Reimagining Digital Marketing Roles for the GenAI Era (Part 1-4)

From Search Stack to Workflow to Job Title: How GenAI Is Quietly Reshaping Your Job Description

Generative AI hasn’t killed SEO, but it’s been quietly rewriting your job description, even if you don’t realize it yet. If you’re in digital marketing, you’re already working with—and being evaluated by—machines. We all know that large language models (LLMs) are starting to shape who sees your content, how it's cited, and where (or whether) it shows up. Just look at how Google's Gemini rewrites answers from multiple sites—or how ChatGPT cites Reddit before your own FAQ. That’s not abstract. That’s your visibility being rewritten.

So far your job didn’t disappear.
But it might just change names, soon.
And scope.
And there are new tools.
And different expectations.

This project is about giving language to those shifts—some of the titles are a bit playful, I’ll admit, but it’s all grounded in real workflow evolution. And real world numbers. So make fun of the names, adopt them or ignore them – all your choice. But I really hope you pay attention to the shift in work tasks and where present day roles end up over the next 5 years.

Why This Exists

I’ve been watching these transitions unfold through a 25-year lens. From helping Fortune 100 companies while at Microsoft, to guiding Fortune 500s at Yext, to building AI-driven tools and consulting on search infrastructure—I’ve seen digital marketing roles transform multiple times. I’ve also published the industry’s first SEO salary survey (with SEMPO) and fielded thousands of questions about roles, compensation, and org design over the years. I’ve spoken at conference about how to manage your career and consulted directly with people to help them with their next steps, negotiations and career decisions.

So when AI started reshaping how discoverability works—not just in SERPs, but inside language models—I knew it was time to rethink the org chart.

This isn’t satire despite the odd, funny and, honestly, quirky new titles I settled on (hey, y’all decided “GEO” was a good idea, so that makes you a GEOlogist!).
It’s actually a serious look at how titles, tasks, and salaries are (and will likely be) shifting under the weight of GenAI. And yeah, a few of these suggested new job titles are funny, and I doubt they’d ever be the choice for any company, anywhere. But this isn’t about nailing the job title. It’s about preparing ourselves for change that hits home. Your job will change, what you do will change and what you get paid to do it will probably change, too. Jokes aside, this is real.

What This Is (and Isn’t)

This is a forward-facing framework. I’ve reimagined 14 core digital marketing roles through the lens of generative search, AI agents, and retrieval-based content discovery. Each one is mapped from an existing title, updated with a modern job scope, and paired with salary evolution data grounded in U.S. national averages.

A few caveats:

• The job titles are inventive—but the workflows are real.

• Salary figures reflect base pay only, typically near the mid point of the range.

• This is not an HR standard or exhaustive database. It’s a strategic snapshot of where these roles are heading.

• While I’m not an HR professional, I worked closely with one on this project—and for the roles they actively, currently recruit, the baseline numbers align.

• I’m sharing the entire process used to generate every datapoint, so you can adapt it to your own team, org, or region. Use an much or little as you like.

• You’ll find a glossary at the end (of part 4) meant to help you understand some of the new work tasks referenced. It’s not exhaustive, just meant to help fill in a few blanks.

• The is a lot of overlap, and that’s by design. Every role has a direct impact now, so every role needs to contribute. Long gone are the days of “SEO-ing the site”, expecting the SEOs to work their magic. If every role doesn’t do their part in this new era, your business suffers. So yeah, overlap is going to happen.

Why It Matters

In a GenAI-first world, discoverability isn’t just about keywords—it’s about retrievability, grounding, citation, and trust signals. The jobs haven’t disappeared. But the work absolutely has evolved.

The question isn’t whether your title will change.
It’s whether your skill set is keeping up with how content is found, ranked, and trusted by machines. Because as sure as you can depend on sunrise, you can depend on companies changing and adapting, whether you’re ready or not. It’s not a matter of if, it’s a matter of when. Faster is better, but slow still gets there.

Why We Need New Titles (and Workflows)

Job titles have always lagged behind the work. “Webmaster” persisted long after the job became ten different specialties. “Social Media Manager” can now mean anything from brand voice strategist to short-form video producer to customer service triage.

We’re at the same point again—but faster.

Generative AI hasn’t just changed the search interface. It’s rewritten the way visibility works. Content is no longer just read by users. It’s retrieved, rated, summarized, cited, and sometimes rewritten by machines. That means the way we structure, measure, and manage content must change too.

This isn’t about chasing buzzwords or inventing shiny new job titles for the sake of trendiness or fun. It’s about acknowledging that:

• Retrievability is now a job function.

• Chunking content for machine consumption is a strategic task.

• Citation visibility and trust signals are measurable, and increasingly, someone's responsibility.

If your job touches code, content, SEO, brand, analytics, or ops—there’s a good chance your work has already shifted. The titles just haven’t caught up yet. We don’t need new titles because the old ones are wrong. We need them because the old ones don’t reflect who’s actually doing the work—or how it’s being evaluated.

How I Built the GenAI OrgMap

I didn’t just invent clever titles and call it a day (though I did have some fun there). This project was built to be useful, not just entertaining. Every role you’ll see below is grounded in real shifts already happening across engineering, search, marketing, and content workflows. And each one connects to measurable changes in how visibility is earned, structured, and evaluated by machines.

Here’s the low-down:

• Mapped Traditional Titles: I started with well-known digital marketing roles — from SEO to email to analytics — and asked: What are these roles actually doing today?

• Tracked Workflow Shifts: Next was a look at how GenAI search systems have altered tasks. For example, SEO is no longer just about keywords and links. It now involves schema precision, retrievability engineering, and grounding optimization. (And yeah, current SEO is about structured data, but be honest - do you have that nailed down at 100% yet? And now, it needs to be.) Thi sisn’t perfect, but it’s close.

• Defined the New Work: For each role, I outlined the evolved responsibilities — what’s added, what’s changed, and what’s now critical for machine-era visibility. Again, not perfect due to having to look forward so much, but it’s a start for us.

• Aligned Titles with Tasks: That’s how a Content Manager became a Cheditor, and a Brand Strategist became a Trust Signal Strategist. Not because I like puns (though, I kind of do), but because these roles now own workflows that didn’t exist five years ago. So we went from Content Manager to Chunk Editor (Cheditor).

• Backed It with Data: For each role, I gathered historical salary data (sources in Part 4), projected future compensation based on scarcity and skill demand, and shared the methodology so you can run your own models.

This isn’t just a thought exercise. It’s a blueprint. And I’m sharing the entire GenAI OrgMap — including methodology, visuals, glossary, and projections — so you can apply it to your own org, team, or next role.

The 14 Reimagined Roles

These aren’t sci-fi jobs for some distant AI future, but a few of them work better if you imagine yourself in The Fifth Element or as a character in Blade Runner. Kidding aside, they’re a reflection of how modern marketing is already changing — quietly, structurally, and beneath the surface of every org chart. I’ve taken 14 familiar roles across SEO, content, communications, analytics, and ops and reimagined them to better reflect what the work now demands in the GenAI era. This is not a list of every job or title, so if your role isn’t here, don’t worry. This is why I’m providing the working framework (in Part 4 of the series) – you can simply take the outline here and fill in your own blanks.

For each role selected, I include:

• The traditional title you probably know.

• The updated GenAI-era title that, I think, more closely captures the new reality.

• A brief descriptor of how the role’s responsibilities have evolved — and what that means for individuals, teams, and orgs moving forward.

Some will feel like small shifts. Others, like entire rethinks. That’s intentional.

These are the roles that live inside the GenAI OrgMap — not hypothetical orgs of the future, but practical, people-powered roles that exist today (or should). The difference is that now, they include accountability for things like:

• retrievability,

• citation structure,

• grounding accuracy,

• prompt design,

• signal visibility, and

• workflow automation across multiple systems.

So whether you're hiring, managing, or rethinking your own next move, these 14 roles offer a new lens for understanding how GenAI is changing the way digital work gets done.

What You're Seeing: Projected Role Disruption Index (2024–2030)

This chart ranks the 14 GenAI-era roles based on how much change they’re expected to undergo between now and 2030. The index is built from the same six weighted factors we used in the salary projection model:

- AI Skill Scarcity

- Strategic Importance

- Automation Threat (inverted — low threat = higher disruption ownership)

- Transferability of Skills

- Market Maturity

- Org-Level Visibility

Each role scores up to 18 points. The higher the score, the more evolutionary pressure that role is under — meaning new responsibilities, skillset demands, and visibility stakes by 2030.

How I Modeled Salary Evolution

This didn’t stop at job titles. For each reimagined role, I projected a base salary trajectory—past, present, and future—grounded in public data and adjusted using a consistent framework.

This isn’t meant to predict the future with absolute certainty. It’s meant to provide a directional benchmark for teams and individuals navigating evolving roles in a GenAI-influenced workforce. The full weighting model is shared in Part 4 of this series, so you can plug in your own roles, adjust the criteria, and generate your own projections.

The Method

For each role, I looked at:

1. Historical Baselines
   Information pulled is inflation-adjusted U.S. national base salary data going back to 2005 using sources like the Bureau of Labor Statistics, Glassdoor, and Levels.fyi. Where direct matches didn’t exist (e.g. no “Signal Analyst” in 2005), I used the most structurally similar role (e.g. “Marketing Analyst”) as a proxy.

2. Present-Day Averages (2024)
   Current public datasets were used for average base salaries across mid-level positions—not entry-level, not executive. Just the core IC or manager level where most of this change is happening.

3. 2026 Projections
   For each role, I modeled growth based on six weighted factors:

   • AI Skill Scarcity

   • Strategic Importance

   • Automation Threat

   • Transferability of Skills

   • Market Maturity Stage

   • Org-Level Visibility

These weights varied by role, based on how critical each factor is to that role's future demand. For example, the Prompt Strategist gets a boost from AI skill scarcity, while the Workflow Intelligence Manager scores high on strategic importance. Feel free to substitute your own weighted factors, too - these are just what I consider important.

A Note on Automation Threat

This is the only factor in the model with a negative weight, because automation reduces long-term salary leverage.

- A role with –5% automation threat is less replaceable and therefore more protected

- A role with –10% is more automatable, which puts downward pressure on compensation

So, unlike the other factors, larger negative values here represent higher risk and less strategic growth.

4. 2030 Normalization
   Most roles show a peak in 2026, followed by a flattening or mild correction by 2030 as tooling improves, training spreads, and the talent pool catches up.

Finally, here’s what the “Impact Levels” mean inside the “What’s Driving Salary Growth” sections for each role.

Each weighted factor influencing salary evolution (like AI Skill Scarcity or Transferability) includes a short descriptor and an Impact Level:

• High = This factor plays a critical role in shaping demand and strategic value for the role

• Moderate = This factor is meaningful but not dominant

• Low = This factor is present but not a major driver

These levels don’t change the weight itself — they help you understand how influential each factor is within the role’s broader context. Think of them as narrative cues for how each signal affects the evolution of the job.

What These Numbers Are (and Aren’t)

• They’re base salaries only
  No bonuses, equity, or benefits—just a clean comp benchmark.

• They represent national U.S. averages
  Localized salary multipliers are included elsewhere in the data, but these are unadjusted national norms. (The average, not for SFO, NYC, LA, etc.)

• They’re directional, not definitive
  A starting point to help you gut-check role alignment and compensation—not a magic formula for HR. (Though HR will understand much of this.)

• They’re replicable
  The full weighting model is shared later in the article, so you can plug in your own roles, adjust factors, and generate your own estimates.

Data on The Roles

Digital GEOlogist

(formerly: SEO Specialist)

What’s Changed
The role once known for keywords, links, and metadata has evolved into something far more strategic — and far more technical. A Digital GEOlogist doesn’t just help content rank; they architect how content is retrieved, chunked, cited, and grounded in GenAI systems. They're responsible for optimizing discoverability not only in traditional SERPs but in conversational AI, assistant-driven environments, and chunk-based rating models.

New Responsibilities Include:

• Structuring content for semantic clarity and retrievability

• Implementing schema strategies for entity linking and trust signaling

• Collaborating on knowledge graph optimization

• Aligning content with grounding-ready formats for AI answers

Salary Evolution (U.S. National Averages — Base Only)

• 2005: $48,000 — Classic on-page/off-page SEO focus

• 2010: $54,000 — Rise of content-centric SEO

• 2015: $63,000 — Technical SEO, mobile-first, and local surge

• 2020: $72,000 — Entity-based SEO and structured data gain ground

• 2024: $84,000 — LLM retrievability begins influencing strategy

• 2026: $98,000 — High demand for GenAI-centric optimization expertise

• 2030: $94,000 — Tapers slightly as tooling matures and skills normalize

Figures are inflation-adjusted U.S. national base salaries. Regional multipliers apply.

What’s Driving Salary Growth?

• AI Skill Scarcity: +15%
  GenAI-specific optimization skills are in short supply, especially those tied to retrievability and chunk structuring.
  (Impact Level: Moderate)

• Strategic Importance: +25%
  This role is central to AI-readiness and modern content discovery.
  (Impact Level: High)

• Automation Threat: –5%
  Core responsibilities still require human context and oversight.
  (Impact Level: Low)

• Transferability of Skills: +15%
  Skills apply across SEO, content ops, and emerging AI platforms.
  (Impact Level: Moderate)

• Market Maturity Stage: +10%
  Organizations are still adjusting to LLM-first optimization needs.
  (Impact Level: Early-stage)

• Org-Level Visibility: +10%
  Increasingly seen as a strategic advisor across departments.
  (Impact Level: Growing)

→ Net projected growth: ~16% (2024 → 2026)
→ Plateau expected by 2030 as AI-readiness becomes standard

Why It Matters
Search didn’t die — it evolved. And this role is at the center of it. If your SEO team is still chasing keyword gaps but ignoring chunk retrievability and citation models, you’re not underperforming… you’re optimizing for the wrong decade.

Cheditor

(formerly: Content Manager)

What’s Changed
Content Managers used to plan calendars and assign articles. In the GenAI era, they now audit, edit, and optimize content chunks specifically for retrieval by AI models. The ChEditor (Chunk Editor) curates internal content libraries, aligns outputs to embedding strategies, and ensures every asset has a defined role in assistant-based search experiences. Think less CMS wrangler, more chunk engineer.

New Responsibilities Include:

• Chunking legacy content into AI-optimized formats

• Tagging and organizing content for vector retrievers

• Establishing embedding pipelines for LLM-readiness

• Maintaining internal content libraries for model alignment

Salary Evolution (U.S. National Averages — Base Only)

• 2005: $49,000 — Editorial planning and basic CMS work

• 2010: $54,000 — Content strategy emerges as a distinct focus

• 2015: $62,000 — Rise of multichannel and persona-based planning

• 2020: $70,000 — Emphasis on UX writing and componentized content

• 2024: $82,000 — Integration of AI editing and QA tools

• 2026: $95,000 — Demand for retrieval-aware editing skills surges

• 2030: $87,000 — Normalization as AI-native tooling simplifies chunking

Figures are inflation-adjusted U.S. national base salaries. Regional multipliers apply.

What’s Driving Salary Growth?

• AI Skill Scarcity: +15%
  Chunking, embedding, and prompt-readiness remain niche but critical.
  (Impact Level: Moderate)

• Strategic Importance: +20%
  Ensures brand content is discoverable in future-facing search models.
  (Impact Level: Moderate)

• Automation Threat: –10%
  Portions of chunking and QA can be machine-assisted.
  (Impact Level: Medium)

• Transferability of Skills: +10%
  Applies across content strategy, knowledge management, and UX.
  (Impact Level: Moderate)

• Market Maturity Stage: +10%
  Few orgs have retriever-aware editing protocols in place.
  (Impact Level: Early-stage)

• Org-Level Visibility: +5%
  Often works behind the scenes in editorial or SEO teams.
  (Impact Level: Low)

→ Net projected growth: ~13% (2024 → 2026)
→ Downward pressure by 2030 due to tool automation

Why It Matters
AI can’t retrieve what it doesn’t understand. And messy, outdated, or unstructured content gets ignored. The Cheditor is the difference between content that’s used and shared and content that vanishes in the age of vector search.

Information Design Lead

(formerly: Content Strategist)

What’s Changed

Content strategy used to mean managing editorial calendars and content audits. Now, it means shaping how information is structured, chunked, retrieved, and aligned across platforms — especially for AI systems. The Information Design Lead focuses on machine-readable clarity, semantic architecture, and omnichannel consistency.

New Responsibilities Include:

• Designing content models for structured chunking and semantic clarity

• Ensuring retrievability and alignment with GenAI content systems

• Collaborating on entity-driven knowledge models and taxonomies

• Leading audits to improve grounding, citation, and answer-readiness

Salary Evolution (U.S. National Averages — Base Only)
Year: 2005 — $58,000 — Editorial planning and website IA
Year: 2010 — $65,000 — Rise of personas and UX alignment
Year: 2015 — $71,000 — Multichannel content mapping
Year: 2020 — $78,000 — Structured data and CMS governance
Year: 2024 — $87,000 — GenAI retrievability enters scope
Year: 2026 — $102,000 — Central role in AI-era content architecture
Year: 2030 — $97,000 — Stabilizes as standards solidify

Figures are inflation-adjusted U.S. national base salaries. Regional multipliers apply.

What’s Driving Salary Growth?
AI Skill Scarcity: +15% (Impact Level: Moderate) — Deep knowledge of structured content and machine readability is still uncommon.
Strategic Importance: +25% (Impact Level: High) — Poorly structured content fails in GenAI systems, making this role central to performance.
Automation Threat: –5% (Impact Level: Low) — While tools assist, human oversight remains key for quality and alignment.
Transferability of Skills: +20% (Impact Level: High) — Skills apply across marketing, product, UX, and documentation domains.
Market Maturity Stage: +10% (Impact Level: Early-stage) — Many orgs are still learning what information design even is.
Org-Level Visibility: +10% (Impact Level: Growing) — Rising sharply as executives ask, “Why didn’t our content show up in the AI answer?”

→ Net projected growth: ~18% (2024 → 2026)
→ Mild decline by 2030 as internal standards and AI content systems mature

Why It Matters

In GenAI-driven systems, content isn’t just read — it’s retrieved, parsed, and synthesized. The Information Design Lead ensures your message survives that process intact. Without this role, your “content strategy” won’t be seen, heard, or surfaced at all.

What’s Coming Next

GenAI isn’t just changing how content is retrieved — it’s reshaping the work behind PR, brand credibility, and even how teams write. In Part 2, we’ll look at the roles driving visibility and verifiability in a machine-mediated world — and why trust is no longer a vibe, it’s structured.