B2B organizations have high expectations for the role agentic AI will play in buyer journeys, account-level intelligence, and customer experience (CX). These expectations span the full customer lifecycle — from pre-sale engagement through post-purchase support — and signal a fundamental shift in how organizations envision their relationship with buyers.

An overwhelming 78% of B2B organizations expect agentic AI to manage at least half of all their customer support interactions within the next 18 months. Another 69% expect the same for their customer sales interactions and transactions. When asked about their brand’s customer experiences, over half (56%) of B2B organizations said they believe agentic AI will coordinate their sales, marketing, and service journeys in real time — a capability that would represent a step-change from today’s siloed engagement models.

Organizations point to tangible readiness indicators to back these goals. Over two-thirds (68%) say they have identified practical, high-value AI use cases, and 65% report having security and privacy controls in place for their AI tools. Meanwhile, almost two-thirds (60%) say their customers actively expect B2B buyer experiences to include innovative AI solutions, creating external demand pressure that reinforces internal urgency.

B2B organizations are also preparing for a future where customers deploy their own AI agents to carry out tasks on their behalf. Yet there is concern that customer appetite for agent-driven interactions may be more cautious than organizational ambitions suggest, particularly in scenarios involving sensitive decisions or fully autonomous actions.

Over half (57%) of B2B organizations believe customers would be at least somewhat comfortable having their own AI agents work alongside a brand’s human representatives. That comfort level drops sharply as AI autonomy increases. Only 39% of B2B organizations anticipate that customers would be comfortable having their own AI agents work directly with a brand’s AI agent, an approach that removes the human intermediary entirely. Fewer than a third (30%) foresee customers letting their own agents handle the research, negotiation, and final decision-making involved in a large or complex purchase (Figure 1). To successfully deploy agentic-driven buyer journeys, B2B organizations will need to account for the pace at which customer trust is built, not just the pace at which the technology is ready.

While B2B organizations are eager to benefit from agentic AI’s transformative capabilities, many struggle to scale the technology due to key issues surrounding data infrastructure, talent development, and the ability to assess AI’s value. These barriers form an interconnected web: without reliable data, AI models underperform; without skilled teams, organizations cannot deploy or refine those models; and without robust evaluation frameworks, leadership lacks the evidence to sustain investment.

Three-quarters of B2B organizations cite data integration and quality as their biggest struggle in implementing agentic AI, ranking it above every other challenge. The underlying problem is structural: only 41% say they have the unified customer data foundation needed to extract insights from all the data created by their AI agents and conversational interfaces.

Fewer than half (48%) say their organization’s current data quality and accessibility is adequate for AI, and over half (54%) admit that their ability to advance AI initiatives is limited by their current level of data unification and structure. These infrastructure gaps are compounded for more than a third (36%) of B2B brands, who say legacy systems and vendor lock-in create technology infrastructure challenges that further constrain their data environments.

In response, interoperability is emerging as a priority in the vendor selection process. Over half of B2B organizations (55%) view agent-to-agent interoperability as crucial when deciding on AI solutions, signaling that organizations recognize siloed tools will only deepen their data fragmentation.

Nearly three-quarters (72%) say they struggle with talent and skills gaps when implementing agentic AI solutions. The expectation placed on employees is high: over half of B2B organizations (58%) say their employees should now consider AI an indispensable coworker, not just a tool. This suggests brands are looking to AI as a force multiplier, particularly on constrained teams. Yet fewer than half (48%) characterize their employees as “comfortable” using AI in their roles, a gap between what organizations demand of their workforce and what that workforce is currently equipped or willing to deliver. Further complicating the issue: while 58% say their organization is committed to providing the skills training required for employees to become proficient in using AI tools, just 49% believe they have sufficient AI training and upskilling programs in place.

Evaluating returns from agentic AI investments proves difficult for B2B organizations: 71% rank unclear ROI or business cases as a key implementation barrier. Over half (57%) struggle to demonstrate measurable returns using CX-related metrics on any of their AI investments, agentic or otherwise. A contributing factor may be how success is defined at the top. Over half (52%) say their organization’s leadership prioritizes purely financial metrics when assessing AI initiatives, a narrower lens than the broader operational, experiential, and strategic value AI can deliver.

The assessment gap is particularly acute for newer technologies. Only 39% of B2B brands have developed measurement frameworks for evaluating the value and ROI of their generative AI technologies, and even fewer (30%) have these frameworks in place for agentic AI. As many as half (51%) say their organization has neither framework in place or are unsure (Figure 2).

Despite implementation challenges, B2B organizations are directing their AI investments toward a clear set of priorities. Rather than leading with revenue growth or cost reduction, B2B organizations are prioritizing customer engagement and operational improvement as the primary goals of AI adoption.

Delivering more personalized customer experiences is the top AI investment goal for 59% of B2B organizations over the next 18 months. The next tier of priorities reinforces this customer-centric and operational focus: automating repetitive tasks (47%); improving customer satisfaction, loyalty, and engagement (45%); improving data quality, unification, and governance (32%); and increasing and accelerating content creation and activation (31%). By contrast, growing revenue or adding new revenue streams ranks as a comparatively lower priority, selected by only 28%, while lowering support and operational costs is cited by just 23% (Figure 3).