B2B marketing teams are building software. They may not use that label, but the work is unmistakable: wiring campaign automations, connecting customer data to content systems, and shipping AI-powered workflows that touch real revenue. David Greenberg, CMO of BlueRock, argues in a new Q&A that this shift has created a new role - the citizen developer - and that most organizations lack the operating model to support it safely.
Research shows 67% of employees want their organizations to use more AI. Yet 36% still don't understand why they're expected to use it. Greenberg says the second number is the bigger red flag. "Enthusiasm for AI is important, but adoption without a clear understanding of the problem you are trying to solve creates a lot of activity without necessarily creating value," he said. Marketing leaders need to help teams identify where AI can redesign work, not just accelerate existing tasks.
What citizen developers actually build
A demand gen manager might construct an AI workflow around event follow-up. The system researches each attendee's company, cross-references CRM history and engagement data, identifies the highest-potential prospects, drafts personalized outreach, and routes those insights to the right salesperson. Work that once spanned several tools and hours of manual effort now gets built by the marketer who understands the problem best.
The danger arrives when an experiment becomes operational software without anyone treating it that way. That workflow may hold credentials, customer data, and permission to act across multiple systems. "Organizations need to enable these new citizen developers, but also give them safe environments, practices and guardrails so they can experiment and move into production without creating unnecessary risk," Greenberg said.
When marketing crosses into software building
Many CMOs recognize their teams are using AI. Fewer recognize those teams are building software. Using AI to create content or analyze data differs fundamentally from building agents and workflows that connect systems, make decisions, and take actions. The CMO's new challenge is pushing the organization to reimagine how marketing works while ensuring teams have the skills, safe environments, and operating practices to build responsibly.
When marketers adopt tools faster than leadership can govern them, the damage can hit data security, brand consistency, and campaign performance simultaneously. Traditional martech kept those risks separate. AI systems that make decisions and take actions can affect all three at once. "The real problem is that teams may not know something has changed until the outcome is already visible," Greenberg said. "Agents can adapt as they execute, so what worked yesterday may behave differently tomorrow."
The AI supply chain and what breaks first
Recent vulnerabilities involving AI models and platforms like Hugging Face expose a practical lesson for marketing teams. Modern AI systems depend on models, APIs, packages, connectors, and external services. Every connection expands what the system can do - and expands what the organization needs to understand and monitor. Teams need a secure workspace where AI can build and operate within defined boundaries, with runtime controls and real-time visibility into behavior changes.
The most overlooked exposure point sits in the connections between systems. The CRM may be trusted, the AI model may be trusted, and the marketing platform may be trusted. But linking them can grant an AI workflow far more access than anyone intended. Greenberg's fix: limit permissions, maintain visibility into what the AI is doing, and run workflows in a controlled workspace that enforces boundaries during execution.
Three moves for the next 30 days
Greenberg outlined three steps for CMOs who want to move teams from casual prompting to secure, repeatable building. First, identify a handful of real workflows worth building - repetitive work, manual handoffs, or processes where employees already understand the problem deeply. Second, give teams a secure environment designed for agentic building, with appropriate boundaries, visibility, and controls. This is the step most organizations skip. Third, teach people how to build well through hands-on learning around AI development practices, not just better prompting.
For marketing leaders looking to build these skills systematically, structured AI Automation Courses can help teams move from ad-hoc experimentation to repeatable workflows. CMOs specifically can benefit from an AI Learning Path for CMOs that addresses the strategic and operational challenges of leading AI adoption.
Responsible AI adoption, Greenberg argues, isn't about slowing people down. Teams doing it well provide a secure place to build and experiment, real visibility into what AI systems are doing, and clear boundaries without creating an approval process for every new idea. "The goal is to move governance closer to the point where AI actually takes action rather than forcing humans to approve every step beforehand," he said. "That's especially important as agents become more autonomous."
Why this matters for marketers
The teams that turn AI into durable competitive advantage will be those that embrace citizen developers while building the operating practices to support them. That means giving marketers across the organization safe environments, clear boundaries, and the skills to turn experiments into production systems. The alternative - ignoring the shift or locking everything behind approval tickets - leaves the best builders either stopped or working around the rules. Neither outcome serves the business.
Your membership also unlocks:
