-2.png?width=2000&height=1124&name=Sidharth%20-%20%20TechSignals%20Blog%20FI%20Template(s)-2.png "How to sell with ai-led first impressions")
For years, the early stages of B2B software evaluation followed a familiar pattern.
A buyer began by searching the web, asking coworkers, and comparing software on websites. Sellers could reliably shape the buyer’s early understanding of their product.
Even when search engines provided options, buyers relied on vendors to interpret needs and connect technical features to business outcomes. The buyer’s problem statements could be easily mapped to the vendor’s strengths.
That sequence has now decisively changed.
Today, buyers turn to a generative AI system. They type a prompt like: “What’s the best customer service platform for a 200-person global support team?”
This response includes a market overview, a comparative analysis, a refined shortlist, indicative pricing, and a set of recommendations. Before any vendor has the opportunity to shape the narrative, the AI has already delivered first impressions. So how do you make sure your product shows up in the buyer’s shortlist?
“As generative AI becomes the buyer’s first stop, sales teams need to assume the conversation starts before human contact.”
Chris Donato
President and Chief Revenue Officer at Zendesk
This article explores how sales leaders must adapt when the first impressions are not mediated by the software maker but by the AI system interpreting its signals. Good news: there is still an opportunity for vendors to reclaim influence on buyers in this new environment.
GenAI now compresses what once took days of research into a single conversational moment. Buyers no longer sift through web pages or comparison charts. They ask the AI and get a synthesized narrative.
The buyer’s confidence is real, but their understanding may be incomplete. AI presents a synthesis of the available data. But it can’t fully interpret the buyer’s organizational complexity, cultural dynamics, risk appetite, or implementation constraints.
The result? Sellers now take on a buyer who feels informed but can often be misaligned.
“GenAI has reshaped how buyers research, compare, and shortlist solutions, creating a more dynamic, non-linear journey. They arrive informed and expect every interaction to move them closer to value.”
Leandro Perez
CMO for Australia and New Zealand at Salesforce
When buyers bring AI-generated conclusions into the first conversations with sales, rejecting or dismissing them could be futile. Why? The buyer views AI as an objective source, not an opinionated one.
As a seller, your role here is not to negate an impression based on AI summaries but to interpret it. Chris emphasizes this advisory shift by adding, “The best sellers are now advisors, guiding customers through what AI already knows about their category and reframing that context with proof, outcomes, and empathy.”
Buyers expect sellers to understand what AI has already said, to bridge the gaps, and to elevate the discussion beyond what a probabilistic summary can offer. Sellers are expected to offer more authoritative advice.
Yet this advisory role comes with a caveat. Buyers often mistake the AI’s confident delivery for complete accuracy. When buyers act on this surface-level information without questioning the data’s age or context, it creates a dangerous blind spot in the sales cycle.
How fast and how confidently AI delivers answers can sometimes be misleading.
Often, AI may exaggerate outdated product weaknesses or misinterpret feature nuances.
That means sellers must diagnose which parts of the AI-generated summary align with the buyer’s needs, and which parts need immediate recalibration.
As Subhasri Banerjee, Content Strategist and Marketer at Concurate, notes, the buyer’s journey is now “multi-touch by default, but it gets compressed because of AI”. This compression means sellers must stop treating first contact as discovery and immediately address the “hyper-specific questions” AI has created.
By the time the first meeting begins, many buyers already believe they understand the landscape. This creates a new challenge where sellers must respond to assumptions they didn’t create.
This creates a fragile dynamic. The buyer feels ready to decide, while the seller is still trying to discover. It is no longer about delivering the “what”. AI has already done that. It’s about answering “so what.” Sellers must stop fighting the AI narrative and start contextualizing it. Here is the tactical framework for engaging a buyer who enters the meeting with their mind already half-made with AI-fed knowledge.
Buyers today don’t trust easily. They’re flooded with AI-generated noise and content that all sound the same. Learn to gain trust and influence buyers in our latest webinar.
AI responses speak with the confidence of a consultant, the authority of an analyst brief, and the simplicity of a friend’s recommendation. If sellers don’t surface what the buyer has already read, they risk speaking past assumptions.
This necessity is reinforced by a new trend:
- Up to 79% of software buyers say AI search has changed how they conduct research, while 29% start their software research with AI search more often than Google, according to the G2 Buyer Behavior Report 2025.
- B2B buyers are adopting AI-powered search at three times the rate of consumers, according to the Forrester 2024 Buyers' Journey Survey.
This means every sales conversation must begin with uncovering the AI’s framing and how it influences the buyer’s impression.
Buyers may not approach AI summaries with skepticism. They may view them as data-driven, even when the underlying data is incomplete or outdated.
Buyers are using AI more like a trusted friend. They’re relying on it to compare vendors, validate claims, and simplify complexity.
If AI is the trusted friend, the salesperson must become the interpreter. They must understand the buyer’s sources of impressions and must verify AI-fed knowledge. Validation opens the door to steering the conversation in the seller’s favor.
AI tends to summarize, condense, and simplify. That makes its outputs useful for initial orientation.
Beyond first impressions, AI could be insufficient to nudge a buyer to decide. Chris highlights, “Winning in this new environment isn’t about more noise, it’s about shaping the narrative AI learns from and showing up with differentiated, data-backed value from the first interaction.”
The most credible sources for buyers are those grounded in peer experience. For instance, when making a final decision, buyers in North America rely most on genAI chatbots (17.2%) and software review sites (13.4%), both outstripping the vendor salesperson (9.3%), according to the G2 Buyer Behavior Report 2025.
In the eyes of the buyer today, a salesperson’s promise could hold less weight than an algorithm’s synthesis. This signals a crisis of trust. To bridge it, sellers must pivot from persuading to proving. AI provides the “average” or the mean experience. Yet your job is to showcase the “actual” experience. A way to do this is to introduce peer reviews and case studies early in the cycle. This way, you stop asking the buyer to trust you and start asking them to trust their peers.
Source: G2 Buyer Behavior Report 2025
GenAI tools capture insights from an organization’s digital footprint, not just the messaging designed for buyers. Any contradictions in this footprint become contradictions in the AI’s synthesis as well.
Sarah Gavin, Senior Vice President of Communications at Zendesk, is dealing with this in multiple ways. "The success of what we do is now much more directly tied to how our customers experience us, which means every marketer now has our customers in the front of our minds with every decision we make," she says.
Second, her teams work more "tightly" as they know the power of consistent, compelling, customer-centric messages matters more than ever. She adds: "G2 serves as an essential reminder for all of us to remember that great marketing comes from working together to build and reflect great customer experiences."
This also requires an intentional strategy anchored in answer engine optimization (AEO). To gain AI visibility, content must be structured to improve machine readability:
Andy Crestodina, CMO of Orbit Media Studios, highlights this: “Taglines don’t train the AI. The sales team knows what wins the deal… But often, the marketing team is focused on brand messages, not sales messages. Marketers’ new job is to train the AI to recommend the brand.”
This means writing website copy that answers the key questions prospects ask during the sales process, he explains.
We know that AI is training on our websites, asks Andy. "But is it finding everything it needs to recommend you?" He advises marketers to:
“I think of AI as the new SDR. So our job is to be the obvious script it reaches for.”
Abhishek G.P.
Vice President of Growth at Atlan
Up to 84% of buyers say they rely on peer review sites when purchasing software. See how you can leverage authentic reviews to build trust, enhance your pitch, and close more deals. Watch now.
When a buyer shows up with an AI-generated shortlist, it reveals how the AI interprets the category and which vendors exhibit visible signals. This intelligence is critical because buyers prefer to limit their options today. They prefer narrowing their options to just two or three vendors, according to the G2 Buyer Behavior Report 2025.
This new shortlist should be treated as intelligence. It tells sales leaders who the new competitors are, which attributes shaped the AI’s grouping, and where the organization’s positioning is either resonating or diluted.
AI improves and accelerates discovery, but it can’t interpret organizational nuance. It can’t interpret the buyer’s organizational politics, risk tolerance, cross-functional needs, implementation realities, or cultural dynamics.
The successful sales teams position themselves not as correctors of the AI narrative but as interpreters. They translate summaries into actionable decision paths. This creates a new mandate for sales teams: Human judgment must become the interpretive layer that transforms AI’s generalizations into actionable direction.
“Sellers need to shift from just pitching to advising. Combining AI efficiency with human judgement, teams can deliver a personalised buying experience that AI alone can’t replicate.”
Leandro Perez
CMO for Australia and New Zealand at Salesforce
AI has rewritten the start of the B2B buying journey. It is now the default starting point for early discovery, vendor comparison, and shortlist formation. But while AI increasingly controls the first impression, it does not control the outcome. That responsibility still belongs to sales leaders.
The prescription is clear.
Sales teams must treat every customer-facing digital signal as training data that shapes the AI’s perception of their brand. Companies that fail to align their narrative across public channels will show less on a search engine.
Sellers must abandon the reflex to “correct” AI-led assumptions. The winners will be those who build on the AI’s summary, contextualize it, and elevate the conversation to a human level. Evidence, not messaging, will become the dominant currency of trust.
Companies should expect AI to become increasingly agentic in vendor discovery and evaluation. Soon, AI tools will not just summarize the market; they will negotiate criteria, validate vendor claims, and conduct preliminary fit assessments. Sales teams must prepare for this by supplying structured proof and strengthening the interpretive role humans play.
AI may control the first impression, but it will never close the deal. The companies that win will treat AI as the opening frame and not the final verdict. They will use superior evidence, sharper judgment, and decisive storytelling to shape the outcome.
Sales is harder than ever. Sales cycles are longer, and buyer expectations are far higher. Watch our webinar to decode the changing buyer behavior.
First, understand what the buyer consumed from AI. Validate the parts that are true, and then add context. Instead of trying to “correct” the AI-formed opinion, sellers should build on it with evidence, case studies, and customer outcomes. This strengthens trust and may nudge a buyer to make a decision.
AI favors content that is recent, structured, and consistent across platforms. Companies should update product pages, publish clear comparison assets, encourage customer reviews, and maintain message alignment across marketing and sales. These structured signals help AI rank the vendor more accurately.
AI can summarize features but cannot interpret a buyer’s organizational politics, priorities, risk tolerance, or implementation realities. Human sellers translate summaries into actionable decisions. Their role is to guide, clarify, and help buyers navigate tradeoffs that AI can’t understand.
