Contently: the managing editor role is what AI-era content teams are missing

What the article is about

Uba Alintah’s piece in Contently addresses a structural gap that has opened up in content teams as AI tools make production faster and cheaper. When one person can generate six articles in the time it used to take to write two, the work that controls quality — maintaining voice consistency, deciding what should and should not be published, preserving brand standards — becomes the actual constraint. The article argues that most teams have not reorganized around this shift, and that the missing role is a managing editor, not a content manager or AI operator.

Context

The article comes in the context of widespread AI adoption in marketing and editorial teams. Contently has tracked content production trends for over a decade, and this piece reflects patterns the platform has observed across client organizations: teams that increased output dramatically with AI but began seeing brand voice drift, inconsistent quality, and declining content performance as a result. The article is not against AI use — it is specifically about how to govern AI use at scale.

Key takeaway

The managing editor role as defined in the article is distinct from what most organizations call a content manager. A content manager typically oversees schedules, assignments, and output volume. A managing editor does something different: they define what quality means for each content type and audience, and they apply that definition consistently across everything the team produces.

The article breaks the role into six functions. Setting publishing standards means defining the benchmark against which all output — human-written or AI-assisted — is measured. Protecting brand voice means catching voice drift early, before it becomes systematic and hard to reverse. Making AI integration decisions means determining where AI drafts are appropriate and where human authorship is required — a distinction that cannot be made once and then automated, because it depends on content type, audience, and context.

The fourth function is maintaining institutional knowledge: retaining understanding of what has resonated historically with the audience, what the organization has committed to publicly, and how editorial standards have evolved. The fifth is translating standards into operational briefs for writers, editors, and AI tools — not just writing guidelines that no one reads, but specific instructions that can be followed at the task level. The sixth function is final editorial judgment: the authority to decide what publishes and what does not, independent of production timelines or approval pressure.

The core argument is captured in the article’s framing: when production is cheap, the pieces that never see the light of day do the real work by protecting the quality of what does get published.

Who should read this

Marketing and editorial leaders who are scaling AI-assisted content production and beginning to see quality or voice consistency problems. Also relevant for senior content practitioners who want a framework for making the case that editorial oversight needs to be resourced, not reduced, when AI increases output volume.