AI is rewriting the B2B buying journey

For a long time, B2B marketing operated on a fairly predictable playbook. Marketing generated leads, sales educated buyers, and deals moved through a funnel that companies could see and reasonably control.

That model is breaking down.

Today’s buyers are far more informed long before they ever speak with a vendor or raise their hand to a vendor in any way. Buyers research independently, consult peers, explore communities, and increasingly rely on AI tools to make sense of the overwhelming volume of information available to them. By the time a vendor enters the conversation, much of the decision process has already taken place. In other words, the game has already started with buyers making plays, but sellers are still sitting on the bench.

Sellers are increasingly late to the game

Phillip Alexeev sees this shift clearly in the data. He points out that roughly three quarters of the modern B2B buying journey (75% of it) happens before a buyer ever contacts a company. He notes that much of the early discovery is now happening through AI-powered research rather than buyers engaging directly with traditional vendor content. Buyer research is also being driven by decision groups containing seven or more members, each of whom may have a different function.
If buyers and buying groups are forming opinions earlier and largely on their own, the influence window for vendors begins much earlier than many companies realize. This has a profound implication for marketing teams.

Nicole Leffer has observed the same “buyers are playing, while sellers sit on the bench” pattern in her work with buying organizations adopting AI. Buyers frequently arrive at vendor websites already having compared options, explored categories, and narrowed their thinking about potential solutions. AI tools make it possible to synthesize information quickly, which means buyers can move through the research phase of the process much faster than before. When sellers finally reach out, the result of the (purchasing) game may already be determined, with vendors having little influence to change the result.

Buying is more collaborative and complex than ever

At the same time that buyer research has moved earlier in the journey, the number of people involved in making a B2B purchasing decision has increased dramatically.

Buying decisions that once involved a single champion now typically include multiple stakeholders from different parts of the organization. Technical teams evaluate implementation risk. Finance evaluates cost and ROI. Operational leaders consider the impact on internal processes. Executives assess strategic alignment. Each of these buying group stakeholders has different influences and different motivators that sellers must consider to influence the collaborative buying decision.

The result is a decision process that is far more complex than the traditional lead-based funnel was ever designed to support.

Mike Rizzo believes this complexity is one of the defining challenges for modern marketing teams. Buying committees have grown in size significantly, and the number of signals generated during the research process has increased at the same time. Engagement is multi-dimensional, happening across websites, communities, product documentation, events, and third-party platforms.

Context matters more than ever

The challenge is not collecting data from buyers. The challenge is understanding and contextualizing what that data actually means, so it becomes actionable for sellers seeking to influence a buying decision.

This is where artificial intelligence begins to play a transformative role.

When buyers research independently and their decisions involve multiple stakeholders, the buying process leaves a trail of signals. A developer reading technical documentation, a procurement manager reviewing pricing information, and a marketing leader attending a webinar may all be participating in the exact same evaluation process. Yet in most marketing systems those buying group activities appear disconnected, without context.

AI can connect those seemingly disconnected signals, making sense of the (seeming) randomness in order to support sales success

Tarun Arora explains that this ability to recognize and analyze patterns across accounts is what makes AI particularly powerful for account-based strategies. Traditional lead models treat individuals as isolated contacts, but real buying behavior rarely happens that way. AI allows teams to detect coordinated activity across an organization and recognize when a buying group is forming around a specific problem.

Instead of reacting to a single, isolated form fill or content download, teams can begin to see the broader story of how a company is evaluating potential solutions.

Understanding and contextualizing these patterns matters because not every signal represents real buying intent. Modern marketing stacks capture enormous amounts of behavioral data, from content engagement to social interactions to product research. Without context, much of that activity is simply viewed as noise sellers can safely ignore. AI helps make sense of it all, enabling GTM teams visibility into the larger buying picture, sooner.

Identifying patterns to provide context

Gina Stracuzzi argues that the most meaningful signals are not isolated actions but patterns of activity across multiple stakeholders. When several individuals from the same company begin researching the same topic within a short period of time, that behavior often indicates a coordinated evaluation effort rather than casual and disconnected curiosity.

AI systems are particularly effective at identifying these patterns because they can analyze large volumes of activity across accounts and channels simultaneously.

Richa Agarwal notes that this capability allows marketing teams to move beyond generic segmentation and focus on meaningful behavioral cohorts. Instead of treating every visitor or lead the same way, organizations can identify clusters of companies that are actively exploring similar problems and tailor their engagement accordingly.

This shift from isolated signals to coordinated patterns fundamentally changes how companies approach demand generation.

It also changes when and how companies need to show up in the buying journey.

From reactive selling to proactive solutioning

If buyers are doing most of their research independently, waiting for them to actively raise their hand by filling out a form or requesting a demo means that sellers are entering the conversation far too late, at a time when sellers have made up their minds or are close to doing so. Vendors need to participate earlier, during the research phase when buyers are still developing their understanding of the problem and tentatively scoping out potential solutions.

David Kirkdorffer believes that this “earlier entrance of sales into the game”  is where AI will reshape marketing visibility in the coming years. Buyers increasingly ask AI systems to explain categories, summarize solutions, and compare vendors. That means the content companies create must be structured in ways that AI tools can interpret and reference.

The goal is not simply to rank in search results but to become part of the knowledge layer that AI systems use when answering buyer questions.

At the same time, early engagement must be handled carefully. Buyers rarely appreciate aggressive sales outreach before they are ready to engage with vendors. No buyer wants a 15% discount on some random “solution” until they actually know what solution might be best for the particular problem they’re facing. If they’re researching and evaluating, and you’re “hard selling,” then the likeliest outcome is the loss of your brand credibility and buyer disengagement.

Mike Rizzo emphasizes that the opportunity lies in helping buyers make sense of the problem rather than pushing them toward a specific product or solution too early in the game. Educational content, thoughtful analysis, and practical frameworks are far more effective at this evaluation stage than promotional (“buy today!”) messaging.

When done well, this more consultative sales approach builds trust and buyer credibility long before a formal sales conversation has begun.

And trust matters more than ever in an environment where buyers are overwhelmed with options and underserved when it comes to educational content around their specific problems and potential solutions.

The new personalization is hyper-contextual

AI can also help companies deliver a level of personalization that was previously impossible to achieve at scale. For years, personalization in B2B marketing meant little more than inserting a company name into an email template. While useful, that “paint-by-numbers” approach to personalization rarely reflected the real context facing the buyer, nor did it do much to impress buyers.

Today, AI can synthesize information about an organization’s industry, technology environment, competitive pressures, and strategic priorities. That enlarged context enables marketing and sales teams to tailor their messaging in ways that feel genuinely relevant. You don’t just know their name [insert name here], but you understand what their biggest challenges are and can start informed conversations around how you can help them solve those challenges.

Tarun Arora describes this shift as moving from superficial personalization to contextual understanding. Instead of simply acknowledging who the buyer is (“Hi Mary!”), vendors can demonstrate that they understand the challenges buyers are confronting right now.

Jason Cormier notes that achieving this level of insight previously required extensive, time-consuming, and labor-intensive manual research. AI dramatically reduces that effort, making meaningful personalization possible across large account lists.

Tapping Into AI’s full potential

Yet despite the emerging opportunities AI presents, most organizations are still early in their journey toward AI-enabled go-to-market strategies.

Jim Sterne points out that many professionals still use AI primarily for simple tasks like generating emails rather than strategic analysis. While the tools are potentially powerful when aimed at the right use cases, their potential is too often being woefully underutilized.

Other contributors highlight AI-related challenges around data quality, organizational alignment, and the difficulty of integrating AI into existing workflows.

Tarun Arora stresses that AI systems depend heavily on clean, unified data. When customer information is fragmented across multiple systems, insights become incomplete and unreliable.

Nadia Davis also notes that scaling AI across enterprise organizations requires careful planning because AI operates probabilistically while many business processes expect deterministic outcomes.

In other words, adopting AI is not just a technology decision: it’s an operational transformation.

Advice for getting AI right

For revenue leaders trying to navigate this transition to AI, several contributors offered similar advice.

The starting point is not the tools themselves, but knowing what to use them for and how. Any journey must begin with your specific use cases, in understanding how your buyers actually make decisions.

Jason Cormier recommends mapping the real decision dynamics inside target accounts before implementing new AI platforms. Gina Stracuzzi echoes this view, arguing that organizations often rush into technology adoption without first understanding the buying process they are trying to support.

Begin with the end result in mind. When teams begin with buyer insight and strong data foundations, AI becomes far more valuable as a driver of measurable sales success. It can synthesize buying signals, surface patterns, and automate analysis that would otherwise require enormous manual effort, setting the stage for value-adding sales actions.

In that sense, AI is not replacing human judgment in B2B marketing, it is augmenting it.

The organizations that succeed will be the ones that use AI to better understand their buyers, interpret the signals those buyers leave behind, and engage them with relevance and credibility throughout the decision process. When sellers get into the game earlier, better sales outcomes result.

Today’s B2B buying journey is changing rapidly. AI is not the only force driving that change, but it is accelerating every part of it.

For marketing teams willing to adapt, AI offers something that has always been difficult to achieve: a clearer view of what buyers are actually doing long before they raise their hand. So stop sitting on the bench and get in the game sooner.

Download the B2B AI GTM Report if you want to go deeper into how leading B2B teams are actually applying AI in their go-to-market strategies