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.
G2 Research
AI Speeds Discovery, but Trust Still Decides
How 335 B2B professionals are navigating AI-assisted vendor research, revealing a buying process where AI accelerates early discovery and content triage but human verification and direct negotiation still govern final decisions.
52%
AI Leads Initial Vendor Discovery
75%
Verification Is The Default Workflow
85%
Triangulated Evidence Beats Single-Source Claims
76%
Human Negotiation Still Anchors Buying
Executive Summary
What We Found
AI is now firmly inside the buying workflow: 52% of respondents use it first for vendor discovery and shortlisting, and 72% use it to triage vendor content before deciding what merits deeper review. But this growing role does not translate into full trust. While 69% trust AI-generated vendor information as a useful input, that trust is conditional, and 75% default to cross-checking outputs across sources because buyers widely expect inaccuracies, generic summaries, or incomplete coverage.
This creates a clear trust ceiling on AI-led buying. When AI gets vendor information wrong, 72% respond with extra scrutiny and 28% may remove a vendor from consideration. As a result, persuasion shifts toward evidence that survives verification: 85% prefer triangulated proof beyond AI, and 76% prioritize commercial proof, pricing, and credibility over feature claims alone. AI can accelerate research, but it does not replace human judgment: 50% want human involvement by mid-funnel, and 76% expect direct human negotiation on pricing and terms.
75%
Verification Is the Default Workflow
Three quarters of respondents use a trust-but-verify approach across sources, confirming that AI outputs are rarely accepted as decision-ready on their own.
85%
Triangulated Evidence Beats Single-Source Claims
Buyers overwhelmingly prefer to validate vendors through a mix of third-party research, peer input, and trusted industry sources rather than reviews or vendor materials alone.
76%
Human Negotiation Still Anchors Buying
Even in AI-assisted workflows, pricing and terms remain a human-led moment, underscoring the limit of fully automated or self-service buying for consequential purchases.
Why this matters · For SaaS vendors
Why SaaS Software Vendors Should Care About This Study
B2B software buying has been restructured at the top of the funnel: 52% of buyers now start with AI for vendor discovery, 72% use it to triage vendor content before going deeper, and 85% require triangulated proof from multiple independent sources before deciding. Vendors who understand how AI is shaping — and limiting — this journey have six concrete opportunities to win.
OPPORTUNITY 01
Optimize for AI-Native Discovery Before Human Review Begins
52% of buyers use AI first for vendor discovery and shortlisting — before a human ever visits your website. AI is not supplementing the research process; it is the first filter. Vendors absent or poorly represented in AI-generated outputs are invisible at the most critical stage of the funnel.
Vendor Implication
Audit how your product appears in AI-generated vendor comparisons. Invest in structured, machine-readable content — clear positioning, validated use cases, and third-party evidence — that AI systems can accurately surface and summarize.
OPPORTUNITY 02
Make Content Scannable for AI Triage — Then Irresistible to Humans
72% of buyers use AI to triage vendor content and decide what merits deeper reading — but 64% still rely on original vendor sources to make final choices. Your content must first survive AI summarization and then reward the human who investigates further. Generic or feature-heavy content fails at both steps.
Vendor Implication
Structure content in two layers: a clear, stat-anchored summary for AI triage, followed by deeper proof — case studies, ROI data, and technical specifics — for the human reviewer. Prioritize substance over style at every level.
OPPORTUNITY 03
Win the Triangulation Test by Controlling Your Evidence Ecosystem
85% of buyers prefer triangulated proof from multiple independent sources over any single AI-generated summary, and 75% actively cross-check across sources by default. Buyers are not just evaluating your product — they are grading the consistency of everything they can find about you.
Vendor Implication
Build an evidence ecosystem, not just a website. Align your G2 reviews, analyst citations, customer case studies, and public pricing so they tell a consistent story. Inconsistency across sources is a shortlist killer in a triangulation-default market.
OPPORTUNITY 04
Treat AI Errors About Your Product as a Pipeline Risk
72% of buyers respond to AI errors with heightened scrutiny and cross-checking, and 28% say they may remove a vendor from consideration when AI gets their information wrong. Inaccurate AI portrayals — wrong pricing, outdated features, misattributed capabilities — are an active threat to your pipeline.
Vendor Implication
Monitor how AI tools describe your product and proactively correct inaccuracies through authoritative, frequently updated public sources. Buyers reward vendors whose information holds up under scrutiny — make accuracy a competitive advantage.
OPPORTUNITY 05
Lead With Commercial Proof, Not Feature Lists
76% of buyers prioritize commercial proof, pricing transparency, and vendor credibility over core product features when making decisions. Feature-led marketing now risks being filtered out by both AI summarization and human reviewers looking for the signals that actually drive purchase confidence.
Vendor Implication
Shift messaging from capabilities to outcomes, pricing clarity, and real-world results. Publish customer ROI data, transparent pricing tiers, and reference cases that give buyers the commercial anchors they need to make — and defend — a purchase decision.
OPPORTUNITY 06
Preserve the Human Touch Where Buyers Need It Most
50% of buyers need a human involved by the mid-funnel evaluation stage, and 76% expect direct human negotiation on pricing and terms. Fully automated or AI-mediated sales motions alienate the majority of buyers at exactly the moment they are most ready to commit.
Vendor Implication
Design your sales motion to hand off to a human at the point of serious evaluation — not just at contract close. Use AI to surface and qualify buyers faster, then show up as humans when it counts. Vendors who do both win the deals that others lose at the finish line.
Chapter 01
AI Has Entered the Buying Workflow
The market context is not whether AI is present in vendor research, but how deeply it is embedded. Buyers are already using AI early for discovery and triage, and AI is changing how vendor content is consumed by helping users scan faster before going deeper into original materials.
“In my opinion, we are somewhere between three and four. We are testing quite a lot, especially, on the marketing purposes and partly in operations, but it's not, fully implemented on all stage, and there is a lot of space to improve.”
— COO, Mid-Market FMCG
Listen
Key Finding
AI maturity splits between pilots and embedded workflow adoption
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
01
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
02
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
03
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.
Organizational AI Adoption and Maturity
High or workflow-embedded AI adoption45%
Moderate but still-evolving AI adoption42%
Early or experimental AI adoption13%
Listen
“I'm responsible for global enterprise platforms, and AI is embedded and is being deployed across multiple platforms ranging from hyperscalers to SaaS to other bespoke areas.”
Head of Technology and Cybersecurity Functions
when asked about High or workflow-embedded AI adoption
Listen
“I would like to place us at number four. We would love to be at number five. That's plan in progress. But, currently, we do have a lot of our processes and workflows managed, end to end by AI.”
Director, Large IT Services Consulting
when asked about High or workflow-embedded AI adoption
Key Finding
AI leads vendor discovery while humans validate final comparisons
Ai’s Role in Early-Stage Vendor Research
52%
of respondents used AI first for vendor discovery and shortlisting
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
01
AI leads early vendor research: 52% use AI first for vendor discovery and shortlisting, making it the dominant starting point in the evaluation process
02
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
03
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.
AI’s Role in Early-Stage Vendor Research
AI-first for vendor discovery and shortlisting
52%
AI for summarizing and comparing vendors
35%
Mostly manual or non-AI vendor discovery
13%
Listen
“Way more useful because instead of doing iterative Google searches, to find more information and to dive deeper and then having to go visit individual vendor websites. Chat both ChatGPT and Google Gemini could both create a comprehensive deep research into the software with multiple options, multiple vendors, and then create charts and even infographics of the various options that, came up as a result.”
Group Product Manager, a Fortune 500 large tech company
when asked about AI’s Role in Early-Stage Vendor Research
Listen
“I think it's was more useful because it aggregated all the information together, so I didn't have to sift through a bunch of different websites or go to different places. I could have it create a table for me and just load the information into that table and then export it and share it with leadership.”
Talent Acquisition Manager, None
when asked about AI’s Role in Early-Stage Vendor Research
Key Finding
AI filters vendor content early, but source validation still wins
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
01
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
02
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
03
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.
How AI Changes Consumption of Vendor Content
AI helps triage content but original sources still matter71%
AI reduces direct reading of vendor content22%
AI has not meaningfully changed content consumption7%
Listen
“I'm able to use quickly, some AI tools in order to take many times very long winded or long white papers or PDFs and give me a quick summary based on the content or context I'm looking for to determine if it's worthwhile to continue reading.”
Chief Information Security Officer, a Fortune 1,000 company
when asked about How AI Changes Consumption of Vendor Content
Listen
“I think it reduces the need to reach out to every individual vendor, but I think it just optimizes to make those engagements more productive and constructive because I can engage after I have done some vetting using AI.”
Group Product Manager, a Fortune 500 large tech company
when asked about How AI Changes Consumption of Vendor Content
Chapter 02
AI Outputs Are Useful but Structurally Untrusted
Although many respondents say they trust AI-generated vendor information at a baseline level, that trust is conditional because AI often produces inaccurate, generic, or incomplete outputs. This creates a built-in ceiling on how far AI alone can carry the evaluation process.
“"I will usually take what the AI gives me with a grain of salt. I won't trust it implicitly and will quite often go to do my own research to back up its claims."”
— Lead Software Architect, Healthcare
Listen
Key Finding
Most trust AI vendor summaries—but only with verification
Baseline Trust in AI-Generated Vendor Information
69%
trust AI-generated vendor information, but verify it
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
01
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
02
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
03
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.
Baseline Trust in AI-Generated Vendor Information
Trust but verify
69%
Generally high trust in AI summaries
16%
Low-trust, skeptical of AI outputs
15%
Listen
“But I verify critical claims, especially around security, compliance, integrations, and performance. With the primary sources like vendor documents, independent reviews, and product demos.”
Head of Business and Consulting Division
when asked about Trust but verify
Listen
“I haven't really faced a situation where it was conflicting. Like, usually, the AI summaries will ref like, some people say this or you know, people say this is good for these reasons. So it does do a good job at, like, summarizing the pros and cons and pulling the good and bad things that people have said. So I would tend to trust the AI.”
VP of People Experience and Talent Development, Cybersecurity Software
when asked about Generally high trust in AI summaries
Key Finding
Generic, Inaccurate AI Research Undermines Vendor Evaluation Confidence
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
01
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
02
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
03
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.
Perceived Failure Modes in AI Vendor Research
Inaccuracy, hallucinations, and outdated information69%
Generic, context-missing outputs19%
Biased or incomplete vendor coverage12%
Listen
“I have encountered in instances, yes, you know, where summaries were misleading, typically around overstating a vendor's capabilities or blending road map aspirations with current functionalities.”
Senior Director, Global Professional Services and Consulting firm
when asked about Perceived Failure Modes in AI Vendor Research
Listen
“I think for vendor research specifically, we need to understand what the vendor is providing within the context of what is beneficial for our company, and AI does not do that.”
Customer Insights and Strategy Manager, A FMCG retail company specializing in leather footwear and craft
when asked about Perceived Failure Modes in AI Vendor Research
Chapter 03
Triangulation Is the Default Coping Mechanism
In response to uncertain AI outputs, buyers adopt a trust-but-verify workflow. They routinely cross-check AI against multiple sources and prefer triangulated evidence over relying on reviews, vendor claims, or AI summaries alone.
“This is where the old expression comes in. Trust but verify. So, generally speaking, I have a good gauge of what vendors can and can't offer. And, I will verify essentially everything that the AI tool provides.”
— CIO
Listen
Key Finding
Trust-but-verify users triangulate across sources, not just vendors
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
01
Most users trust but verify across sources: 75% routinely cross-check outputs rather than relying on a single source
02
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
03
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.
Verification and Cross-Checking Behavior
Trust-but-verify across sources75%
Heavy cross-checking with multiple tools and people14%
Light follow-up checking11%
Listen
“The first thing I would do to verify is I would check the sources from the AI, and then I also do my own due diligence by going on the website, or checking third party reviews or even sometimes just talking to peers or other people in the industry to see if they have any know, personal experiences with using a certain vendor.”
Finance Manager
when asked about Trust-but-verify across sources
Listen
“I cross check it against the official vendor documentation, and, recent release notes, then validate it with real user reviews or reference customers. And a quick demo or trial to confirm it fit fits the use case or not.”
Transformation Consultant, Boutique Consulting
when asked about Heavy cross-checking with multiple tools and people
Key Finding
Trust Peaks When Buyers Triangulate Vendor Facts With Independent Validation
Preferred Evidence Sources Beyond AI
85%
of respondents who discussed evidence sources preferred a triangulated mix 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
01
Triangulated proof overwhelmingly wins: 85% preferred a mix of evidence sources beyond AI rather than relying only on reviews or vendor claims
02
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
03
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.
Preferred Evidence Sources Beyond AI
Prefers a triangulated mix of sources
85%
Prefers peer reviews and social proof
12%
Prefers official vendor sources
3%
Listen
“I basically this is my take. You know, the first thing that I do is triangulate. I will usually check third party sources such as analyst reports or peer reviews or, trusted industry platforms to validate, the claims.”
Senior Director, Global Professional Services and Consulting firm
when asked about Preferred Evidence Sources Beyond AI
Listen
“We trust, a variety of we try to triangulate a variety of different data sources to get to a recommendation and trust that rather than third party reviews or vendors own website.”
General Manager and Head of Business Product Engineering, Amazon Business
when asked about Preferred Evidence Sources Beyond AI
Chapter 04
Errors Change Shortlists and Raise the Bar for Proof
When AI gets vendor information wrong, the effect is not merely annoyance; it triggers rechecking and can alter which vendors remain under consideration. As a result, buyers lean harder on commercial proof, vendor credibility, and pricing realities rather than feature claims alone.
“We'll have to take that with a pinch of salt because of, since this is AI generated we all have to consider that it might not be truly accurate so for that, in terms of the results, always double check and go through and compare the output.”
— Cybersecurity Manager
Listen
Key Finding
AI Errors Prompt Rechecks More Often Than Shortlist Removal
How AI Errors Affect the Shortlist
72%
said AI errors mainly trigger caution and rechecking
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
01
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
02
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
03
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.
How AI Errors Affect the Shortlist
AI errors mainly trigger caution and rechecking
72%
AI errors can remove vendors from the shortlist
28%
Listen
“Initially, I would prompt the AI for high level questions, but not ask for sources. And that that's when I we had misleading information. But since then, I always ask the AI to give a tool summary and also share the links to the sources where they found information so that I can check and verify.”
Vice President of Revenue Operations
when asked about AI errors mainly trigger caution and rechecking
Listen
“We found that one particular player was I'm cost, but, not substantially better than other players. And at the same time, it had certain data security leak Issues have waited in the past by the AI. So we did not move ahead. Beyond the AI research. To even engage with that vendor.”
Vice President, Head of Digital Public Infrastructure Initiatives and Gen AI Initiatives, Banking
when asked about AI errors can remove vendors from the shortlist
Key Finding
Proof, pricing, and credibility outweigh features in decisions
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
01
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
02
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
03
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.
Decision Criteria Beyond Core Product Features
Prioritizes commercial proof, pricing, and vendor credibility77%
Prioritizes technical, security, and implementation fit13%
Prioritizes organizational fit and real-world outcomes10%
Listen
“Like, I when I do you know, software research, if they don't have pricing impacts information on their website, I tend to put them aside at you know, and and prioritize the ones that actually have it.”
Director of Product Marketing and Customer Marketing, cybersecurity company
when asked about Decision Criteria Beyond Core Product Features
Listen
“One thing I like to know about vendors, and they're not always up front with it, is how long they've been in business and who some of their major clients are to see if they really do align with what I'm looking for for my company or my uses.”
Talent Acquisition Manager, None
when asked about Decision Criteria Beyond Core Product Features
Chapter 05
Human Commercial Engagement Remains a Hard Requirement
Even as AI supports early research, buyers still expect humans to enter the process by mid-funnel, especially when pricing and terms are involved. Resistance to fully self-service or AI-negotiated buying highlights a strategic opening for experiences that blend AI efficiency with human reassurance and negotiation support.
“I need to speak to a human once we move beyond this discovery into validation, in particular where we are discussing use case free to implementation approach, commercial flexibility.”
— Senior Director, Global Professional Services Consulting
Listen
Key Finding
Human Support Becomes Essential by Mid-Funnel for Half
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
01
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
02
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
03
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.
When Human Interaction Becomes Necessary
Human needed throughout or by mid-funnel evaluation50%
Human needed mainly for pricing, negotiation, and contract close31%
Minimizes human contact until late stage19%
Listen
“I think for me, it's gonna be the context of my organization, my needs, my timeline. Understanding things about data protections, integrations with other software, the those kind of practicalities, I'd expect speak to a human sales rep.”
CFO
when asked about Human needed throughout or by mid-funnel evaluation
Listen
“Yeah. There are for sure are sometimes. I think, you can't do everything in it with AI. You will need to, if you're in a buying place, you will need to speak to a a sales rep to get exact pricing, payment terms and conditions, things such as that.”
Platform Architect Lead
when asked about Human needed mainly for pricing, negotiation, and contract close
Key Finding
Pricing Transparency Matters, but Buyers Still Expect Human Negotiation
Pricing Transparency and Negotiation Expectations
76%
of respondents discussing pricing transparency expected human negotiation for pricing and terms
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
01
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
02
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
03
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.
Pricing Transparency and Negotiation Expectations
Expects human negotiation for pricing and terms
76%
Values both transparent pricing and negotiated flexibility
14%
Wants upfront pricing transparency
10%
Listen
“Absolutely. There's definitely a point where you need to speak to human sales rep. Pricing and negotiation of terms, the organization we work for being a nonprofit and attached to larger organizations requires us to try to work everything through with our legal representation team.”
Product Manager, FamilySearch
when asked about Pricing Transparency and Negotiation Expectations
Listen
“when you are doing a price negotiation for a tool. Which involves hundreds of users or thousands of users, and it has some enterprise licensings and very complex licensing licensing criteria. When you enter those negotiations, you want to speak with a human.”
Program Manager, None
when asked about Pricing Transparency and Negotiation Expectations
Key Finding
Most buyers want humans involved in complex or final decisions
Openness to Fully Self-Service or AI-Negotiated Buying
45%
of respondents rejected 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
01
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
02
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
03
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.
Openness to Fully Self-Service or AI-Negotiated Buying
45%
Rejects fully self-service or AI-negotiated buying
Rejects fully self-service or AI-negotiated buying
45%
Broadly open to self-service or AI-negotiated buying
24%
Open only for simple or low-risk purchases
21%
Open only for simple/low-risk purchases
5%
Open only with safeguards or enabling conditions
2%
Open only for simple, low-risk purchases
2%
Open only for simple/low-risk/after research
0%
Open only for simple, low-risk, after research
0%
Listen
“If we are coming to the point where we're seeking to enter into any contractual arrangements, particularly through a procurement process, of any complexity, that's when speaking to the human is nonnegotiable.”
Director of Digital Transformation, Norfolk County Council
when asked about Openness to Fully Self-Service or AI-Negotiated Buying
Listen
“I would be open to a fully self-service AI negotiated buying experience, you know, for simpler lawyer risk purchases where requirements are standardized. Yes. Definitely.”
Senior Director, Global Professional Services and Consulting firm
when asked about Openness to Fully Self-Service or AI-Negotiated Buying
Strategic Patterns
Cross-Cutting Themes
PATTERN 01
The Trust Ceiling on AI-Led Buying
AI now leads early-stage vendor discovery and content triage, but conditional trust and widespread awareness of failure modes prevent buyers from relying on it end-to-end. Because users expect inaccuracies or incompleteness, they verify AI outputs rather than treat them as decision-ready inputs.
Implication
Winning in this environment requires designing AI experiences that are explicitly verifiable, source-linked, and transparent about confidence rather than pretending to replace diligence.
PATTERN 02
From AI Shortlisting to Evidence-Based Elimination
AI helps create initial shortlists, but when outputs contain errors, buyers respond by rechecking and may remove vendors from consideration. This shifts the burden of persuasion toward commercial proof, credibility, and other non-feature signals that can survive scrutiny across sources.
Implication
Vendors cannot rely on discoverability alone; they need consistent, corroborated evidence across channels so that verification reinforces rather than weakens their position.
PATTERN 03
AI Can Accelerate Research, but Not Replace Commercial Human Judgment
While AI is increasingly embedded in research workflows, buyers still want human involvement by mid-funnel and especially around pricing and terms. Openness to AI-assisted evaluation does not translate into comfort with fully self-service or AI-negotiated purchase experiences.
Implication
The strongest go-to-market models will pair AI-led efficiency in discovery with well-timed human engagement for evaluation, negotiation, and final confidence-building.
Quick Answers
Common Questions
Key Insight
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.
Question 02
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.
Question 03
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.
Question 04
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.
Question 05
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.
Strategic Recommendations
What This Means for You
01
Critical
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.
02
Critical
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.
03
High
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.
04
High
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.
Research Methodology
This research draws on 335 in-depth interviews with business professionals representing a wide mix of roles, industries, and company sizes.
Interviews ran 0 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.
G2 Research, June 2026
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.
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