When Vice President and Principal Analyst Ross Graber took the stage at B2B Summit North America this spring, he opened with an unlikely case study: nearly four decades of loyalty to the New York Mets. Mets fans, he observed, have spent years repeating a familiar mantra: “You gotta believe.” Belief serves a purpose. It keeps hope alive, even when the evidence points elsewhere. The same dynamic, he argues, exists in B2B marketing.

For years, marketers have operated under a widely accepted assumption that engagement demonstrates value. Website visits, content downloads, event attendance, leads, and pipeline influence became proxies for marketing’s contribution to the business. Entire processes, technologies, and measurement frameworks were built around proving engagement. According to Graber, that assumption was always incomplete. Now, AI search makes the problem impossible to ignore.

The Accountability Model Was Already Showing Cracks

Graber’s keynote pointed to a disconnect many marketing leaders already recognize. Only 37% of B2B marketing decision-makers trust their measurement and analytics enough to inform decisions. At the same time, four of the five most common criteria that organizations use to evaluate marketing rely on proof of engagement.

Those metrics persist because they are tangible — they provide a straightforward way to explain marketing activity to executive stakeholders. But they don’t necessarily reflect how buying decisions are made. As Graber noted, much of marketing’s influence occurs long before a prospective customer ever fills out a form, attends an event, or enters a formal buying cycle.

AI has made these metrics harder to defend. As buyers incorporate AI tools into their research, fewer interactions take place on vendor-owned properties. Instead of clicking through to websites, buyers increasingly receive answers directly within AI search experiences.

As Graber noted, the consequences for measurement are enormous. AI search produces dramatically fewer click-throughs than traditional search experiences, and many organizations already have experienced substantial declines in web traffic. As engagement data declines, processes built around it also become less reliable. Graber pointed to several cornerstones of modern B2B marketing that depend on engagement visibility, including waterfall models, budgeting approaches, attribution systems, and sales-and-marketing alignment practices. For organizations that continue to rely heavily on engagement metrics as evidence of success, AI search threatens to undermine the very foundation of their accountability systems.

A Different Definition Of Accountability

Rather than trying to preserve an increasingly fragile measurement model, Graber urged marketing leaders to rethink accountability altogether. He advocates for an approach that centers on return on objectives.

While traditional engagement-centric models often jump directly from business goals to marketing metrics such as lead volume, sourced revenue, or influenced pipeline, a return-on-objectives approach introduces an intermediate layer: the business challenges preventing growth. Marketing objectives are derived from specific business obstacles. If the challenge is low problem awareness, for instance, marketing may focus on increasing urgency around that problem. If buyers consistently prefer competitors, marketing may prioritize shifting brand preference. Success is measured against objectives tied directly to those challenges, rather than against generic engagement totals.

Moving away from engagement-based accountability requires more than new dashboards. Graber emphasized that marketing leaders need to prepare their organizations for the scale of change underway. That begins with using internal data to demonstrate how buyer behavior is shifting and why established assumptions may no longer hold. Traffic trends, AI search referrals, engagement patterns, and conversion data can all help establish urgency and build support for change.

Organizations also need to revisit the objectives embedded across planning, budgeting, and performance management processes. Goals built around site visits, form fills, lead volume, or sourced pipeline may increasingly encourage behaviors that conflict with visibility in AI-powered environments.

Several examples highlight how companies are already reevaluating long-standing practices. In one case, a marketing organization moved away from sourced-pipeline goals in favor of objectives tied more closely to visibility and credibility in AI search experiences.

An Opportunity To Rewrite The Rules

The keynote’s message was ultimately less about measurement than about adaptation. Marketing leaders have a choice: They can continue defending measures that become less meaningful as buyer behavior evolves, or they can build a new accountability framework grounded in business objectives, business challenges, and measurable outcomes that matter to the organization’s growth.

As Graber argued, although AI search is disrupting long-standing assumptions about marketing accountability, it also creates an opening to replace them with something better.

Missed Ross at B2B Summit North America? You can catch his keynote, “An Accountability Reset Is Past Due,” at Forrester’s B2B Forum EMEA, happening September 28–29 in London.

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