· 9 minute read
Content Teams Don't Have a Speed Problem
Why faster production leaves the hardest editorial problems untouched.
I can take an essay from idea to published in a few hours. I talk through my thinking with Claude Code, it drafts, I refine, and it handles the HTML, design, formatting, and publishing. No CMS. No design handoff. No upload workflow. Just thinking and shipping.
So why did the editorial team I led still take weeks on a single article while using the same AI models?
For months, I couldn’t explain the difference. We use AI. Everyone on the team uses it in some form. But production hasn't 10xed. It hasn't even 2xed. I had this quiet “should” feeling: we should be further ahead, we should be publishing more, we should have figured this out by now. Every content leader I talk to carries a version of the same anxiety.
The slow part of serious editorial work isn’t drafting. It’s deciding what the piece should say and gathering enough experience to support it. Speed still matters. Publishing quality content at pace is how you build a content library that compounds. But most of the tools we’re reaching for are designed to speed up production.
Why engineering teams shipped 10x faster
I have a friend who's a senior engineer at an AI platform. He works enormous days and he'll freely tell you his output has increased by an order of magnitude. Hundreds of pull requests per month. His company measures developer productivity by tokens consumed. They use it as a signal of how much implementation work is flowing through AI rather than being typed by hand.
His CEO mandated that no one propose a problem without first considering how to build an agentic workflow for it. The entire culture shifted around one question: what can be automated so humans focus on what can't?
When I hear stories like this, my reaction is envy. We have the same AI models. We have content guidelines, quality criteria, style documents. Why can't we ship at the same rate?
Here's what's easy to miss: most of what got faster was implementation. The code itself. The translation of a design decision into working software. An engineer still decides what to build, how to architect it, which tradeoffs to accept. That judgment hasn't been automated. What's been eliminated is the hours of typing, debugging, and boilerplate between “I know what this should do” and “it works.”
AI removed much of the implementation work. Engineers still make the important decisions.
In engineering, a huge percentage of the work was implementation. Compressing it created enormous gains.
Content has a different ratio.
Why the same playbook doesn't transfer
When you sit down to produce an article, what's actually hard? Not the typing. Not the formatting. Not even the research, most of the time.
What's hard is the through-line: the argument that organises everything and makes the piece worth reading. It's the expertise that comes from interviewing someone who's done the work, or having done it yourself. It's the editorial judgment to know what to cut, what to expand, and where the piece is actually saying something.
AI can speed up parts of this work, but it can’t make those editorial decisions for you.
A journalist mate told me his anxiety about AI eased when he realised something obvious: ChatGPT can't interview people. AI can speed up the tasks around reporting, but it can’t build source relationships, ask the right follow-up, or earn trust.
This also explains why my solo workflow is so much faster than my team's. AI handles the design, formatting, CMS, and publishing, while I make all the editorial decisions. A team adds more voices to align, more review steps, and more legal and brand considerations. That coordination becomes a bottleneck that AI can’t really solve. The challenge scales with team size.
| Context | AI speed gain | Real bottleneck |
|---|---|---|
| Solo creator | High | Your own thinking |
| Small team | Moderate | Aligning on angle and quality |
| Mid-size editorial | Lower than expected | Coordination, reviews, expertise |
| Enterprise | Lowest | Legal, brand and stakeholders |
So why do we all feel behind? AI was sold to content teams as a way to produce drafts faster and increase output without hiring more people. And sure, you can generate a competent first draft in minutes. Many of us dream of one-shotting a publishable piece with the right prompt, or at least generating an AI-assisted draft that just needs “some” editing. That’s getting better. But for teams with real quality standards, even the best AI draft depends on decisions made before writing begins: the angle, the evidence, and the editorial goal. Prompt engineering alone doesn't substitute for editorial strategy.
Competent first drafts were never the constraint. The constraint was always: is this piece actually saying something worth remembering?
When you hold your work to that standard, AI doesn't make it dramatically faster. It speeds up production and makes the remaining editorial work easier to see.
What's actually working
I don't have a clean solution. Anyone who tells you they do is likely selling something. But I can share what's emerging on our team, because the pattern is instructive even if it's messy.
We rebuilt our quality criteria to assess editorial judgment. We used to score content against a list: engaging introduction, visual break density, proper formatting. Writers could tick every box and still produce something helpful, but forgettable. The piece scored well without being transformative.
So we rebuilt the criteria around:
- Does this piece have a clear through-line?
- Is there genuine expertise from someone who's done the work?
- Does the piece create a clear before and after for the reader?
- Is the credibility earned, not assumed?
This was counterintuitive. We raised the bar on human judgment at the same moment we were trying to speed things up. But it forced a useful clarification: the quality standard defines what humans need to contribute. Everything else becomes fair game for AI assistance.
The team built their own tools and shared them organically. One writer created a custom GPT loaded with our quality criteria. She used it to evaluate drafts before submission, then shared it with the team. Another built a skill for generating custom graphics in our brand style, something that used to eat hours in design tools.
Nobody mandated these. They emerged because the team had a clear framework for what quality means, and individuals found their own ways to handle the operational work around it.
Writers, editors, and strategists began working together earlier. This surprised me most. As the criteria shifted toward packaging and expertise, the process started looking less like a production line and more like a writers' room. More calls between editors and writers. More workshopping of angles before drafting begins. More input from strategists on what the content library actually needs.
We’re investing more at the start of the process in creative strategy, angle development, and expert partnerships. AI picks up more of the style conformance, formatting, and research compilation later.
But this only works if collaboration has a defined place in the workflow.
If your team is co-located, treat briefing like a writers' room. Get the strategist, writer, and editor in a room together before the outline exists. If you're distributed, this takes more planning: scheduled cross-timezone calls during outlining, an automated Slack thread that pings everyone involved when a piece enters ideation, and AI transcription so insights don't disappear after the call.
- Ideation sessionWriter, editor and strategist workshop the through-line.
- Expert outreachGather first-hand insight. AI transcribes and summarises.
- Outline and angle reviewCheck the argument before drafting begins.
- Draft and AI passAI helps with style, research gaps and formatting.
- Editorial reviewFocus on argument, credibility and transformation.
- Production and publishAI handles final QA, assets and formatting.
The goal isn't to slow down. It's the opposite: front-load the creative collaboration so that drafts arrive with a stronger through-line, fewer revision cycles, and less back-and-forth later. Speed and quality aren't in tension when the thinking happens at the right stage.
The split that actually matters
A pattern runs through all of this. AI is much better at some parts of the work than others.
Invest human energy here
In engineering, the infrastructure that enabled 10x gains was technical: centralised APIs and unified tooling. For an editorial team, the equivalent is a shared definition of good work and a process that brings people together before drafting.
Once that foundation is clear, people can choose tools that remove routine work.
Automate formatting, research compilation, and QA. Spend the saved time on angles, evidence, and editing. The useful question is how to restructure the workflow so people spend most of their time on work that only they can do.
The real advantage
Teams shouldn’t expect every kind of work to speed up equally.
The value of editorial work comes from the decisions behind the output. Engineers are discovering this too, as AI handles more implementation and architectural thinking becomes more important. Editorial work has always depended on the through-line, the expertise, and the change it creates for the audience.
AI has made those decisions more visible by removing some of the routine work around them.
If your team hasn't 10xed output, that doesn’t mean it has failed. The hardest parts may still require human judgment. You can support that work with tighter collaboration, clearer quality standards, and AI handling routine tasks.
The teams that figure this out will be fast and good. They'll build content libraries that compound because what they publish is worth building on.