An efficient content system is a boon to any marketer, no matter the size of their operation.
That’s why I’m bullish on content engineering.
It’s a buzzword right now but I think it’s here to stay. Engineers are all about efficiency and, these days, they’ve got a lot of amazing tools to employ.
“So should my company hire a content engineer this quarter?”
Clients have been asking this question more often. Here are my thoughts on how, and when, to bring a designated content engineer onto the team.
TL;DR
- A content system is more important than a content engineer.
- You can build a content system right now without any new budget lines.
- Engineers don’t turbo-charge AI, they find the best use cases for it.
- We use gradations of AI assistance for different content tasks.
How I talk about content engineering with my clients
Content engineering is really just an outgrowth of marketing automation. And marketing automation is nothing new.
For example, I use this very simple HubSpot workflow for lead captures on the ércule website.
When someone fills out a lead capture on our website, this workflow sends them an email and sends us a Slack notification. We set some life cycle stages. Stuff like that.
Thanks to AI, this type of automation is now available to people who create marketing content of all formats:
- I publish a piece of content and AI turns it into an email.
- I write a piece of content and AI adds relevant internal links.
- I distribute a post on LinkedIn and automatically receive updates about its performance.
- I find outdated pages on my blog and AI automatically revises them.
That's the real magic of content engineering, for me. But engineering content like this requires a systems-based approach to your content program. Without a content system, there’s nothing to engineer.
What I mean by “content engineer”
A content engineer thinks of content not in terms of individual pieces but the systems that produce those pieces.
A content system includes:
- Infrastructure (i.e. tools for research, production, and data analysis)
- Strategy (i.e. campaign creation, platform-specific strategies)
- Implementation (i.e. content production + distribution).
- Maintenance (i.e. making sure all pipelines are flowing).
- Development (i.e. continuous improvement of inputs, outputs, and user satisfaction).
All of this is in service of a company’s specific marketing goals, of course. It’s the content engineer’s job to make sure that the system is meeting those marks.
To succeed, a content engineer will need real marketing chops. This isn’t just a technical “marketing ops” hire. It’s a new hybrid role.
What a content engineer does
It’s a hybrid role: the content engineer manages marketing ops, strategy, and creative output.
So they handle all of the strategic and editorial workflows that content marketers have been doing for years: orchestrating campaigns, conducting competitor research, managing creatives, setting timelines, aligning content with product.
And with the help of smart tools, the content engineer also manages more technical marketing tasks:
- Growing SEO and GEO.
- Updating existing content.
- Running technical audits of the website.
- Building AI infrastructure and scaling content generation.
- Analyzing performance metrics.
I see this as an advantage for anyone who’s currently focused on content strategy. Content engineering gives you a lot more tools to ensure high content performance. The burden of success no longer rests on the acts of campaign choices and edits. You can put data and automation to work for every piece of content as well.
The wrong reasons to hire a content engineer
There’s one narrative about content engineering which focuses exclusively on production volume and automation. I think it’s misled.
In that volume-first narrative, the engineer’s job is something like…
- Pump out the highest volume of content possible.
- Eliminate the need for human writers from the budget.
- Jam AI tools into every aspect of content production.
Here’s the problem with that approach: none of those goals are focused on actual marketing goals. Besides, “scaling” is much more nuanced than simply saying, “make more content.”
You can’t scale a content system sustainably on output goals alone. You have to scale quality mechanisms, too. Otherwise, the content will flail and the system will eventually break.
Real reasons to hire a content engineer
The fundamental reason is simple: you want to produce better content, more sustainably, and (yes, of course) in a more scalable fashion.
So let’s forget about the production growth for a minute. Take a more nuanced look at what engineers can do and what it means to scale. You might hire a content engineer if…
- You want to experiment more with your content. Experimentation is vital to any marketing team! And expanding your experimentation capabilities in a meaningful way requires some thoughtful growth. In other words: scaling. A smartly engineered content system can test messaging variations, analyze what resonates, and implement improvements across hundreds of pieces of content.
- Content quality is a top priority. This is another systemic issue. Improving output quality is fundamentally an engineering task. AI tools are mighty useful here. As Josh Spilker from AirOps put it, “Let’s think about the other tools that have helped with ‘quality assurance’ that have made us better writers, creators, and SEOs. Like a spellchecker.”
- You think humans are the best writers. I agree. AI can’t replace the work of a brilliant writer. But, as AI tools become table stakes for marketing teams, they can handle the rote content tasks that would otherwise eat away at the work days of your writing staff.
The engineer’s job is to establish a more efficient baseline for your content systems. This frees up your in-house creatives to focus on original work that generates real competitive advantage.
Content engineers don’t worship AI, they calibrate it
The content engineer’s job is to employ every tool available in the most effective way possible. In that sense, AI is just another tool.
Yes, the rise of AI is what makes “content engineer” a particularly promising role for marketing teams. But getting the most out of AI doesn’t mean jamming it into every process. It means using AI where it’s actually useful.
Let’s look at three examples of calibrated AI integration:
- 100% AI-produced content
- LLM-assisted content, refined by human writers
- 100% artisanal content written by humans
One piece might require a ton of hands-on editorial attention and a dash of AI for distribution. Content engineering is about deciding when, how, and how much to use AI.
Purely automated content creation
This is the turnkey level of automation, which is often (justifiably) maligned.
Scenario: you’re spinning up content strategies from scratch.
Either you’re a brand new company or you’re an established company trying to break into a new niche. There are a ton of relevant topics that you need to be writing about.
The goal: publish now, refine later.
Organic content is a long game. The content you publish today may not bear fruit for months. So the goal here is publication, not perfection.
The method: automated research + writing.
Let’s look at a simple example of this in the AirOps interface.
This workflow does deep research on a given topic, writes articles on all those topics, finds all the FAQs related to that article, then adds some of its own to the article. After that, the AI can even publish directly in your CMS.
The risk here is obvious: you could end up producing a lot of slop this way. But, with proper quality assurance mechanisms, you could also end up covering a lot of topics in search. These are quereies that you wouldn't otherwise be able to cover..
LLM-assisted content creation
This is a hybrid approach. Automation is used to deliver rough drafts that writers can use to create original content.
Scenario: you’ve already built some authority for a given topic but you need to expand the scope of your coverage in search.
The goal: scale up content velocity.
Your brilliant in-house writers have already published some content about topics that are central to your brand strategy. You’ve established a point of view. The goal now is to increase the number of posts that elaborate on that point of view.
The method: curated inputs for automated first drafts
Rather than letting the LLM start researching entirely on its own, provide brand materials and your own research documents to guide the automation process. Then hand the drafts over to your real (human) writers. This way they’re not starting every new post from scratch.
When we generate an LLM-assisted post for our clients, we work with them to cultivate the input material – everything from brand strategy docs to whitepapers they’ve written to social posts written about similar topics.
We run that through the LLM workflow and the first draft output is… fine. Not amazing! But this is a first draft.
It absolutely requires some editing. But this first draft was generated in 10 minutes. It saves our writers hours – sometimes days – that they used to spend in the early research and outlining stages.
100% artisanal content
Content composed in the classic style: giving a thoughtful editorial assignment to a very smart, knowledgeable writer. No AI involved.
The scenario: you’re trying to stake your claim to a competitive topic.
Only the best will do. You need a unique narrative angle, a robust argument, and an irresistible voice on the page.
The goal: winning attention and building trust.
In other words, the goal here is to create a piece of content that really sets you apart from competitors and lays out your company’s point of view.
The method: damn fine (human) writers.
Human writers are irreplaceable, in my opinion. And I say that as a marketer who is extremely bullish on AI and content engineering. But AI actually plays a role here, too.
For example, look at this post drafted by one of our writers: “DRY infrastructure as code on AWS”.
It’s leagues better than the AI-generated draft I mentioned above. The difference is immediate to me and, I believe, to most readers.
And this content mingles with AI workflows in two ways:
- We use artisanal content as an input for AI workflows. Original source material like this is crucial to creating decent content with LLM assistance.
- AI automation frees up the writers to work on ambitious content like this. Writers have more time to prioritize high-ROI content like this when lower-priority content tasks can be automated.
Content engineering plays a huge role here. The content engineer decides which pieces of content warrant some automation and which ones deserve the most human attention.
Content engineering lets you automate what you can automate. That’s all. (But that’s pretty huge.)
The engineering framework is more important than the job title
Do you need to hire a content engineer? I don’t think so. Not right away.
But here’s a change to make right away: start thinking about your content as a system. Even if you only publish one post per month, a systems framework will help you improve your whole operation.
Content marketing is going to get more and more competitive as these automation tools become smarter and more accessible. The sooner you start incorporating automation, the sooner you’ll start adapting to that new climate.
If you have a large enough team (and budget) and you have a spot for a content engineer, that’s great. I say: go for it. Building that new role could help you learn a lot.
But refining your content systems in the first place doesn’t require a devoted job title. It requires a shift in frameworks. A big reset is coming for the marketing world. You can get ahead of it.
