The Front Lines
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Aidan McLaughlin.
Senior Director, Content Marketing · Indeed

I lead strategic content operations at global scale, unifying Indeed's narrative across job seekers and employers while building the systems that transform thousands of assets into measurable business impact. I drive establish measurement frameworks, design workflows that eliminate duplication, and orchestrate cross-functional collaboration across Marketing, Creative, PR, Economics, and Product teams. Leading a in-house team plus contractors and agencies, I balance innovation with operational rigor to enable consistency at scale.

Guest
Aidan McLaughlin
Senior Director, Content Marketing
Company:
Indeed
Location:
Ireland
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The Content Strategy That Replaced 3,000 Pieces Per Quarter

There's a moment in most content operations when the volume stops compounding and starts working against you. At Indeed, that moment arrived with a 3,000-piece-per-quarter machine running at full capacity — and diminishing returns across the board.

Aidan McLaughlin, Senior Director of Content Marketing at Indeed, joined a recent episode of The Marketing Front Lines to explain what broke, what replaced it, and why the fix required rethinking the relationship between narrative and production from the ground up.

When the Model Breaks

Indeed's content operation was legitimate in scale: YouTube, LinkedIn, SEM, career guides, employer thought leadership, events — each with dedicated teams, each producing constantly. The logic was sound for its era.

"In the SEO world, where search was just the dominant thing you're trying to win, volume trumped quality in a lot of places," Aidan said. "That was sort of the ethos for a long time. But the ground has shifted a little bit, so we had to rethink that strategy."

The shift wasn't purely algorithmic. LLMs changed what discovery means for content at scale — they favor rich editorial, proprietary data, and genuine point of view over optimized volume. And internally, a production system built around asset output had no mechanism for asking whether any of it was telling a coherent story.

The answer wasn't to slow down production. It was to invert the model.

Narrative Architecture Over Asset Output

Aidan helped build what Indeed calls a Content Center of Excellence — eight teams, each owning a specific audience and channel. But the org structure was secondary to the strategic question underneath it: what are the narratives worth building everything else from?

Finding those narratives requires three inputs working simultaneously. First, the commercial objectives — what story does the business actually need told right now. For Indeed, that meant communicating a shift from "the place with all the jobs" to a matching and AI-driven platform, which required different stories for job seekers and employers alike. Second, proprietary data — the signals only Indeed owns that no competitor can replicate. Third, what Aidan calls category entry points: the specific moments in a buyer's journey where they need to hear from you.

"As soon as you put all that stuff down and you write it down and you get teams together and you look at it, really strong narratives emerge from that," he said. "It's clear — the things that knitted together, what are the red threads."

From those narratives, everything cascades. Clips, articles, infographics, social assets — all derivative, all purposeful. "If we can create 3,000 derivative assets from the one big narrative we're trying to tell to the market, that is great. It's like flipping the model."

The operational implication is significant: long-form investments — a podcast, a short documentary, a filmed talk — stop being brand expenses and become content factories. The ROI case changes entirely when the question shifts from "where will this live" to "how many assets can we extract from this before we're done."

The Craft Problem Underneath the Strategy Problem

Volume is a production problem. Narrative is a craft problem. They require different skills and different disciplines — and conflating them is where most content operations go wrong.

Aidan is unusually specific about what separates a narrative from a sequence of well-researched facts. He references George Saunders — whose book on short story craft Aidan returns to regularly — on the non-negotiable requirement: "Once upon a time... and stayed an is not a story." Change and tension aren't stylistic choices. They're structural requirements.

"It's amazing how hard it is and what discipline it takes to lay out all of your facts and information in a three act structure, and then find the real tension and the real change," Aidan said.

This isn't abstract. Indeed ran a program called Rising Voices for five years — funding ten short films about work annually, debuted at Tribeca Film Festival, now streaming on Hulu. Each year the team read roughly 800 scripts to select ten filmmakers. That volume of script evaluation built something most content teams never develop: genuine pattern recognition for what creates narrative tension versus what only looks like it. The program has ended, but the capability it built into the team hasn't.

Testing Narrative Before Spending on Production

The most counterintuitive element of Indeed's approach is the sequence. Most content operations test performance after launch — traffic, engagement, conversion. Aidan's team tests narrative framing before a dollar of production is committed.

Indeed uses a group called the Leadership Connect Community: long-term VIP clients with enough trust in the relationship to give unfiltered feedback on story drafts. The questions are specific: Is this solving your problem? Is this challenging conventional wisdom? Is this giving you something you didn't already know?

"We're told very clearly — no, you're overpromising, you're not delivering here," Aidan said. "And then we can refine our story arc."

The same skepticism applies to AI-assisted workflows. Indeed is building Claude-powered tools with embedded style guides and synthetic audience personas — testing messaging against buyer archetypes before it reaches market. But Aidan flags a failure mode that most teams haven't named yet.

"I spend a lot of time pretty much every day in Claude building projects and trying to work on these systems. It starts predicting what I want and I have to be incredibly sensitive to that and build an antagonistic relationship with the outputs — it very easily tricks you into thinking you're on this path of truth."

His practice is to interrogate outputs hardest at the moment of greatest satisfaction — asking what's wrong before moving forward. It's a forcing function most AI workflows don't have built in.

The Governance Layer

Running narrative-first content across eight teams without centralizing every decision requires something that holds the work together at the values level. For Indeed, that's five editorial principles applied to every piece of content regardless of channel or creator: insightful, evidence-based, human, hopeful, and actionable.

The definition of hopeful is precise. Aidan borrows from Nick Cave's conception — hope as a warrior emotion, not optimism. Something that gives people the energy to move forward, not reassurance that everything is fine.

"That framework is applied to any creator as they're thinking about their work," Aidan said. "And I think that has helped us get a coherence to what we're trying to achieve."

Editorial values at this level do something org charts and style guides can't: they make judgment calls portable. When a creator on any channel faces an ambiguous decision about a piece of content, the values answer the question without requiring escalation. At scale, that's the difference between coherence and noise.

Which, as it turns out, is exactly what Indeed was trying to solve for in the first place.

Listen to the full conversation with Aidan McLaughlin on The Marketing Front Lines.

Seven takeaways from this conversation.

Actionable for HR Tech Builders marketers

  1. The content volume playbook is broken — the fix is narrative architecture, not better briefs.
    Indeed was producing 3,000+ pieces per quarter and hitting diminishing returns. The shift wasn't to produce less — it was to identify a small number of high-conviction, cross-channel narratives and then engineer derivative assets from each one. The goal Aidan describes: get to 3,000 assets from one core narrative instead of 3,000 separate narratives. That inversion matters operationally. It means your most expensive content investments — long-form docs, podcasts, short films — become content engines rather than one-off line items. If you're still commissioning content at the asset level, you're building the wrong thing.
  2. Narrative discovery is a structured research process, not a creative exercise.
    Aidan's team runs three inputs simultaneously: (1) commercial objectives — what story does the business need told right now; (2) proprietary data — what unique signals does Indeed own that no competitor can replicate; and (3) category entry points — the specific moments in a buyer's journey where they actually need to hear from you. When those three align, strong narratives surface without forcing them. Most B2B content teams start and end at step one, which is why so much of it reads like internal positioning dressed up as thought leadership.
  3. Tension is the variable most B2B content is missing.
    Aidan references George Saunders: "once upon a time... and stayed an is not a story." The point isn't stylistic — it's structural. If your content doesn't have genuine tension (a real choice, a real risk, something that could go wrong), it doesn't have a story, it has a sequence of claims. The discipline is in forcing that tension into the structure before a single word gets written — beginning, middle, end, and a change that actually means something.
  4. Test narratives with real buyers before you scale them — not after.
    Indeed uses a group they call the Leadership Connect Community: long-term VIP clients who have a trusted enough relationship with the team to give honest feedback on narrative drafts. The specific question Aidan's team asks: is this solving your problem, giving you new insight, or challenging conventional wisdom? If the answer is no, the story arc gets reworked before it hits production. Most teams A/B test content after launch. Aidan's team qualitatively pressure-tests narrative framing before spend. That's a different activity and a more valuable one.
  5. Set editorial values before you set content formats.
    Indeed operates five editorial values that apply to every piece of content regardless of channel, format, or team: insightful (adds something new or challenges conventional wisdom), evidence-based (proprietary Indeed data or verified third-party research), human, hopeful in the active sense Aidan borrows from Nick Cave — hope as a warrior emotion, not false optimism — and actionable. These values do the coordination work that style guides can't. When you're running eight content teams across a center of excellence, shared editorial principles create coherence without centralizing every decision.
  6. The ROI case for long-form content is a derivative assets argument.
    The historic blocker for pitching documentary or long-form investments inside a brand is distribution: where will this live, how do we measure it. Aidan's reframe is a production question instead: how many clips, articles, infographics, and social assets can we extract from this one investment? If the answer is enough to fuel your channel needs for a quarter, the unit economics flip. The long-form piece stops being a brand expense and becomes your lowest-cost content factory. The operational requirement is getting internal systems — from production to channel distribution — aligned enough to actually execute the handoffs.
  7. Build an antagonistic relationship with your AI outputs — especially when you like them.
    Aidan flags a specific failure mode: AI tools start predicting what you want to hear, not what's accurate or useful. His practice is to interrogate outputs hardest at the moment he feels most satisfied with them — explicitly asking what's wrong with the work before moving forward. He also notes the risk with synthetic audience testing specifically: the danger isn't using AI personas to pressure-test messaging, it's treating the outputs as truth rather than directional signals. Professional judgment still has to own the final call. If your AI workflow doesn't have a forcing function for that skepticism built in, the tool is flattering you, not helping you.

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