When our blog team made the switch to an AI-first writing model in June 2025, my reaction was split. There was excitement on one side: I could see the inefficiencies in our process that AI might actually fix. On the other, anxiety. And while plenty of that anxiety was about the technology itself (how well these tools could actually write, how much we could trust them), my primary concern was my team.
I’ve managed HubSpot’s blog writing team for three years, and before that was a HubSpot Blog writer myself. I understand what it means to have ownership over your process. Many of the writers I manage got into this work because they love writing. They enjoy the craft of finding the right sentence and earning a reader’s attention. Traffic numbers and conversions are secondary. During this AI transition, I felt the weight of what it could mean for those whose entire approach to their craft was changing fast.
So much of the public conversation about AI content gets the story wrong. There’s an assumption that because AI can write passably well, the hard part is over. Just apply the same low-effort polish you’d apply to an AI-drafted email or memo to your blog post, then ship it.
But I can tell you from experience: it doesn’t work that way. A blog post is meant to compete against dozens (or hundreds) of other posts on the same topic. In this game, generic, low-effort writing will not win, and that’s good news for your content team. It means that even though the writing process has changed, the craft remains important.
So, how do you balance craftsmanship with the efficiency gains AI promises? And how do you build a process that takes advantage of what AI is actually good at without falling into its traps? We spent the last year figuring that out. Here, I’ll share what we learned: where time was saved, where it got added, and what it takes to make AI work without compromising the quality standards you’ve spent years building.
Table of Contents
Before AI, writing was personal.
What We Learned From Letting Writers Lead
Comparing Pre-AI vs. Post-AI Writing
AI content at scale is easy. AI content worth reading is hard.
What This Means for People Managers
My Takeaways as a Content Team Manager
Before AI, writing was personal.
Before AI, our writing process was, well, a writing process. While we had editorial standards and guardrails to ensure consistent quality, we didn’t enforce a specific method for how posts should be written. We hired writers and editors for their ability to craft, and we trusted them to use that ability. We understood that writing is more than just a technical skill. Giving our writers the space to develop their own individual workflows yielded content with personality and unique insights.
When training new writers, we taught a simple, flexible framework to help folks start to structure their time:
Outlining and planning took around 30 minutes to an hour.
Research could take one to two hours, depending on the complexity of the topic and the writer’s familiarity with it. This included sourcing quotes, finding original data, and writing from personal experience.
Drafting, the part where someone actually puts words to the page, took around five to seven hours on average for a first draft.
Editing, QA, formatting, and finalizing in our CMS added another roughly two hours on top of that.
For a standard blog post, you’re easily looking at a full day’s work, usually longer. The bulk of that time lived in the drafting phase — writing the words, finding the right structure, and figuring out how to explain something clearly was the core of the job.
What We Learned From Letting Writers Lead
When we formally adopted AI-assisted writing in June 2025, my approach was deliberate: don’t over-prescribe. Allowing our writers to develop their own processes had served us well for over a decade, and we wanted to maintain that sense of control for our writing team as much as possible, even as we added AI to the mix.
We gave writers access to AI tools (specifically Claude and ChatGPT, along with NotebookLM) and space to figure out how to work them into their process. Even with AI, I believed writing is personal, and I wasn’t going to tell a team of experienced writers exactly how to do their jobs. Our goal was experimentation first, structure later.
What followed was not the clean efficiency win I expected. AI solved some problems and introduced new ones. But by working through those problems with our writers, we built a process that we know works.
Faster drafting doesn’t mean faster content.
At first, the signs were good. Writers were putting words on the page faster than before, and I assumed that would translate directly into faster posts overall.
It didn’t.
A couple of months in, after reviewing time tracking data and having conversations with my team, a more complicated picture emerged. The quantity of posts stayed about the same, but posts were getting longer, more detailed, and more demanding to produce. Writers were spending more time testing software, sourcing expert quotes, reviewing tools in-depth, and going deeper on topics. The time AI saved in drafting was being absorbed by everything else a competitive post requires.
What made it harder was that some writers felt the pressure of both expectations at once: write faster and write more. When I understood that this was happening, I had to recalibrate. AI hadn’t decreased the workload. In many ways, it had increased the workload.
So my first lesson was: If you’re measuring AI’s impact on your team purely by post count, you’re going to be disappointed. The win is in what those posts become, not how fast they get there.
Research was our biggest surprise.
I also assumed AI would make research faster. After all, instead of spending an hour getting up to speed on a topic, a writer could ask Claude and move on.
For general topic knowledge, that’s actually true. On well-documented subjects with plenty of training data to draw from, AI can explain concepts clearly, answer follow-up questions, and get a writer up to speed faster than navigating a dozen browser tabs.
The shortcomings, however, were flagged by writers almost immediately.
The first time it really surfaced was on a listicle covering competitor products. A writer came back to me flagging that the pricing information AI had surfaced was wrong. Not slightly off, but fully outdated. AI didn’t know the pricing had changed, and it didn’t flag its own uncertainty.
I probably should have seen that one coming. After all, a tool trained on data from months ago obviously can’t know that a competitor changed their pricing. But it took a writer caring enough to flag it before I accounted for it.
From this, we learned that for anything requiring accuracy about competitor pricing or features, AI research falls apart. This makes sense when you think about how these tools work: An LLM’s training data has a cutoff date, meaning it’s unlikely to reflect the latest pricing changes, feature updates, or product rebranding. And when AI tools do attempt a live web search, they’re only as good as the pages they can parse. Product pages are often structured in ways that are difficult for an LLM to interpret, which makes even real-time lookups prone to inaccuracies. The result is information that sounds authoritative but may be months or years out of date.
We produce a lot of competitor listicles and product-focused content, and for those, research still requires significant manual effort. So while AI has sped up general-topic research, competitor- and product-focused research takes roughly the same amount of time as it always has.
The Hallucination Problem
The research problem ran much deeper than product pricing alone. Beyond outdated competitor data, we were seeing fabricated statistics, misattributed quotes, and incorrect terminology, all delivered with the same confident, authoritative tone. AI doesn’t know what it doesn’t know.
We place a high premium on accuracy, and these so-called AI hallucinations turned fact-checking from a light final step into one of the most time-consuming parts of the process. In some cases, AI drafting took longer than manual drafting because writers had to spend time verifying what the AI got wrong.
What struck me was how quickly my team caught it. That commitment to publishing only truthful, verified information is exactly what separates a good writer from a content generator. It reinforced something I already believed: the writer matters more than the tools they use.
To address hallucinations, we introduced a new formal step in our process: dedicated fact-checking. Pre-AI, writers were responsible for the accuracy of their own research, and a final editorial pass would catch anything obvious. With AI in the mix, fact-checking became its own distinct phase. For a typical post, I estimate it adds around 30 to 60 minutes to the process. It’s longer for research-heavy pieces or anything involving stats, quotes, or product information.
Sure, you could skip fact-checking and assume AI gets everything right. But is that really the standard you want to hold your content to? Publishing misinformation, even unintentionally, damages trust in a way that’s hard to recover from.
Quality doesn’t police itself.
QA time grew too, for reasons beyond what fact-checking was already catching. Fact-checking is the process of verifying that the information in a post is correct. QA is about everything else: Does this post sound like a person wrote it? Does it meet our editorial standards? Is it structured in a way that search engines and AI tools can parse?
Without a structured quality layer, AI-assisted drafts introduced problems writers wouldn’t necessarily catch on their own, such as:
AI clichés and hedge-word-heavy phrasing (“it’s worth noting,” “leverage,” “it’s important to understand,” etc)
Redundant sentences.
Plagiarism from competing content (not intentional, but from models quoting their source material directly in the output)
Broken or faulty links
Missing image alt text
Incorrect heading orders
Sentences, paragraphs, and sections that aren’t optimized for LLM chunkability.
To address this, I built a custom QA checker directly into Google Docs that flags these problems and more. It’s become a required step in our workflow.
What AI Didn’t Touch
Through all the changes above, some things didn’t move. The E-E-A-T requirements we had before AI — original quotes, firsthand perspectives, solid data sourcing — stayed intact. Editorial judgment stayed intact. The strategic thinking that goes into deciding what a post needs to say, and who it needs to say it to, stayed intact.
AI handles much of the execution now: drafting, scoping, generating topic angles, and explaining concepts. But the layer of work that makes a piece worth reading (the human perspective, the original insight, the rigorous sourcing) is still on the writer. If anything, it matters more now because it’s what separates your content from everyone else’s AI output.
Comparing Pre-AI vs. Post-AI Writing
To understand what changed, it helps to see it. Here’s a rough breakdown of how time was distributed across a typical blog post before AI, and how that distribution shifted after we adopted it:
Note: Time estimates are averages based on HubSpot Blog Team internal tracking. Individual posts vary based on topic complexity, length, and content type.
Of course, the reality varies more than these charts suggest. A writer might draft for an hour, realize they need more research, go back to drafting, catch something that needs fact-checking, and return to drafting again. A real workflow looks more like this:
Still, the time savings are evident.
AI content at scale is easy. AI content worth reading is hard.
If AI can generate a blog post in minutes, why not just publish and move on? More content means more chances to rank, more surface area for leads, and more output for less effort, right?
Early in the AI content boom, we all noticed a certain strategy emerging in the SEO space: Publish as much AI-generated content as possible, as fast as possible, and capture market share before competitors caught on. Flood the zone. It operates on the belief that volume alone would be enough to win.
Except, it isn’t, and the consequences caught up.
Slop at Scale
The content that comes from such a fast-and-loose strategy is recognizable to anyone who’s spent time in this industry.
A blog post that strings together sentences without saying much. A listicle that could have been written about any company in any industry. No original data. No real point of view. No specific examples that couldn’t have been fabricated. And if you run it through a plagiarism checker, you’ll even find content that was lifted from competing sources (not intentionally, but as a byproduct of how LLMs work).
In short, it’s generic. You’ve probably heard the term “AI slop” to describe this kind of content as well. Experienced readers and, more importantly, search algorithms can spot slop from a mile away, and it hurts far more than it helps.
Marketers are catching on. According to HubSpot’s 2026 State of Marketing Report, 52% of marketers believe AI has made content so easy to create that it’s become less effective overall.
Need more proof? In 2026, SEO expert Lily Ray tracked more than 220 websites that identified themselves as customers of AI content creation platforms and found a consistent pattern: rapid growth in organic traffic, followed by a steep decline that, in many cases, erased the gains entirely. Across her dataset, more than half of the sites lost at least 30% of their peak organic traffic, and nearly a quarter lost 75% or more.
Here’s an example of what that looks like for one such website.
I understand why teams took to this approach at first. The pressure to produce is real: 83% of marketers say they’re expected to create more content than ever because of AI.
But this is exactly what leading only with speed looks like in practice. If quality becomes something you try to retrofit after the fact, it rarely works.
Start with standards, then add AI.
The teams doing AI writing well start from the opposite end. They define what “good” looks like — voice, depth, accuracy, originality — and then work backward to determine where AI can help them get there faster without compromising on those standards. All writing is anchored in quality from the beginning.
On the HubSpot Blog, that means keeping our E-E-A-T requirements intact. We build quality guardrails into our workflow and are honest about which content types benefit from AI involvement and which don’t. For example, a how-to guide and a piece of original research will have different requirements, and treating them the same way will result in generic content that goes unnoticed.
As former Director of the HubSpot Blog Karla Hesterberg puts it: “The only truly effortless process produces slop.” Teams that resist the slop, keep good writers in the driver’s seat, and use AI as a lever rather than a replacement are the ones producing work that converts.
For a more thorough dive into how we maintain quality standards in an AI-assisted editorial process, check out this post from HubSpot Blog writer Amy Rigby on how HubSpot creates quality content in the age of AI.
What This Means for People Managers
Almost overnight, my role shifted in two directions at once. On one side, I became more process-oriented than I’d ever been. I had to think more deeply about workflows, tooling, and how posts get assembled. On the other, people management got harder. Not because my writers were difficult, but because I was asking them to change how they work.
Process changes are relatively straightforward to document, but maintaining motivation and morale through a transition that fundamentally changed their jobs is a different problem entirely. You can build the most thoughtful AI writing process in the world, but if your writers aren’t bought in, none of it works.
Writers come to this job for different reasons, and AI lands differently depending on which reason that is.
Some writers are wired for outcomes. They get into content marketing because they’re good at writing and they like seeing the business impact. For them, AI is a welcome change because it gets them to the result more quickly.
Other writers came to this work because they love the craft. The writing process is something they care about deeply. For them, an AI-first model can feel like it’s taking something away. I don’t see this as a performance issue or a failure to adapt. It’s an internal tension that should be taken seriously.
As a manager, my job is to recognize where each team member falls on that spectrum and work with them accordingly. I create a space for honest feedback, including negative feedback about AI. I know that if writers don’t feel comfortable telling me what isn’t working, I’ll build processes on bad information. The writers who most honestly pushed back on our early AI workflows gave me the clearest roadmap for what needed fixing.
Steps for Managing Your Team Through the AI Transition
Here’s what I’d tell any manager navigating AI writing:
Don’t over-prescribe early. Give writers access to tools and room to experiment before locking in rigid workflows.
Mandate experimentation, not method. The teams that prescribed specific AI usage from day one created unnecessary friction.
Recognize that you’re asking writers to change how they work. That takes time and trust.
Create space for honest feedback, including constructive feedback. If writers don’t feel safe telling you what isn’t working, you’ll build processes on bad information.
Understand where each writer falls on the spectrum between outcome-driven and craft-driven, and manage them accordingly.
The goal hasn’t changed: a team that produces work worth reading. The path to get there just looks different from what it did in 2024.
My Takeaways as a Content Team Manager
A year into this transition, I’m proud of where we landed, even though we’re still figuring some things out.
What made the difference wasn’t only the tools, but the writers. I knew that going in, and this past year confirmed it at every turn. The writers who pushed back honestly gave me the roadmap, and the ones who struggled helped me understand what was actually being asked of them.
What sticks with me most from this experience is something writers have started saying recently: that they’re feeling like real writers again. The balance between AI efficiency and actual craft has started to feel sustainable. That happened not because we found a perfect prompt or the right tool, but because we never let go of what made our content good in the first place.
If you’re an editorial lead navigating the AI shift right now, know this: What gets you through is having writers who care enough to be honest with you, and being a manager who creates space for that honesty. Don’t underestimate what it means for a writer to feel like a writer. It shows up in the work.
AI is a tool. Keep your writers at the center, hold the quality line, and the rest follows.