Where enterprise sellers fit in AI-assisted B2B buying journeys

Seismic's 2026 The New Go-to-Market Standard for B2B Selling Report found just 7% of B2B businesses always wait until everyone on their buying team feels entirely confident in a proposed solution before procuring it.

The Seismic research also discovered 93% of scaled B2B organizations still proceed with a sizable purchase for their companies while still having unresolved doubt within their buying groups at least a quarter of the time.

Consensus can be difficult to come by for high-priced investments.

Agreement gets even harder to achieve in this new(ish) AI-assisted B2B buying environment, as each purchasing stakeholder—from end users, to senior executives—tends to research vendors in siloes using their AI systems of preference.

Regardless of which generative AI tool a buying decision-maker or collaborator uses to review brands and their offerings, the same issue typically occurs: Champions, economic and technical buyers, budget-holders, and other committee members end up with 'information overload' and conflicting details and data.

For enterprise sellers, this presents an opportunity to intervene intelligently in this evaluation process to address potential customers' questions, comments, and concerns with direct, plain language straight from the source.

Not from a general-purpose AI tool that firms attempt to turn into their B2B 'shopping assistants.'

What the rise of AI-assisted buying has done to buyer behavior in recent years

"B2B buyers are more comfortable using digital channels and GenAI to navigate the purchase process on their own, but that does not eliminate the role of the seller," Gartner Sales Practice VP Analyst and Chief of Research Robert Blaisdell recently explained. "Buyers still turn to sales reps to validate AI-generated insights, and support decision-making at critical moments in the journey."

Leveraging large language models, answer engines, and AI agents to get a deeper understanding of a prospective vendor—their solutions' key features, pricing model, company news, and other tidbits and insights—has forever changed B2B customer behavior for large businesses and the buying landscape at large.

And there's no going back.

Specifically, this B2B customer journey that's reliant on generative AI models has:

Moved vendor discovery into AI chatbots before salespeople get a call

"Why reach out to a salesperson only to hear a plain pitch when I can turn to ChatGPT to get the info I need on their company and products?" It's not overgeneralizing to say this is the mindset of many B2B buyers today.

The emergence of AI discovery now stages a brand's first impression.

Answer engines and AI 'helpers' condense years of thoughtful and careful market positioning put in place by go-to-market teams into a few tidy paragraphs. That abbreviated summary now replaces an organization's homepage and a seller's call opener as the framing for how buying councils see vendors.

Shortlists take shape in private chat windows, often before a company being considered learns it's even in the running. By the time a first meeting lands, prospects have sorted rivals, formed hard opinions, and drafted pointed questions.

Those questions aren't generic either, since buyers are becoming more sophisticated with long-form prompting. They describe their own firm's circumstances first, which makes each answer about a vendor feel custom-crafted for them.

Hyper-personalized recommendations by feeding AI business context

Just as enterprise sellers use content personalization to better engage prospects before, during, and after sales conversations and offer bespoke resources to them, LLMs can tailor vendor comparisons to a buyer's own situation.

All it takes is a well-written prompt.

Paste in procurement rules, a security questionnaire, last quarter's numbers, and next quarter's goals, and the recommendation reads like a briefing from an analyst assigned to that account.

It persuades precisely because it feels personal.

That said, tailored isn't exactly the same as complete. An AI model only works with the info it's handed. That means unwritten politics, half-formed priorities, and budget parameters are never factored into suggestions for B2B buyers.

Perceptive buying groups usually sense that gap, and it nags at them.

The more convincing an AI-generated answer sounds, the likelier they are to seek a second (or even third or fourth) opinion, and whom they ask says a great deal about their trust in AI chatbots.

Pushed B2B buyers to fact-check any AI answers with peers they trust

That could mean a former coworker at a similar company who utilizes a SaaS solution of interest, a customer community thread for a given business, or a mutual connection on LinkedIn who bought in the category in the last year.

Ideally, though, it means a knowledgeable seller for the vendor in question who knows their solutions inside out and can provide individualized advice, updates, and intel that B2B buyers need to make enlightened decisions.

Lived experience is the input no prompt can fully replicate.

Fluency with agentic and generative AI has become a warning sign as much as a comfort, since buying committees have watched confident answers collapse under one follow-up question and now (rightfully) treat every output as a draft.

That leaves B2B buyers' confidence in AI research in third-party hands, with vendors hearing the verdict only after it's delivered (and working hard to dispel any myths and misconceptions shared by AI about their companies).

Why B2B buyers are best served speaking with vendors even after AI discovery

"Buyers can access product claims, comparisons, and AI-generated answers before they speak with a seller," according to Seismic's 2026 The New Go-to-Market Standard for B2B Selling Report. "[But] buyers still rely on people when a decision requires context, accountability, and a clear view of the tradeoffs."

There are several reasons why decision-makers at commercial accounts—from deal champions, to budget-holders—would be wise to reach out to sales representatives when appraising their organizations:

• Pricing talks need a human who can read what's behind the number. An LLM can benchmark solution prices quickly, based on publicly available information. Yet 55% of B2B buyers polled by Seismic said their firms still rely on a salesperson to negotiate pricing and contract terms. A seller can factor in the deal scope, contract length, and rollout phasing against potential discounts.
• Business cases only hold up when someone can defend the assumptions. Nearly half (49%) of B2B buyers surveyed by Seismic noted they lean on salespeople to evaluate the possible return on investment of potential solutions. A savvy seller can rework ROI inputs with finance leaders on buying committees and alleviate any anxieties they have about systems and services under consideration.
• A polished AI summary doesn't often show where the downside sits. Another 45% of B2B buyers polled by Seismic turn to sales teams to better understand risks, since a fluent answer from an AI assistant can bury important caveats. Sellers can point to any likelihood of internal friction or bottlenecks that could occur after implementing a solution and outline total cost of ownership.
• An accurate general answer can still fail in the buyer's environment. Seismic's research found 43% of B2B buyers rely on sellers to interpret how a solution applies to their unique business situation and can fold into their existing technology environment (i.e., learn about integrations, data migration, etc.). Sellers can catch a flawed suggestion before it reaches the buying committee.
• Conflicting AI answers inside a buying group need someone to referee. One-third (34%) of B2B buyers surveyed by Seismic still speak with salespeople to check claims found in AI tools used for vendor research. Only sellers can reconcile competing summaries from AI-powered search in ChatGPT, Claude, and the like against current facts tied to their brands' products and services.
• Sellers can share what similar customers learned during their rollout. That's why 39% of B2B buyers polled by Seismic continue to lean on sellers for rich industry expertise and peer references, much like the case studies that skeptics ask for. Adoption hurdles, typical onboarding timelines, and results months after investment at comparable scale often never appear in public content.
• Large buying councils need a guide who understands each member's concern. With 37% of buyers surveyed by Seismic relying on sales organizations to navigate internal alignment, each function still needs its own answer. A seller can match an executive's ROI-related questions, IT's integration needs, and IT and security's risk checks with tangible proof each team can inspect.

As our report explains, AI is extremely helpful to B2B buyers in many ways, but human judgment at the moments when prospects require it most helps them "make sense of abundant information, reconcile competing requirements, quantify value, surface risk, and stand behind a recommendation."

How sellers can address B2B buyer information overload and fill in context gaps

It's evident analysis paralysis stemming from 'information abundance' due to AI chatbots can actually make it harder for B2B buyers to come to an accord regarding investment in a new product or service.

What's just as clear is the opportunity for salespeople to help guide potential customers in their decision-making process and ensure they don't end up with buyer's remorse from a big (and pricy) investment.

To do so, enterprise sellers must:

Prepare for every meeting with leads with current account context, stakeholder insight, and known risks in hand

Deloitte's 2026 B2B commerce research found around 40% of enterprise buying groups have enlisted agentic AI to vet vendors, draft orders, comb through contracts, and sanity-check pricing models.

Walking into a first call without matching homework invites being outpaced. The smarter play is arriving with a dossier on the organization's footprint, the people attending, and landmines likely to surface.

With AI-powered sales agents, enterprise sellers can assemble that briefing automatically, while account-specific revenue intelligence keeps the file fresh as new signals trickle in. That head start frees the opening minutes for more intricate discovery questions instead of rote introductions.

Uncover what each decision-maker already learned from AI tools before you add brand ‘truth' to a pitch

Think of this as light detective work. Consultative selling starts with curiosity: Ask the executive, architect, and budget-holder which chatbot they consulted and how it answered.

Few buyers volunteer that info unprompted, yet their responses reveal distortions ripe for correction, stale rumors to retire, and genuine enthusiasm about potential solutions to nurture.

The distance between their mental picture and reality is where buyer engagement is won or lost, since nobody warms to a seller who contradicts them without listening to their pains and problems first.

Reconcile conflicting AI summaries against facts and plainly state which claims fit the buyer's situation

Picture three buyers quoting three different chatbots, each sounding equally certain. Your job is to play editor, sorting the accurate from the outdated and the generic from the genuinely applicable.

A digital sales room gives everyone one shared, current reference point rather than competing screenshots. Resist the urge to dismiss a chatbot's output wholesale. Acknowledge what it got right. Refine the rest.

Behind the scenes, GTM alignment ensures marketing, product, and sales all speak with a single voice, so the corrections you offer land as authoritative rather than self-serving.

Quantify value with assumptions finance can assess, then adjust them live as the buyers offer input

Numbers only convince leaders when they're transparent.

Most finance-focused executives distrust a tidy ROI figure that arrives without its workings, so show the math: baseline costs, ramp time, and the variables most likely to swing results one way or the other.

An AI sales assistant models all scenarios in mere minutes, letting salespeople recalculate mid-conversation as leads push back or change parameters. Of course, comfort with the arithmetic takes practice, which is why sales coaching belongs in the plan from day one.

Transparency also invites skeptics to poke holes productively, which builds trust faster than polished materials.

Provide proof tied to each buying committee member's questions to quickly mitigate any concerns

Few buyers in a boardroom share identical worries. The finance lead wants payback timelines, security wants audit trails, and the operations crowd wants evidence the rollout won't disrupt their week.

Sellers who use AI can summon the right case study, certification, or benchmark data point instantaenously. Rather than recycling a broad sales pitch deck, assemble targeted resources for every single voice at the table, and the room's skepticism tends to ease.

Anticipating those divergent agendas in advance turns a tense round of objections into a collaborative working session.