AI is transforming B2B marketing — from how briefs are written to how campaigns are orchestrated. But while the tools are evolving fast, the way organizations manage AI responsibilities is often stuck in reactive mode. Forrester’s latest research reveals that most marketing teams still operate with ad hoc approaches to AI responsibilities, limiting its potential to drive tangible business outcomes.
The Problem: Ad Hoc Doesn’t Scale
In the rush to adopt AI, many marketing leaders assign resources on the fly, without a formal structure or accountability. This “just in time” approach may feel agile, but it creates silos, duplicates effort, and introduces governance risks. Without a strategic framework for organizing AI responsibilities, teams will struggle to effectively deliver on promises such as personalization, analytics, and improved customer experiences.
Many marketing teams struggle with three common challenges:
Skill gaps. There’s a shortage of both skilled AI talent in marketing and project managers who can guide AI initiatives from ideation to production.
Lack of strategic resourcing. Despite their perceived importance, marketing AI projects are often underresourced, with team members adding them on top of current workloads.
Resistance to change. Teams may fear job displacement or lack the motivation to learn new skills, slowing adoption.
The Way Forward: Define And Organize AI Responsibilities
Before seeking to scale AI projects, marketing leaders must define responsibilities and who owns them. These responsibilities aren’t necessarily entirely new, but they must be contextualized for AI. Our research has identified the following AI marketing responsibility categories: vision, strategy, and investment planning; organizational readiness and training; governance and ethical oversight; use case ideation, evaluation, prioritization, and approval; technical development and data preparation; and production deployment and value measurement.
Models For Organizing AI In Marketing
How these responsibilities are organized varies among companies, ranging from ad hoc experimentation and tiger teams (the two most common types we have observed) to AI councils, centers of excellence, and federated collaborations. There’s no “one size fits all” approach. Your ideal model will depend on key factors such as the strategic importance of AI in your organization, team competency levels, your existing approach to centralization vs. decentralization, available resources, and the culture between marketing and IT.
To learn more about this latest research, I encourage you to join me at B2B Summit APAC next month. My keynote address, “Structuring Your GenAI Talent For B2B Marketing’s Future,” will go into greater depth and provide insights you can apply to your marketing organization.
