDFs or gated reports

Hidden credibility doesn’t translate to discoverability. If LLMs can’t find it, they won’t cite it.

Treat Peer Review Platforms as Strategic Assets

G2, TrustRadius, Capterra, and Gartner Peer Insights were among the most frequently cited domains across every category in this study. These platforms are more than social proof; they are front-line influence channels in the GenAI discovery layer.

You should:

• Source fresh reviews tied to evolving positioning and messaging
• Monitor tone and keywords that LLMs may latch onto
• Promote review wins across other indexable surfaces
• Embed review content into structured, linkable pages

Optimize Content for Prompt Behavior, Not Just SEO

Traditional SEO optimization focuses on high-volume keywords and rank positioning. GenAI prompt behavior is different – discovery has moved from search queries to conversations.

To appear in those buyer-to-AI conversations, your content should:

• Use list formats, comparative framing, and buyer-friendly language
• Include third-party citations (analyst quotes, review stats, awards)
• Be technically scannable (e.g., metadata, semantic structure, clean HTML)

“Top vendors for enterprise LMS,” “best alternatives to X,” and “easy to implement [category] tools” are the kinds of prompts shaping buyer opinion. If your content can’t support those questions, you’re likely invisible.

Align Sales to the Story Buyers Already Believe

GenAI often shapes the first narrative the buyer sees – and by the time they talk to sales, they’ve already started to trust it.

The rep’s job has shifted:

• From educating to validating
• From positioning to accelerating
• From explaining the landscape to removing friction

Sales enablement must account for:

• What GenAI is already saying about your brand
• How to address outdated, inaccurate, or negative LLM responses
• How to reinforce trusted signals from analysts, customers, and reviewers

Next Steps for GTM Teams

The way B2B buyers discover and evaluate vendors is changing – fast. GenAI tools like ChatGPT, Claude, Copilot, and Perplexity are becoming the new first stop for vendor research, shortlisting, and validation.

And what shows up in those GenAI responses is based on a very specific set of signals: trusted third-party validation, publicly available analyst insight, strong customer voice, and structured, comparison-ready content.

To stay visible and trusted in this new layer, GTM teams need to take intentional action now.

Remember to:

• Treat GenAI visibility like a strategic channel: Monitor it, measure it, and plan for it – just like you would with paid, organic, or partner-led channels.
• Benchmark your GenAI footprint: Understand where and how your brand is appearing across real prompts – and who’s showing up instead.
• Make your trust signals discoverable: Promote analyst mentions, customer reviews, and validation points across structured, public-facing formats.
• Align content and messaging to prompt behavior: Think like a buyer. Publish what they’re asking for – side-by-side comparisons, expert-backed rankings, implementation guidance.
• Enable sales to meet buyer expectations: Train reps to reinforce what buyers have already seen in GenAI responses – and to address what may be missing or misunderstood.

This isn’t a future trend. It’s a current advantage – for those who see it, measure it, and act on it.

About Spotlight + Profound

This report was produced through a partnership between Spotlight and Profound, two organizations working at the intersection of GenAI, reputation strategy, and modern influence orchestration.

Together, we help B2B companies understand how they’re being discovered, evaluated, and recommended – and give them the tools and insights to shape that perception.

Spotlight: Orchestrating Influence Across the Modern Buyer Journey

Spotlight helps B2B companies take control of how they’re discovered, evaluated, and recommended by orchestrating influence across trusted third-party channels – including analysts, customers, GenAI, and other influential industry voices.

Its platform and services enable GTM teams to:

• Monitor influence signals across GenAI, customer feedback, analyst coverage and market activity
• Optimize brand reputation by focusing efforts on the channels and voices that matter most
• Scale program efficiently through tools and workflows that activate third-party validation at greater volume and reach

From insight to activation, Spotlight helps companies make their credibility discoverable – and influence measurable.

Profound: Making GenAI Visibility Measurable

Profound is the first platform purpose-built to monitor brand visibility across large language models (LLMs). By analyzing real prompt responses across tools like ChatGPT, Claude, Copilot, and Perplexity, Profound helps B2B teams:

• Understand where and how they show up in GenAI
• Benchmark against competitors
• See which sources, narratives, and sentiment patterns are shaping visibility
• Identify what actions to take to improve performance

Profound provides the signal layer – Spotlight activates it.

Research Methodology

About the Research

This joint research by Spotlight and Profound is designed to examine how GenAI-powered tools influence B2B vendor discovery and perception. It explores the degree to which vendor visibility, sentiment, and sourcing vary across market categories and identifies which channels and signals show up most frequently in AI-generated recommendations.

We studied how LLMs respond to real buyer-style prompts – capturing the sources they cite, the vendors they mention, and the language they use to justify inclusion or recommend solutions.

This research represents a snapshot in time. It highlights how vendors can appear within GenAI answers and the signals that shape that visibility. This report is not an analysis of market leadership or vendor capabilities.

Visibility Scoring Approach

Visibility scores represent how often a vendor is named in GenAI-generated responses to curated prompts across each market category.

• Visibility Score: The percentage of answers a vendor appeared in across 30 custom prompts, averaged over a 7-day period and run across multiple answer engines.
• Visibility Rank: The vendor’s relative position in terms of share-of-voice across those prompts compared to others in the same category.

This scoring was conducted using Profound, which allows data analysts to programmatically generate prompts and analyze the content and citations of AI responses across multiple models.

Sentiment Analysis

This study includes sentiment analysis to help uncover how GenAI responses describe each vendor. Profound's methodology goes beyond simple keyword matching to analyze the full tone, structure, and context of each response, classifying sentiment as positive, negative, or neutral.

Sentiment scores are not a measure of product quality or user satisfaction. Instead, they reflect how vendors are talked about across GenAI platforms in response to specific prompts designed to reflect buyer evaluation behavior.

How Sentiment Is Measured

Profound applies advanced natural language processing (NLP) on a set of prompts to:

• Classify overall sentiment of AI-generated responses mentioning a vendor, based on the tone and context of the full response.
• Identify the themes driving that sentiment – for example, a positive mention may be tied to “scalability” or “ease of use,” while a negative mention might stem from “high cost” or “limited integrations.”

Important Considerations

• Prompt-based and market-specific: All sentiment data in this study was generated from custom prompts designed to reflect buyer research behavior in each of six B2B software categories. Prompts intentionally avoided vendor names and were tailored to the types of questions buyers ask when creating shortlists.
• Point-in-time snapshot: Sentiment findings represent a snapshot of model behavior in August 2025, and may differ from what an individual brand sees in their own Profound dashboard. This is especially true as GenAI responses evolve rapidly in response to model updates and changes on the public web.
• Relative and directional: Scores are best used to compare tone across vendors, spot messaging themes that influence perception, and identify recurring friction points (e.g., pricing, support, implementation). They are not a diagnostic or quantitative brand performance score.
• Used to inform GTM: This data is most valuable when used to inform GTM strategy. For example, to:
• Understand how messaging is being echoed (or misunderstood) in GenAI
• Spot inconsistencies between internal positioning and public language
• Prioritize areas for amplification, clarification, or response

Prompt Construction

To simulate real-world B2B evaluation behavior, we created 30 prompts per category, carefully constructed to reflect the types of discovery-phase questions buyers ask without including vendor names. Prompts were customized by category and aligned to common evaluation patterns seen in marketing, sales, IT, procurement, and C-suite roles.

Examples of prompt types:

• “What are the top platforms for [category function]?”
• “Which solution is best for [buyer goal]?”
• “What are the leading tools for [specific use case]?”
• “What are the top [category] solutions for mid-market teams?”

Prompts avoided vendor-led language and instead focused on use cases, outcomes, product categories, and role-based needs.

LLM Platforms Used

The research draws from responses generated by the following GenAI platforms:

• ChatGPT
• Microsoft Copilot
• Perplexity AI
• Google AI Overviews
• Google AI Mode
• Google Gemini
• Grok
• Meta AI

These tools were selected due to their growing usage in buyer research and vendor evaluation workflows. Where possible, prompt responses were captured from both free and pro versions of these models.

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