Opening thesis
We are in the middle of a structural shift in how B2B technology is bought and sold. Artificial intelligence has become a primary research tool for buyers — not a future capability, but a current baseline. And yet, at the same moment that information has become abundant and AI-generated summaries are available for every vendor category, trust in the sellers that buyers actually encounter has fallen to 45%. Information is everywhere. Verified, attributed, trustworthy human expertise is scarce. That scarcity is the commercial opportunity that defines this moment for channel professionals.
Who this is for
This article is written for three audiences. Channel sellers, consultants, and advisors in IT and cyber security who want to understand why structuring and publishing their expertise has become a competitive priority rather than a nice-to-have. Vendor-side channel and marketing teams who need to understand why their channel partner knowledge is an underutilised asset in buyer research. And buyers and procurement teams who want to understand how to identify and access verified channel expertise rather than relying solely on vendor-produced content and AI-generated summaries.
The core problem: the Impact Gap
A global study by LinkedIn Sales Solutions, conducted with Ipsos across more than 900 B2B buyers in seven markets, identifies what it calls the Impact Gap — the widening chasm between what sales organisations are expected to deliver and what their existing systems actually produce. Performance expectations compound every year. The tools deployed to meet those expectations keep falling short. Volume goes up. Trust does not follow.
The root cause the research identifies is that most sales tools and motions optimise for activity — more outreach, more sequences, more AI-generated personalisation at scale — rather than for the quality of trust signals that actually move buyers. The sellers closing the gap are not the ones doing more. They are the ones being more precise, more credible, and more relevant at the moments that matter.
The sellers closing the Impact Gap are not the ones doing more. They are the ones being more precise, more credible, and more relevant at exactly the moments that matter to buyers.
Key findings from the research
The AI takeover
94% of B2B buyers now use AI at every stage of their purchase process. They use it to analyse proposals, compare alternatives, identify risks, and fact-check vendor claims. AI now ranks among the top two information sources buyers consult at every stage of the buying journey. Buyers form shortlists of two to three vendors before any human contact takes place. The first impression no longer belongs to the seller — it belongs to what AI can find about the seller before the first conversation.
The human advantage
Despite this, human seller engagement is still valued — but where it matters most has shifted. 88% of buyers say seller engagement is most valuable at the vendor comparison and pilot testing stages. This is the mid-funnel moment where buyers need confidence to defend their investment internally and validate that their chosen solution delivers measurable outcomes to stakeholders. Generic content cannot answer those questions. Verified, experienced human expertise can.
The trust imperative
86% of buyers say that expertise drives trust. Yet only 45% describe the sellers they actually encounter as trustworthy. That gap — between what buyers say would build trust and what they actually experience — is the single largest commercial opportunity in B2B sales today. The sellers who close it win larger deals, faster, and with higher retention.
Evidence: what separates top performers
The research identifies four repeatable behaviours that distinguish sellers who exceed quota from those who miss it.
- Better signals: Top performers track 18% more signals than quota-missers and are 3x more likely to re-rank target accounts as new signals emerge. They act on real intelligence — expansion announcements, regulatory changes, product launches — rather than static demographic lists.
- Multithreading: Quota-exceeding sellers generate 20% more meetings from warm introductions. Top performers are 3x more likely to be connected to eight or more stakeholders at a target account. Existing relationships are the most underused competitive asset in B2B sales.
- Speed as a trust signal: Top sellers are 6x more likely to respond to a high-relevance buying signal within hours. Responsiveness signals attentiveness — the buyer feels specifically noticed rather than generically targeted. Buyers who have changed jobs in the last 90 days are 40% more likely to accept an outreach attempt.
- Personalisation over scale: Three-quarters of sellers agree that high-volume untargeted outreach erodes buyer trust. Top performers use AI to interpret signals and sharpen judgment, not to automate spray-and-pray at greater scale. They measure AI impact by meetings booked and deals won — not by volume sent.
The channel-specific implication
The general findings become sharper when applied to IT and cyber security channel professionals specifically. The channel is one of the most information-dense but least structured parts of B2B technology. Sellers who have been doing deals in microsegmentation, Zero Trust, SASE, cloud security, or endpoint security for five or more years carry knowledge that is genuinely irreplaceable — specific vendor behaviours, real buyer objections, route-to-market patterns, deal mechanics that only come from having been in the room.
That knowledge currently lives in heads, CRM notes, and personal networks. It is invisible to buyers conducting AI-assisted research. It is invisible to vendor teams trying to understand channel coverage. It is invisible to buyer-side agents compiling shortlists. The buyers who are right now using AI to research microsegmentation vendors are not finding that channel expertise. They are finding product datasheets, analyst reports, and vendor-produced content.
The first channel professionals to structure and publish verified, attributed expertise will disproportionately shape what AI systems find, cite, and surface when buyers research their categories. This is not a future state. It is happening now.
Lessons learned
- Publishing generic content is not sufficient. AI already has access to all publicly available generic content. What creates differentiation is specific, attributed, verifiable expertise tied to real deal experience, named technology categories, and identifiable account patterns.
- Volume of outreach is negatively correlated with trust at scale. The research is explicit: three-quarters of sellers say high-volume untargeted outreach erodes trust. More activity is not the answer.
- The window for human expertise has narrowed but its value has concentrated. The mid-funnel vendor comparison and pilot stages are where expertise has maximum commercial impact. Being present, credible, and findable at those moments is worth more than being visible at every stage.
- Warm introduction advantage is structural, not tactical. Sellers who generate meetings from warm introductions consistently outperform those who rely on cold outreach. The structural advantage belongs to professionals whose relationship networks are discoverable and accessible to the right buyers at the right time.
- Buyers without sellers make worse decisions. Buyers who self-serve through the entire journey without human expert input experience 23% higher purchase regret. The value of verified human expertise is not theoretical — it is measurable in outcomes.
Buyer checklist: how to access verified channel expertise
| What to look for | Why it matters | Red flags |
|---|---|---|
| Verified identity and company affiliation | Anonymous expertise cannot be weighted or attributed | No verifiable profile, company, or track record |
| Specific technology domain declarations | Generalist claims are less useful than declared category depth | Expertise claimed across too many unrelated categories |
| Account relationship evidence | Deal experience in your sector and region is the most relevant signal | No evidence of relevant sector or region exposure |
| Attributed published insights | Published expertise signals willingness to be accountable for claims | Only vendor-produced or unattributed content available |
| Org cluster corroboration | Multiple professionals from the same organisation making consistent claims adds weight | Single isolated voice with no collegial corroboration |
| Recency of activity | Active pipeline knowledge is more relevant than historical knowledge alone | No indication of current market activity |
FAQ
Why has trust in sellers fallen even as AI-generated information has increased?
Because AI has raised the baseline of what buyers can discover independently, making undifferentiated seller claims easier to challenge and less valuable. When AI can instantly compare a vendor's claims against competitors, identify gaps in a pitch, and surface alternative options, the bar for what constitutes genuinely useful seller input rises significantly. Sellers who show up with generic information are offering less value than buyers can already access themselves.
Does this mean human sellers are becoming less important?
No — it means human expertise is becoming more concentrated in value rather than more evenly distributed. The research shows that 75% of buyers would prefer to make a purchase without a seller, but those who do experience 23% higher purchase regret. Human expertise is most valuable at the mid-funnel vendor comparison and pilot stages. Being present at those specific moments with credible, verified expertise is more commercially impactful than being active at every stage with generic content.
What makes expertise verifiable rather than just published?
Verified expertise has three characteristics: it is attributed to a named, identifiable person with a confirmed organisational affiliation; it is specific to a technology category, sector, or deal pattern rather than generic; and it is consistent with and corroborated by other verified sources in the same domain. Anonymous claims, vague generalisations, and isolated single-source assertions carry less weight with both human buyers and AI research systems.
How does this apply to channel professionals specifically versus direct sales?
Channel professionals often have deeper category expertise than direct sellers because they work across multiple vendors, multiple account types, and multiple deal patterns simultaneously. A channel specialist who has sold microsegmentation through three different vendor programmes into EMEA financial services has a breadth of comparative experience that vendor-direct sellers rarely accumulate. That expertise is more useful to buyers making vendor selection decisions — but only if it is structured, attributed, and discoverable.
What should a channel professional publish first?
Start with technology domain declarations — the specific categories where you have the deepest experience. Add account relationship evidence — the sectors, regions, and deal patterns you have navigated. Then build toward published insights: objections you have encountered, buyer patterns you have observed, vendor behaviour you have witnessed across deals. Each addition increases the specificity and credibility of your discoverable profile.
Conclusion
The confluence of AI-augmented buying, concentrated trust value, and relationship-based deal mechanics creates a specific and time-sensitive opportunity. The buyers researching technology categories right now are using AI to shortlist vendors before a single sales conversation takes place. The channel professionals with structured, attributable, verified expertise published in discoverable form are influencing those shortlist decisions. Those who have not yet structured and published their expertise are not.
Information abundance has made generic content worthless. It has made verified, attributed, specific human expertise more valuable than at any previous point in B2B sales. The Trust Gap — between what buyers say would build trust and what they actually experience — is the gap that structuring and publishing real channel expertise is designed to close.
Signal.lab exists to make that expertise structured, attributed, and findable — by buyers, by buyer-agents, and by the vendors who depend on their channel to perform.