At this year’s Marketing AI Conference (MAICON), I noticed a shift in how marketing leaders are approaching AI transformation. The conversation is moving beyond AI literacy and individual productivity toward building repeatable workflows, developing AI agents, and making these capabilities part of everyday work. Many of the sessions highlighted marketing’s next challenge: empowering teams to build with AI while maintaining the quality, consistency, and accountability the business requires.

The next phase of marketing AI transformation will depend on deliberate choices about what teams can build independently, what should be standardized, and who owns the results.

Let Teams Build, But Choose What Gets Standardized

Most organizations have invested in AI tools, training, AI champions programs, and governance. Those investments matter, but they don’t automatically change how marketing operates.

  • Move from AI literacy to building workflows. Several MAICON sessions illustrated a common problem: training marketers to use AI doesn’t necessarily translate into shared ways of working. One marketing organization had 76% of its marketers obtain an AI certification, yet it still struggled to turn that investment into operational workflows. It eventually shifted toward individual teams within marketing identifying and developing one high-impact workflow to redesign and build. Another marketing organization invested in builder bootcamps and team hackathons, requiring participants to develop agents for specific use cases. Teams presented their work to leaders, with the top three agents moving into production across marketing. Both examples point to an important lesson: a central team can’t identify and build every valuable workflow. Marketing leaders should set priorities and provide resources, while teams identify their high-impact opportunities, build the solutions, and demonstrate results.
  • Decentralize problem-solving and standardize what must be consistent. Giving teams the freedom to build doesn’t mean every team should develop its own standards, knowledge sources, or governance practices. Marketing leaders should make explicit decisions about where autonomy makes sense and where consistency matters. Teams can experiment and build workflows that address their specific needs, but they shouldn’t independently recreate the foundations that all marketing workflows depend on. Standardize approved messaging, brand guidelines, trusted knowledge sources, content quality requirements, templates, and evaluation practices. Establish centralized ownership for shared capabilities, critical integrations, and systems that introduce operational, compliance, or reputational risks.
  • Govern according to risk, reach, and reliance. As more marketers become builders, new challenges emerge in duplicate agents, inconsistent quality, rising costs, and maintenance problems when builders leave. One organization described a tiered governance model in which individuals could freely build personal productivity agents, team-wide agents required registration and ownership, and organization-wide agents received central stewardship. This is a useful starting point, but consider risk, reach, and reliance when deciding how much oversight an AI workflow requires. Evaluate what could go wrong if a workflow produces an inaccurate or inappropriate result (risk), how broadly its outputs or actions affect the organization and its audiences (reach), and how much other people or processes depend on it (reliance). Use those factors to determine requirements for testing, approval, ownership, monitoring, and maintenance.

Reinvest Human Effort In Creating And Sustaining Content Value

Content work illustrates why these operating model decisions matter. As AI makes content faster and cheaper to produce, leaders must rethink where human expertise adds the most value and who is accountable for content quality and performance. A MAICON keynote on AI-native content teams reinforced the importance of developing original inputs AI can’t independently produce: distinctive expert perspectives, proprietary research, customer insights, lived experience, and original ideas. These are the foundations of content worth creating and scaling.

  • Protect the work that makes content worth creating. AI can help marketers prepare to interview a subject matter expert, analyze research data, develop assets, and adapt content across formats and channels. But the most valuable inputs still depend on human expertise. Producing more content isn’t the answer to improving AI visibility, credibility, or influence. The more content production becomes automated, the more valuable the work before and after production becomes.
  • Treat content as an investment that requires ongoing care. Before creating anything, source expertise, develop evidence, and determine what audiences genuinely need. After content is created, verify its quality, monitor its performance, and improve the content over time. This is becoming a practical leadership opportunity. In recent conversations, a CMO and a chief digital officer described requiring employees to stand behind the content they create, assigning clear owners, and moving away from a “publish and move on” mentality. Both described efforts to treat content more like a product or investment that requires ongoing stewardship.
  • Maintain critical company, product, and customer knowledge. AI-native content teams depend on trusted knowledge that people and agents can continuously access and reuse. Assign owners to keep product information, approved claims, customer insights, and other critical knowledge accurate and current. As agents create, adapt, and update content at scale, outdated or inconsistent source information can spread across many outputs. Maintaining that knowledge becomes an essential part of content governance.

Build For Today’s Productivity And Tomorrow’s Expertise

One unresolved question deserves attention. In his opening MAICON keynote, Paul Roetzer raised a sobering dilemma: as AI takes over the work through which early-career marketers traditionally developed expertise, how will the next generation acquire the judgment needed to direct and evaluate AI?

Apprenticeship models may help, but the answers aren’t straightforward amid pressure to increase efficiency and reduce costs. Leaders should consider what employees need to learn from the work they’re accelerating and automating.

For now, marketing leaders can make three consequential decisions: where should teams have the autonomy to build, what knowledge, standards, and capabilities should be shared across marketing, and who is accountable for the quality and ongoing value of AI-enabled work. The answers will determine whether AI delivers disconnected productivity gains or a more effective marketing operating model.

If you’re deciding how to scale AI workflows while maintaining quality and control, contact us. We can help you assess your current approach, prioritize opportunities, and establish the foundations for responsible scaling.

 

Share