♾️The Content Stack Debt — and how to fix it

You’re reading The Content Loop — a 5-min read on how B2B SaaS marketers can use original research and product-led content as a growth lever.

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I’ve been AWOL for the past two months—and for good reason. I had to move to another city on short notice, and let’s just say things got real chaotic 😅 

But the upside was that I’ve had a lot of time to think about where I want to take my business and this newsletter.

So here’s what’s next:

I’ll now be publishing two issues a week—on Mondays and Thursdays—covering topics like content strategy, product-led content, and original research.

You can expect deep dives into what’s working in B2B SaaS content, tactical breakdowns of real strategies I’ve used (or seen done well), and interviews with content (marketing) leaders you’ll want to learn from.

If that sounds like your jam, I’d love for you to stick around. If not, no hard feelings—unsubscribe anytime.

Now, let’s get into this week’s issue with a brilliant content leader 👇

Most content teams have a dirty secret they won't admit: they're drowning in AI tools but still can't ship content any faster.

I've been talking to marketing leaders all year, and the pattern is everywhere. Teams with subscriptions to Claude, ChatGPT, Jasper, Copy.ai, and whatever else launched this month. 

Everyone's excited about the productivity gains.

But somehow, they're still stuck with five-reviewer approval processes, shadow workflows that bypass official systems, and content that takes just as long to ship as it did before AI existed.

The problem isn't the tools. It's what we call “tech bloat”—buying solutions without understanding the processes they're supposed to fix.

That's why I sat down with Julia Porter, Optimizely's senior director of content marketing, to understand how content teams can realistically overcome this problem.

That insight kicked off something most content leaders talk about but never actually do: a systematic audit of how work really happens versus how they think it happens.

Here's how Julia diagnosed her team's “Content Stack Debt”—and how she’s fixing it.

The mess hiding in your workflows

When Julia started mapping her team's actual workflows, she discovered something that might sound painfully familiar.

This is exactly what I've seen at other companies. Official processes look clean in Notion or Monday.com, but there is a messy reality where people create workarounds because the "official" way doesn't actually work.

Everyone sees AI as a way to create content faster. So they layer ChatGPT and Claude on top of broken handoff processes. But the problem? More content gets produced, but the same bottlenecks slow everything down.

That's why Julia flipped the question. Instead of "How can AI help us create more?" she asked, "Where are we actually getting stuck, and what role should AI play in fixing that?"

That shift led to something most content teams skip entirely: documenting reality before trying to improve it.

Auditing the AI content stack—with the right guardrails

Julia knew that a comprehensive workflow audit could easily become a six-month death march that everyone dreads. So she designed something intentionally constrained: 30 days, four focus areas.

She calls it auditing your "Content Stack Debt"—a concept she developed that perfectly captures what happens when we’re collecting AI tools like kids in a candy store.

The four areas became her framework:

• People: Who's doing what, and where are the gaps 

• Process: How work flows between team members and stakeholders

• Platforms: What tools are being used, where teams are doubling up, and which gaps matter 

• Prompts: How individuals are using AI and where collaboration could amplify results

When you’re involving people as one parameter of the audit, it’s easy for them to feel like they’re under the chopping block. It’s one of the reasons Julia didn't position this as a performance review. 

Getting buy-in meant involving key stakeholders from the start. She worked with campaigns teams, product marketing, brand, and creative to understand what each group cared about. 

Then, she planned to give them personalized views of the results.

This stakeholder mapping is something most content leaders skip. But that's why Julia's framework will be implemented instead of gathering dust in a Google Doc.

What the audit continues to reveal (and the tactical fixes)

One of the early discoveries from Julia's audit was that their quality assurance process wasn't as tight as they thought—especially for scaling AI-assisted content.

This insight led to two potential changes worth stealing:

Round-robin content reviews

Instead of the same people always reviewing the same types of content, team members are starting to swap pieces for cross-pollination. As a marketer, you know how hard it is to edit your work because you're close to it. This is why she plans on using such a system so that:

• Fresh eyes catch issues that usual reviewers miss

• Knowledge gets distributed across the team

• People are planning to learn from each other's approaches 

Julia also started quarterly stakeholder reviews, using collaborative tools to pin up content selections and evaluate what's working. 

Cross-functional prompt libraries

We're all "stealing" prompts from influencers on LinkedIn—but the real value comes from testing and refining baseline prompts you already know work. To do just that, Julia's team created a tightly focused selection of prompts segregated by use case and shared it across the team.

Her goal is to make sure people come back and reuse, remix, or customize these prompts as needed. It’s an excellent way to scale AI usage—and stress test prompts to make sure you get the expected results.

The collective feedback also gives them data to potentially build AI agents for systematic quality checks down the line. Here's an excellent framework Julia uses to assess how mature your team is in this regard:

Why this approach actually works

Looking back, Julia says the exercise was brilliant because she could focus her mind on what needed to be focused on. 

Start your own content stack audit this week:

• Talk to your team members and map your current workflows (the real ones, not the official ones)

• Interview 2-3 key stakeholders about their biggest workflow-related frustrations

• Audit your current AI tool usage across the team

Once you see the patterns, you'll know exactly where AI can help versus where it's just adding noise.

The best part is that you no longer have to look at the "tool" part of the equation. The tool is only as good as the person (or team) that wields it. 

If you involve the right people and start asking the hard but necessary questions, that’s when you break through the implementation chaos.

This line of thinking allows you to actually implement the tool, not just admire what it’s capable of.

P.S. Liked the issue? Share it with someone who could benefit from it.

That’s all for today! Next week, I’ll walk you through why category entry points matter for product-led content.

As always, if you have any questions or feedback, you can send it here: