# AI Speeds Discovery, But Trust Still Decides

**Author:** G2 Research  
**Published:** June 2026  
**Last updated:** September 23, 2026  
**Based on:** This research draws on 335 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.  
**Topics covered:** AI Research, Buyers, Go-to-market

How 335 B2B professionals are navigating AI-assisted vendor research: 52% now use AI first for vendor discovery and shortlisting, 72% use it to triage vendor content before deciding what merits deeper review, 85% prefer triangulated proof from multiple independent sources over any single AI output, and 76% still expect human negotiation for pricing and terms.

**Executive answer:** 52% ai leads initial vendor discovery: More than half of buyers now start vendor discovery and shortlisting with AI, showing that AI has moved from experiment to front-end research layer.

**On this page:** [Organizational AI Adoption and Maturity](#organizational-ai-adoption-and-maturity) · [AI’s Role in Early-Stage Vendor Research](#ai-s-role-in-early-stage-vendor-research) · [How AI Changes Consumption of Vendor Content](#how-ai-changes-consumption-of-vendor-content) · [Baseline Trust in AI-Generated Vendor Information](#baseline-trust-in-ai-generated-vendor-information) · [Perceived Failure Modes in AI Vendor Research](#perceived-failure-modes-in-ai-vendor-research) · [Verification and Cross-Checking Behavior](#verification-and-cross-checking-behavior) · [Preferred Evidence Sources Beyond AI](#preferred-evidence-sources-beyond-ai) · [How AI Errors Affect the Shortlist](#how-ai-errors-affect-the-shortlist) · [Decision Criteria Beyond Core Product Features](#decision-criteria-beyond-core-product-features) · [When Human Interaction Becomes Necessary](#when-human-interaction-becomes-necessary) · [Pricing Transparency and Negotiation Expectations](#pricing-transparency-and-negotiation-expectations) · [Openness to Fully Self-Service or AI-Negotiated Buying](#openness-to-fully-self-service-or-ai-negotiated-buying) · [Strategic takeaways](#strategic-takeaways) · [Frequently asked questions](#frequently-asked-questions) · [How this was produced](#how-this-was-produced)

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## Organizational AI Adoption and Maturity

AI adoption is split between organizations embedding it into day-to-day work and those still building momentum. Roughly half, 45%, described adoption as high or workflow-embedded, while 42% sit in a moderate but still-evolving stage. Only 13% remain in early or experimental adoption, pointing to a market that is advancing, but unevenly.

This polarization shows up in how organizations describe their progress. High-maturity respondents talk about AI running end-to-end processes across platforms, while moderate adopters are testing use cases without full implementation or strategic centrality. In practice, many firms are moving beyond exploration, but scaling from scattered pilots to enterprise-wide execution remains the key maturity gap.

**Key takeaways:**
- Adoption is clustering in the middle: 48% are mid-stage and partially embedded, making uneven implementation the dominant maturity pattern rather than either true experimentation or full strategic adoption
- Embedded AI remains a minority reality: only 39% describe adoption as advanced or strategic, while 41% say AI is broadly enabled and operationalized across workflows
- Pilots are not the main bottleneck anymore: just 13% remain in early or experimental stages and only 1% report isolated or siloed use cases, showing most organizations have moved beyond pure testing into selective deployment

**Strategic implication:** Segment offerings and go-to-market by maturity tier: package fast-start governance, integration, and change-management support for the 48% in partial adoption, while reserving enterprise pricing, workflow automation, and operating-model transformation services for the 39% with advanced deployment. Shift messaging away from pilot value and toward standardization, adoption consistency, and measurable workflow outcomes, since only a small minority remain in experimentation and most organizations now need scale, orchestration, and cross-functional enablement.

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## AI’s Role in Early-Stage Vendor Research

AI now leads the earliest phase of vendor research, with 52% of respondents using it first for discovery and shortlisting. Another 35% use AI mainly to summarize and compare options, meaning nearly nine in ten bring AI into the process before final selection. Only 13% still rely primarily on manual or non-AI approaches.

AI is strongest where speed and synthesis matter most: landscape scans, early filtering, and side-by-side comparisons. Respondents describe using it to replace iterative searching, aggregate vendor information, and create structured outputs for sharing. Even so, a smaller group still begins with internal repositories, consultants, or traditional research sources rather than AI tools.

**Key takeaways:**
- AI leads early vendor research: 52% use AI first for vendor discovery and shortlisting, making it the dominant starting point in the evaluation process
- Discovery outranks fit screening: 30% primarily use AI for vendor discovery versus just 5% for shortlisting and fit screening, showing stronger value at the top of the funnel
- Humans still validate the final comparison: 78% use AI as a support layer with manual validation for summarizing, comparison, and research, while only 2% primarily rely on AI to compare and prioritize vendors

**Strategic implication:** Shift budget and messaging toward AI-discoverable top-of-funnel visibility: structure product content, comparison pages, pricing, integrations, and category metadata so copilots can surface and shortlist your offering accurately. Equip buyers with validation-ready proof for the human checkpoint—clear differentiation, customer evidence, implementation details, and transparent pricing. Prioritize AI-friendly discoverability over late-stage persuasion, because selection begins in AI workflows but closes only after manual comparison and verification.

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## How AI Changes Consumption of Vendor Content

AI is primarily changing how buyers screen vendor content, not whether original materials matter. Nearly three quarters, 72%, use AI to summarize long papers, extract key claims, and decide what deserves deeper review. By comparison, about one in five, 22%, say AI is reducing direct reading of vendor materials, while only 7% report little to no change.

In practice, AI is becoming a front-end filter that shortens time spent on long-form content and improves vendor conversations after initial vetting. The key nuance is that source documents still anchor trust and final evaluation for most respondents, even as a smaller but meaningful group shifts more heavily toward summaries instead of reading blogs, white papers, and webpages directly.

**Key takeaways:**
- AI now gates first-pass vendor reading: 80% use AI summaries to triage before deciding what to read directly, while only 7% report a strong reduction in direct content reading and 13% say AI has little or no impact
- Original vendor sources still decide final choices: 64% say AI helps prioritize content but they still return to vendor materials selectively, and another 29% say vendor content comes back later for validation and selection
- End-to-end AI replacement remains rare: just 4% keep direct vendor content central throughout, showing most buyers use AI early but rely on original sources before making decisions

**Strategic implication:** Design vendor content for two moments: AI ingestion and human validation. Publish concise, structured, citation-rich assets that summarize value, pricing, differentiators, and proof points so AI can accurately surface them in early triage, then reinforce selection with deeper original materials—technical documentation, case studies, ROI evidence, and transparent pricing—for late-stage verification. Shift messaging from broad awareness content to comparison-ready, source-verifiable claims, and instrument content to track both AI referral influence and validation-stage engagement.

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## Baseline Trust in AI-Generated Vendor Information

Trust in AI-generated vendor information is strong but clearly conditional. Roughly seven in ten respondents, 69%, trust AI summaries as a useful input, yet still verify the output before acting on it. Smaller groups sit at either end of the spectrum: 15% are openly skeptical, while 16% express generally high trust in AI-generated summaries.

Verification is the dominant operating model across the market. Skeptical buyers often cite inaccuracies and rely on personal expertise or independent research, while the majority use AI as a starting point and confirm critical claims through documentation, reviews, references, and demos. This makes AI most effective for early vendor screening, not as a standalone source of truth for purchase decisions.

**Key takeaways:**
- Trust is widespread but rarely blind: 69% trust AI-generated vendor information overall, yet 83% say they verify summaries before relying on them while only 10% use them as a starting point and 2% report low trust and limited use
- Verification is driven by error and bias concerns: 55% say trust falls when they see errors or potential bias, making accuracy and neutrality the biggest factors shaping confidence in AI vendor summaries
- Credibility depends on visible evidence: 35% say trust depends on source visibility or grounding, while only 10% express relatively high trust when the supporting evidence appears credible

**Strategic implication:** Build AI vendor summaries as evidence-first decision aids, not standalone claims: surface citations, source dates, confidence signals, and clear links to original materials in every summary. Position the product as “accelerated research with built-in verification,” and price premium tiers around auditability, source transparency, and bias controls rather than automation alone. Equip sales and marketing with proof workflows, accuracy benchmarks, and comparison views that help buyers validate outputs quickly and trust them enough to act.

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## Perceived Failure Modes in AI Vendor Research

AI vendor research is widely seen as unreliable, with 93% of respondents citing failure modes. Nearly seven in ten pointed to inaccuracies, hallucinations, or outdated information, while about one in five described outputs as too generic. A smaller but important 12% flagged incomplete or biased vendor coverage.

The biggest concern is not just wrong answers, but misleading ones: tools often blur current capabilities with future road maps, miss company-specific requirements, and favor vendors with stronger public visibility. In practice, that means teams still validate findings manually, especially when evaluating newer or less visible providers against their own business context.

**Key takeaways:**
- Trust in AI vendor research is fragile: 72% reported frequent accuracy or source-trust concerns, and another 3% described severe distrust driven by hallucinations, bias, or unverifiable claims
- Generic results dominate buyer experiences: 86% said AI vendor research was too generic or incomplete for their buying context, while only 7% found results mostly relevant with just minor gaps
- Failure modes are widespread, not isolated: 93% discussed shortcomings in AI vendor research, showing that inaccurate, incomplete, and context-mismatched outputs are a mainstream barrier to confident vendor evaluation

**Strategic implication:** Shift AI vendor research from a self-serve answer engine to a verified decision-support workflow: require source citation, recency checks, buyer-context inputs, and human review for shortlist-critical outputs. Position the offering around defensible evaluation quality rather than speed alone, and price premium tiers on validation, customization, and analyst oversight. Messaging should emphasize auditability, context fit, and reduced shortlist risk, with generic research reserved for early-stage discovery only.

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## Verification and Cross-Checking Behavior

Users routinely verify AI outputs by checking other sources rather than accepting recommendations at face value. Three quarters took a trust-but-verify approach across sources, making this the dominant behavior by a wide margin. Smaller groups either conducted intensive triangulation, 14%, or did only light follow-up checking, 11%.

Most users appear comfortable using AI as a starting point, but not as a final authority. The dominant pattern is pragmatic validation through vendor documentation, third-party reviews, and peer input, while a smaller segment goes further with structured triangulation across tools and people. This suggests AI is valuable for speed and direction, but credibility still depends on external confirmation.

**Key takeaways:**
- Most users trust but verify across sources: 75% routinely cross-check outputs rather than relying on a single source
- Official sources are validation tools, not the only answer: 58% use vendor or official sources alongside other checks, while just 4% go to the source first alone
- Triangulation is typically broad, not shallow: 50% verify against several external sources and another 27% extend checks to people, tools, or testing, versus only 19% who do just one extra check

**Strategic implication:** Design products, pricing, and go-to-market around verification workflows, not single-source trust. Package enterprise offers with citation trails, exportable evidence, test sandboxes, and integrations to external databases, search, and internal knowledge tools so users can triangulate quickly. Position official documentation as one proof point within a broader validation ecosystem, and message speed-to-confidence rather than authority alone. Enable team review features and expert support, since many users extend verification to people, tools, and live testing.

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## Preferred Evidence Sources Beyond AI

Triangulated evidence is the clear standard, with 85% of respondents preferring to validate AI outputs through a mix of third party research, peer input, and trusted industry sources. By comparison, only 12% leaned primarily on peer reviews and social proof, while just 3% favored official vendor information as their main confirmation source.

This pattern points to a risk management mindset: decision-makers want multiple, independent signals rather than a single source of truth. Peer input still matters, but usually as one layer in a broader validation process; relying mainly on LinkedIn, reviews, or vendor claims remains the exception, not the norm.

**Key takeaways:**
- Triangulated proof overwhelmingly wins: 85% preferred a mix of evidence sources beyond AI rather than relying only on reviews or vendor claims
- Vendor content works best as a factual anchor: 52% used official vendor sources as a validation or depth layer, while only 22% treated them as the primary source of truth and 20% gave them low trust or secondary status
- Independent validation is the decisive trust filter: 39% saw human and third-party sources as the primary proof layer and 58% used them as a corroborating reality check, with just 2% expressing skepticism or low trust

**Strategic implication:** Build every buying journey around evidence triangulation: use vendor materials to deliver precise product facts, pricing logic, security details, and implementation depth, then pair each claim with independent proof such as customer references, analyst validation, expert reviews, and practitioner communities. Shift messaging from brand assertion to claim-plus-verification, equip sales with third-party substantiation by funnel stage, and package pricing and ROI conversations with external benchmarks so trust is earned through corroboration, not promotion.

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## How AI Errors Affect the Shortlist

AI errors usually slow decisions rather than stop them outright. Nearly three quarters, 72%, said mistakes primarily trigger extra scrutiny, prompting teams to double check outputs, verify sources, and do additional research before moving vendors forward. By contrast, 28% said AI-driven findings can directly remove a vendor from consideration.

In practice, AI is acting as an early warning layer in the shortlist process. Most teams treat questionable outputs with caution and validation, but a meaningful minority use negative AI findings, especially around credibility, compliance, or security concerns, as grounds to rule vendors out before deeper engagement even begins.

**Key takeaways:**
- AI errors prompt rechecks, not immediate removal: 72% said errors mainly trigger caution and rechecking, while 87% create extra verification and only 20% lead to direct exclusion or abandoning the search
- Caution overwhelmingly outweighs shortlist impact: 87% said AI errors create caution and extra verification, versus just 8% who saw no direct shortlist impact and 4% who said confidence drops but the vendor stays in play
- Shortlist damage happens mostly through softer filtering: 19% said vendors are indirectly filtered due to poor fit or missing information and 11% said they are deprioritized pending validation, compared with 20% who are directly excluded

**Strategic implication:** Prioritize error-proofing and verifiable outputs in early buyer touchpoints, because AI mistakes rarely remove vendors outright but reliably trigger deeper scrutiny. Equip sales and product teams with source-backed claims, transparent methodologies, audit trails, and fast human validation to preserve shortlist momentum. Position premium tiers around accuracy assurance, onboarding support, and trust safeguards, while tightening messaging to emphasize reliability, completeness, and low-friction verification rather than broad AI capability alone.

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## Decision Criteria Beyond Core Product Features

Commercial proof, pricing, and vendor credibility dominate decision criteria beyond core product features, cited by 76% of respondents. By contrast, only 13% prioritized technical, security, and implementation fit, while just 10% focused on organizational fit and real-world outcomes, showing that buyers often screen vendors first on business viability and commercial transparency.

Commercial diligence centers on clear pricing, proof of market traction, and confidence that a vendor can deliver as promised. Technical and organizational considerations still matter, but they appear secondary in this theme, surfacing more as validation steps once a vendor has cleared credibility and value-for-money hurdles.

**Key takeaways:**
- Credibility outweighs feature-only evaluation: 96% consider vendor trust, culture, or relationship fit in decisions, while just 2% are primarily product-led with limited vendor-fit concern
- Proof is a near-universal buying requirement: 95% require operational fit, evidence, or risk validation beyond core features, including 43% demanding extensive proof of success
- Buyers cluster in the middle, not the extremes: 54% show moderate trust or culture consideration and 52% conduct moderate fit and evidence validation, signaling decisions are shaped by commercial confidence as much as product capability

**Strategic implication:** Lead with commercial confidence, not feature depth: equip sales and marketing with quantified ROI proof, customer outcomes, implementation references, and risk-mitigation plans early in the buying journey. Position pricing around value realization and deployment certainty, with clear packaging, business-case tools, and defensible cost narratives. Prioritize trust-building motions—executive access, responsive service models, and cultural fit signals—because vendor credibility and operational readiness now determine conversion as much as product capability.

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## When Human Interaction Becomes Necessary

Human support enters well before the final buying step for roughly half of buyers, with 50% saying they need a person throughout the journey or by mid-funnel evaluation. By comparison, 31% mainly want human involvement for pricing, negotiation, and contract close, while only 19% prefer to delay contact until the final stage.

Mid-funnel human interaction is driven less by basic product discovery and more by validation: buyers want tailored answers on security, integrations, implementation, and organizational fit. The split suggests self-service can carry early education, but enterprise sales still depend on people when risk, stakeholder alignment, and commercial flexibility become decision-critical.

**Key takeaways:**
- Half need humans by mid-funnel: 50% of buyers require human support throughout or by the evaluation stage, with 46% needing it after initial research and 31% needing it throughout evaluation for confidence and fit
- Sales still closes the deal for most: 56% mainly need human involvement at pricing, negotiation, or contract close, showing commercial conversations remain the biggest trigger for live support
- Human help spikes with complexity: 21% only need people for complex, high-stakes, or customized deals, while 14% view human contact as avoidable friction unless absolutely necessary

**Strategic implication:** Deploy a segmented support model that keeps early research self-serve, then introduces human guidance by evaluation for fit validation and confidence-building, with named experts available for shortlist reviews, ROI framing, and technical scoping. Concentrate senior sales capacity on pricing, negotiation, and complex customized deals, while reducing unnecessary outreach for low-touch buyers. Align messaging to signal “expert access when needed,” and package advisory support, implementation guidance, or solution design into higher-value commercial tiers.

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## Pricing Transparency and Negotiation Expectations

Pricing conversations are driven far more by the expectation of human negotiation than by a desire for fully transparent list prices. More than three quarters, 76%, expect direct sales involvement for pricing and terms, while only 10% primarily want upfront pricing transparency. Another 14% want both visibility and room to negotiate.

Enterprise buying context explains that preference. Respondents describe pricing as intertwined with legal review, service levels, licensing complexity, and contractual flexibility, making human involvement essential as deals mature. At the same time, a smaller but meaningful group still wants early pricing signals or ranges to screen vendors quickly before investing in deeper sales conversations.

**Key takeaways:**
- Negotiation outweighs transparency in pricing talks: 76% discussing pricing transparency still expected human negotiation for pricing and terms, showing that visibility alone does not remove the need for sales involvement
- Opaque pricing drives buyers to humans: 39% said hidden or complex pricing forces human contact, compared with just 8% who expect or require fully upfront pricing
- Self-serve pricing remains the exception: 74% said human negotiation and contract review are essential, while only 2% believed simple fixed pricing can be handled entirely through self-serve

**Strategic implication:** Publish clearer baseline pricing, package ranges, and pricing logic online, then route buyers quickly into sales-led negotiation for terms, exceptions, and contract review. Design pricing pages to reduce avoidable friction—not to replace reps—using richer cost breakdowns, comparison tools, and AI-guided estimation to qualify interest before human engagement. Equip sales with transparent pricing narratives, approval guardrails, and negotiation frameworks so conversations move faster while still supporting tailored commercial outcomes.

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## Openness to Fully Self-Service or AI-Negotiated Buying

Roughly half of respondents, 45%, rejected fully self-service or AI-negotiated buying, making resistance the largest single stance in the sample. By contrast, 24% were broadly open to AI-led buying, while 21% would accept it only for simple or low-risk purchases, showing openness remains conditional for many buyers.

Human involvement remains most important when purchases become complex, high value, or contract-heavy. Even among those open to automation, many draw a clear line around standardized, low-risk transactions, suggesting AI-led buying is more viable for routine procurement than for negotiations requiring nuance, accountability, and relationship management.

**Key takeaways:**
- Humans remain central to key buying moments: 68% want human involvement for complex, high-value, or final decisions, and another 5% require a human in most or all cases
- Fully autonomous buying lacks broad support: 45% reject fully self-service or AI-negotiated buying, while 28% say AI can support the process but not replace human interaction
- Self-service works mainly in low-risk scenarios: 49% are open only for simple, low-risk, or post-research purchases, while just 10% are broadly open to self-service and 36% need safeguards or other enabling conditions

**Strategic implication:** Design buying journeys with human escalation built into every complex, high-value, and final-stage decision, while reserving self-service for simple, low-risk transactions. Price and package offerings to support this split: streamlined digital paths for routine purchases and advisor-led options for larger or higher-stakes deals. Position AI as an assistive tool that improves speed, research, and comparison—not as a substitute for human guidance—and reinforce trust with clear safeguards, transparency, and easy access to experts.

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## Strategic takeaways

### Design For Verification, Not Blind Trust

Make AI-facing and buyer-facing content source-linked, current, and easy to validate across channels. With 69% trusting AI only conditionally and 75% cross-checking outputs, vendors should optimize for auditability rather than polished but unsupported summaries.

### Strengthen Commercial Proof Across The Funnel

Invest in consistent pricing narratives, customer proof, market credibility, and implementation evidence that can survive triangulation. Since 76% prioritize commercial proof beyond features and 28% say AI-driven errors can eliminate vendors, credibility gaps are now shortlist risks.

### Win The AI Discovery Layer With Structured Content

Publish clear, differentiated, machine-readable content that helps AI tools accurately summarize capabilities, use cases, and fit. Because 52% start with AI for discovery and 72% use it to triage content, vendors need content built for both algorithms and human evaluators.

### Introduce Human Support Earlier In Evaluation

Do not wait for contract close to involve people. With 50% wanting human support throughout or by mid-funnel and 76% expecting human negotiation on pricing and terms, the best model blends AI efficiency early with human validation during evaluation and commercial discussions.


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## Frequently asked questions

**How embedded is AI in vendor research today?** AI is already deeply embedded in early-stage research. 52% of respondents use AI first for vendor discovery and shortlisting, while another 35% use it mainly to summarize and compare options. That means nearly nine in ten bring AI into the process before final selection.

**Do buyers actually trust AI-generated vendor information?** Yes, but only conditionally. 69% said they trust AI-generated vendor information as a useful input, yet still verify it before acting. Only 16% expressed generally high trust, while 15% were openly skeptical.

**What do buyers do when they suspect AI outputs are wrong?** They verify rather than proceed blindly. 75% took a trust-but-verify approach across sources, and 85% preferred triangulated evidence beyond AI, typically combining third-party research, peer input, and trusted industry sources.

**Can AI mistakes really affect which vendors make the shortlist?** Yes. While 72% said AI errors mainly trigger extra caution and rechecking, 28% said AI-driven findings can directly remove a vendor from consideration. In other words, bad or inconsistent information can create real commercial consequences.

**How far can AI take the buying process before humans are needed?** AI is strongest in discovery, summarization, and early comparison, but buyers still want people involved as decisions become more commercial and specific. 50% need human support throughout or by mid-funnel, 45% reject fully self-service or AI-negotiated buying, and 76% expect human negotiation for pricing and terms.


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## Research Methodology

G2 is the world's largest and most trusted software marketplace, helping 90 million people every year make smarter software decisions based on authentic peer reviews.

This research draws on 335 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.

Interviews ran up to 29 minutes and covered AI’s role in early-stage vendor research, baseline trust in AI-generated vendor information, verification and cross-checking behavior, and preferred evidence sources beyond AI. The conversational format allowed respondents to discuss their actual practices rather than select from preset options, surfacing nuance that closed-ended surveys typically miss.

Respondents included business professionals across technology, financial services, healthcare, manufacturing, and retail. All participants were selected for their direct experience with vendor research and evaluation processes. Company sizes ranged from small businesses to large enterprises.

The analysis of 335 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure analysis quality. Each transcript was independently reviewed by G2's AI Custom Research team to inform narrative, context, and clarity.

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*Source: G2 Research — "AI Speeds Discovery, But Trust Still Decides" (June 2026). Based on G2 first-party research. Markdown edition structured for answer-engine optimization (AEO).*