The AI confidence gap in B2B marketing.

Skills are rising. Confidence is dropping.

Skill development is clearly on the rise. B2B marketers are actively exploring new tools, building AI into daily workflows, and refining their prompting techniques. Content workflows are accelerating and gaining sophistication. Many teams are finding ways to integrate AI into campaign execution and production.

Momentum has not translated to stability. Across the study’s six adoption pillars, Job Confidence, the measure of how secure and supported marketers feel in their roles, ranked lowest. Meanwhile, Individual AI Proficiency was among the highest ranked. Those two readings sit at opposite ends of the index.

Read together, those two data points draw a paradox. Marketers are becoming more capable while feeling less secure. That is the AI confidence gap.

What marketers are actually asking

Without shared standards or clear direction from leadership, experienced marketers keep surfacing the same questions:

• “What does success with AI actually look like inside our organization?”
• “Where should I take initiative, and where is leadership guidance still needed?”
• “When am I augmenting my work, and when am I replacing part of it?”

These are not the questions of a disengaged team. They are the questions of a team that has moved ahead of its own operating model. Marketers are requesting specific AI guidelines, flagging friction in role designations, and hesitating to fully adopt tools without leadership endorsement. AI use is spreading organically, and many teams lack the frameworks that give it long-term legitimacy.

Frame the “why” behind AI

Confidence does not come from tools alone. It demands context, clear explanation about what AI is for, how it fits, and where it is going. Leadership plays a clarifying role here that no toolset can substitute for.

Most teams are familiar with what AI can do. Fewer have a shared sense of why they are using it beyond raw efficiency. A shared narrative connects adoption efforts to broader goals. Whether AI is being introduced to scale content, free up creative time, or accelerate speed to market, purpose matters. When teams understand the bigger picture, adoption starts feeling intentional.

Delineate roles and responsibilities

After getting access to the tools, teams benefit from explicit guidance about how responsibilities are divided between AI and human input.

That work usually involves mapping tasks across the marketing function and considering three questions:

• Where can AI help scale or simplify repeatable work?
• Where is human insight, judgment, or nuance required?
• Where is human-AI collaboration most effective, with appropriate oversight?

Outlining these boundaries supports more consistent execution and reduces ambiguity about how roles evolve as adoption deepens.

Revisit performance metrics for AI-supported work

Traditional KPIs may not fully reflect the value of AI-supported work. A marketer using AI to accelerate iteration or refine messaging might be creating more impact without increasing output volume. That work is invisible on a scorecard that only counts assets shipped.

Updating metrics to reflect new workflows and outcomes provides a more accurate view of contribution. It also supports a culture where thoughtful use of AI is rewarded, and where speed of production is only one dimension of what good work looks like.

Bring teams into the conversation

Rather than introducing AI top-down, many organizations are creating space for teams to share input and shape adoption. Common formats include internal AI working groups, department-level leads, or regular forums that exchange use cases, challenges, and learnings.

When teams feel included in the process, they are more engaged with the results. Adoption becomes a shared design problem instead of a mandate that arrives from a memo.