Jun 3 2026
What Not To Automate With AI: The SEO Deskilling Trap

When AI arrived in the marketing mainstream, it was accompanied by a persistent and scary narrative: The machines are coming for our jobs. On the surface, those fears might appear well-founded. According to the Content Marketing Institute, 43% of surveyed marketers said their organization had laid off marketing employees within the last year – a staggering 30% increase from 2024. For organizations with 1,000 or more employees, this number rises to 62%.

But a single stat can never give us the full picture. One thing we have in abundance right now is research papers, reports, and surveys trying to understand AI’s impact on business, on consumers, on creativity, on cybercrime, and, of course, on the workplace – from how we work to if we work. Taken together, these studies reveal a far more complicated story.

Anthropic recently published its report on the Labor Market Impacts of AI (March 2026), which found “no systematic increase in unemployment for highly exposed workers since late 2022.” And the World Economic Forum predicts that, while AI and information process technologies will displace about 9 million jobs by 2030, it will also create about 11 million new jobs. It seems AI might eventually drive a net gain in jobs.

Of course, stats like these are no reassurance to anyone who finds themselves “displaced” by AI in the interim. Anthropic’s report also ranked the 10 occupations with the greatest potential exposure to AI. Computer programmers (74%) top the list, while marketing specialists (64.8%) come fifth, which should leave us in no doubt that SEO as a profession is extremely exposed to AI disruption.

So, should we be worried or not?

The question isn’t how much you can automate or safely delegate to AI, or how small a team you can get away with. As it turns out, some of the most mundane or repetitive jobs, many of which might seem ripe for automation, may be far more valuable retained as manual, human-led tasks. Just because it’s easy or even cheaper to automate something doesn’t necessarily mean you should.

Augmented Versus Autonomous AI

Anthropic also publishes a quarterly Economic Index report, analyzing Claude usage data to track how people are working with AI in professional settings.

At the time of writing, the most recent report, Learning Curves, came out in March and draws on data from February 2026. It found that more than half (53%) of all interactions on Claude.ai are now “augmented” – human-in-the-loop interactions where the user learns, collaborates, and iterates on a task with Claude. Automated use – defined as interactions where the user delegates tasks entirely to Claude with little back-and-forth – has fallen to 44%.

So, is this more efficient?

The January edition, Economic Primitives, delves deeper into questions of task complexity, completion speed, and success rates – and this is where things get complicated.

It turns out that more complex tasks benefit from greater time savings. Working with AI can help users to complete tasks that would typically require a high-school education 9x faster, 12x faster for tasks requiring a college degree.

But these huge time savings come with a trade-off – and it’s a biggie. The same report found that basic queries or tasks, such as answering straightforward questions about products, currently achieve a 70% success rate. For more complex tasks, the success rate falls to just 66% for college-level work.

While that’s only a 4% difference, I’d argue neither result is particularly encouraging. To put it another way, the outputs from Claude aren’t up to snuff approximately one-third of the time.

One area where this low success rate has the potential to create issues is in code generation, which currently makes up 35% of all Claude usage.

Research from code review platform CodeRabbit found that AI-generated code produces roughly 1.7 times more issues than human-written code, including logic errors, readability problems, and, perhaps most concerning, security vulnerabilities.

If you’re an experienced developer, you’re more likely to spot the errors and improve on what AI has given you, treating Claude’s output as rough prototype rather than a finished product. But what if you’re not?

This is the dilemma: AI is not a replacement for genuine expertise. On the contrary, it appears a level of expertise is essential to using AI effectively.

Ironically, the people who would once have carried out a lot of these routine tasks – juniors or entry-level hires – lack the experience to assess what AI gives them.

That’s why I strongly believe no one should delegate a task to AI that they couldn’t do themselves. Once someone has learned a task, and developed a deep understanding of the concepts involved, then AI becomes a tool to speed up the process.

The Deskilling Trap

If expertise is vital to working with AI effectively, then it follows that businesses should focus on hiring people with the necessary skills and experience.

And multiple studies suggest this is exactly what’s happening.

  • Entry-level job postings have declined ~35% across the U.S. economy since January 2023, with AI cited as a significant contributing factor.
  • In tech companies, hiring of new graduates with less than a year of experience has declined 50% since 2019. Grads now account for only 7% of hires.
  • One in three companies has pulled back on hiring entry-level marketers, nearly 2.5 times more than those increasing entry-level hiring.

At the same time, organizations appear to be increasing, rather than decreasing, their overall hiring of marketing talent by a significant margin.

This suggests organizations aren’t laying off staff or cutting back on junior hires to shrink their teams, but to reshape them. They’re hiring more senior, skilled, and experienced marketing talent who, as the CMI report puts it, “can direct, oversee, and – when necessary – rebut AI rather than compete with it.”

But is that conclusion supported by the data?

Both the Anthropic Labor Impacts report and the Revelio Labs research attempted to answer this question by comparing entry-level hiring patterns in industries and occupations with differing levels of exposure to AI disruption. The Anthropic findings, based on tracking the monthly job-start rate for younger workers (aged 22-25), were suggestive but not conclusive.

However, the Revelio Labs data focused on advertised entry-level job openings across four categories, finding that AI exposure has had a clear impact on entry-level demand:

  • 40% decline in highly exposed entry-level jobs.
  • 33% decline in lowly exposed entry-level jobs.
  • 27% decline in highly exposed non-entry-level jobs.
  • 16% decline in lowly exposed non-entry-level jobs.

Taking all the evidence together, the picture we’re left with is of a skills market in crisis. Most of the demand is now concentrated at the top, while the bottom of the pipeline thins out.

There’s a crunch coming.

The Qanat Problem

These days, talk of AI “transforming the landscape” has become an overused cliche. But around 2,500 years ago in ancient Persia, qanats were an equally revolutionary technology that quite literally transformed the landscape.

Qanats are precisely engineered underground channels, each one dug by hand by skilled workers called muqannis, using gravity alone to carry water over great distances from the mountains to the deserts.

Farms flourished. Cities grew. Persia bloomed.

Like AI, the benefits were huge, but the infrastructure was largely invisible. People became accustomed to drinking and bathing and irrigating their gardens with little regard for how the water got there.

Well-maintained, there is absolutely no reason why a qanat could not continue bringing water for hundreds, even thousands of years. In fact, some ancient qanats are still active even today, with 11 of these systems collectively designated as a UNESCO World Heritage Site.

Even if a qanat fell into disrepair through neglect – shafts left uncleared, tunnel walls allowed to crumble, silt left to accumulate – or if other, deeper wells extracted too much groundwater, lowering the water table below the level of the qanat, the consequences weren’t always immediate. Water would continue to flow for a while, but it would gradually decrease over time, until the flow became a trickle, then a dribble, and eventually … nothing.

Right now, businesses are happily drawing as much metaphorical water as they can from AI. However, the consequences of overuse and poor planning – such as applying AI to the wrong tasks – might not become apparent for some time. For now, the water still flows – but that doesn’t mean there’s no damage.

Many of today’s entry-level hires will go on to become the mid-level and senior talent of tomorrow. But without a constant flow of new blood entering the industry and gradually learning the craft, that skilled talent pool will soon shrink. And with demand for senior marketing expertise on the increase, you can expect the cost of hiring that talent to go up.

By then, it’ll already be too late to start hiring and training the next generation of marketers.

→ Read More: Ask An SEO: Should I Hire Candidates Who Can Use AI Tools Or Have Traditional Skills?

What Not To Automate

The default approach to AI adoption seems to be to identify any tasks that are repetitive, time-consuming, or mechanical, and automate them – or at least as much of the process as possible.

This isn’t necessarily wrong, and there are plenty of such tasks that can easily be delegated to AI without stealing valuable experience from someone, like downloading files, formatting documents, or aggregating data from multiple sources. There’s little to no value to be gained from expending human effort on these.

However, some repetitive tasks do generate value, even if, on paper, manually completing the task looks like cost and inefficiency. These are the tasks, which, over time, imbue an understanding of why something works. The value is in the investment you are making in your team’s development.

You might already have some form of staff development program or provide support to employees wanting to take up training courses. But this isn’t about sending your devs on a two-week course in JavaScript. This is about mastering the everyday stuff no course or textbook can teach you.

Keyword research is a good example of a task where SEO theory turns into practical understanding. Yes, AI can produce a keyword list faster than anyone, clustered by intent, filtered by difficulty, and mapped to the funnel. You could generate the complete report in the time it takes a junior to open a spreadsheet.

But by conducting keyword research for a wide variety of clients in different verticals and targeting different customers, a fledgling SEO will gradually acquire and hone their commercial instincts. Why are certain keywords more valuable than others? How do factors such as intent, geographic location, or even the time of year impact the results? Which keywords represent the strongest opportunities for a client?

It’s one thing to present a client with a neatly formatted document setting out a long list of viable keyword options, but it’s quite another to be able to answer the client’s questions, absorb feedback, and make further recommendations.

Don’t approach AI in terms of roles or job titles. The key is to audit your tasks and workflows to identify which activities don’t increase understanding and, more importantly, which ones do – and assign value accordingly.

This allows you to be deliberate and strategic about which activities to preserve as training infrastructure.

Practice Makes Perfect

The key to mastering any form of expertise is repetition – and there are no shortcuts.

Want to play the clarinet? Perhaps, you dream of fronting a jazz band one day, creating music that clings to the soul.

AI can play music. AI can even create music. But AI cannot make someone into a musician. It cannot replace the repetitive, tedious practice required for someone to develop genuine expertise.

In SEO and marketing, all those routine, repetitive tasks aren’t inefficiencies to be automated away. They are the scales. They are the learning to read sheet music.

You can’t magically imbue fresh-faced graduates with five years’ experience overnight. You need to give them five years working on the job, developing their skills, deepening their knowledge, and honing their instincts.

That’s why it is vital for businesses to keep hiring and developing new talent. After all, it’s far cheaper to hire, nurture, and develop internal talent than it is to compete for senior expertise in a shrinking pool – with salary expectations to match.

No one notices when a music student stops practicing, even more so if they never had the opportunity to start. But if too many budding musicians never master their instruments, there will be no one left to play in those jazz clubs and concert halls until, one day, the music stops.

More Resources:

Featured Image: Andrey_Popov/Shutterstock

Ecommerce MGMT 0 Comments
May 31 2026
White-Collar Will Be Fully Automated In 18 Months – So What Makes You Different? via @sejournal, @gregjarboe

Mustafa Suleyman, CEO of Microsoft AI, has predicted that most professional white-collar work will be fully automated by August 2027. Marketing. Accounting. Legal. Project management. He named them.

The day before, I’d been reading about Jensen Huang’s commencement address at Carnegie Mellon, where he told 5,800 graduates at one of the country’s top engineering schools to consider becoming electricians.

The same day, a philosopher reviewing a tech journalist’s new book, “I Am Not a Robot”, in “The Boston Globe” asked the question neither of them had touched – if machines can now reason, what exactly is left for us?

Huang Tells Graduates To Build Things

Moneywise reported how Jensen Huang delivered his Carnegie Mellon commencement address in the rain, to 5,800 graduates at one of the country’s premier computer science and engineering universities, and spent a significant portion of it making the case for a career in the trades.

“AI gives America the opportunity to build again,” he told the crowd. “Electricians, plumbers, iron workers, technicians, builders – this is your time. AI is not just creating a new computing industry; it is creating a new industrial era.”

He wasn’t being contrarian for effect. Moneywise reported capital spending from the largest U.S. tech companies could hit $700 billion this year in data center construction alone, and Randstad’s March analysis of more than 150 million U.S. job postings found demand for skilled trades growing three times faster than for professional desk-based roles. None of that infrastructure gets built without people pulling wire and laying pipe.

Huang also said something that tends to get buried under the trades narrative: “Yes, AI will change every job. But the task and the purpose of a job are not the same. Many tasks will be automated. Some jobs will disappear. But many new jobs and entire new industries will be created.” That distinction between tasks and purpose is the one SEO professionals should write down.

Suleyman Says White-Collar Work Has 18 Months

Microsoft AI CEO Mustafa Suleyman told the “Financial Times” that AI is approaching “human-level performance on most, if not all professional tasks.” His timeline is 12 to 18 months. The specific roles he named as vulnerable were accounting, legal, marketing, and project management.

He named marketing explicitly, and 18 months from February 2026 is August 2027.

The prediction has been circulating long enough to become background noise. That’s exactly the problem with it. Search has already changed more in the past 18 months than in the preceding five years. The practitioners feeling that change most acutely are not the ones whose jobs have disappeared. They are the ones whose workflows have been disrupted faster than their strategic frameworks have been updated.

Kaag Asks The Question Stern’s Book Doesn’t Quite Ask

Sunday morning, John Kaag’s review of Joanna Stern’s “I Am Not a Robot: My Year Using AI to Do (Almost) Everything” completed the pattern for me. Kaag, a philosophy professor at University of Massachusetts Lowell, approaches Stern’s experiment less as a technology story than as a question about what remains distinctively human once machines can imitate more and more of what we do.

He traces the arc back to Alan Turing’s famous “imitation game,” where the challenge was whether a machine could successfully pass as human in conversation. For decades, humans occupied the position of judge and evaluator. But sometime in the internet era, that relationship quietly flipped. CAPTCHA systems began asking us to prove that we were human and check the box confirming “I am not a robot.” What started as a security measure also became a cultural metaphor: machines were no longer trying to earn our approval; we were adapting ourselves to their standards of verification.

Kaag argues that Stern’s book pushes beyond the novelty of AI assistants writing emails or summarizing meetings. The deeper issue is whether human identity itself becomes harder to define once systems can convincingly simulate judgment, language, and even personality. If an algorithm can reproduce our tone, our style, and eventually much of our professional output, then the important question is no longer whether AI can think like us. It is whether we still understand what makes human thinking meaningful in the first place.

To explore that question, Kaag invokes Mary Everest Boole, the 19th-century thinker and educator married to mathematician George Boole, whose logic became foundational to modern computing. She speculated that once reasoning itself became mechanized, humanity would need to anchor its identity somewhere beyond pure rationality. Her answer was not efficiency or calculation, but qualities grounded in empathy, moral judgment, and human connection.

That idea lands differently in 2026 than it might have a decade ago. Stern’s reporting demonstrates how capable AI systems have already become at tasks once considered markers of expertise. But Kaag’s larger point is that capability alone does not settle the question of value. The more machines approximate reasoning, the more pressure there is on humans to articulate what cannot simply be automated: lived experience, accountability, intuition shaped by failure, and the ability to care about consequences in ways that are more than computational.

That is the tension running underneath Stern’s book and, increasingly, underneath modern knowledge work itself. The challenge is no longer proving that machines can imitate us.

What Makes You Different?

Three pieces, written independently, from a commencement stadium in Pittsburgh, a “Financial Times” interview, and a Sunday book review, arrive at the same argument from three directions.

Huang: The purpose of a job survives even when its tasks are automated.

Suleyman: The tasks of most white-collar work will be automated faster than most people are prepared for.

Kaag: If reasoning can be mechanized, and it can, increasingly, then the thing that defines us has to be something else.

For SEO professionals, that is the most practical question in the field right now. When your content, your strategy memo, or your keyword analysis could have been generated by a system that has learned to approximate you well enough, what makes yours different? The honest answer, Kaag suggests, is not a skill set or a process. It is the irreducibly personal quality of a perspective formed through actual experience, actual failure, actual presence in the work. That is what cannot be checked in a box.

More Resources:

Featured Image: beast01/Shutterstock

Ecommerce MGMT 0 Comments
May 25 2026
Why High-Performing Marketers Get Stuck In Execution Mode via @sejournal, @bngsrc

Most high-performing marketers hit a wall they never saw coming. But this isn’t because they stop working hard or run out of ideas. In fact, their ability to execute flawlessly and quietly becomes what holds them back.

Let me explain what I mean.

The shift from executor to strategist is one of the most significant career transitions a professional can make. And almost no one explicitly teaches it.

There are no beginner’s guides or formal training programs for it. There’s just a slow and confusing process of realizing that the rules of the game have changed and that the skills that got you promoted are no longer the skills that will carry you forward.

In this article, I will try to explain why this gap exists.

Why Execution Gets You Hired But Not Promoted

There’s a reason why most leaders excel as executors early in their careers.

Execution is a way to demonstrate your competence. It’s visible, measurable, and rewarding. The problem is, execution creates a trap.

When you solve problems well, leaders give you more problems to solve. You become indispensable as a doer, which makes you invisible as a leader.

Your productivity stays high. Your strategic effectiveness remains low. And the promotion you’re aiming for keeps moving just out of reach.

This is a structural failure rather than a personal one. Organizations are designed to reward execution in the early stages of a career. Feedback loops usually look like this: publish the page, launch the campaign, fix issues, hit the target, send the report.

But somewhere around mid-career, the signals change. The work that matters most becomes harder to measure, and the people who advance are the ones who learn to work within this uncertainty.

The Invisible Ceiling Most People Don’t See Until They’ve Hit It

The tricky part of this ceiling is that it’s hidden behind appreciation and praise.

You finish a quarter, and your manager compliments your output. You complete a project, and the team celebrates. It all seems like success.

But if you pay attention, you’ll notice that the conversations at a higher level are different. And these conversations are about what should be prioritized, what sensible compromises the organization should abandon altogether.

This is precisely the level where strategy lives. And it requires a completely different way of thinking.

Executors ask, “How can I solve this problem?” Strategists ask, “Should we even be solving this problem?” The shift from “how” to “should we” represents one of the most important mental shifts a marketer can make.

It’s also one of the least intuitive, because it feels like stepping back the moment instinct tells you to put in more effort.

As one observer put it, execution success can mask the need for evolution. Clarity comes not from leaning harder, but from stepping back.

What Changes When You Shift Your Lens

Transitioning from executor to strategist doesn’t mean you’ll do less work. It means you need to think differently.

Early in a career, success is task-oriented, characterized by quick responses, clean deliveries, and long working hours. Value is created by completing tasks. But as roles become more complex, the output that matters stops being a completed task and starts being a well-framed question.

There’s also a shift in delegation that catches many high-performers off guard. Strong executors generally resist delegating tasks because they know they can do them better and faster themselves.

But this instinct, if left unchecked, will bury them in the work. Every hour spent on tasks someone else could handle is an hour not spent thinking about what you can do at your level.

I believe nobody needs direct reports to start practicing this. You can begin by creating repeatable templates that others can use, collaborating with colleagues to distribute parts of a project, or setting aside calendar time for higher-level thinking. Because strategist behaviors can also be rehearsed before the title arrives.

The Mindset Shifts That Matter Most

The gap between an executor and a strategist is simply about your way of thinking. And that makes closing the gap difficult, because changes in mindset don’t show up in skill assessments.

Here are the most important ones:

From Solving To Questioning

Executors carry out the tasks assigned to them. Strategists, on the other hand, question whether the problem is the right one to solve. Diverting resources away from the wrong priorities is more valuable than perfectly executing the tasks brilliantly.

From Urgent To Important

Execution culture rewards responsiveness. Strategic thinking rewards prioritization. Learning to distinguish between what’s urgent and what’s actually important, and acting accordingly, is a discipline, not an instinct.

From Individual Output To Organizational Leverage

The strategist asks, “What can I make possible?” and this represents a shift from doing to multiplying. This is what creates the kind of impact that is noticed at the leadership level.

From Certainty To Informed Ambiguity

Executors generally thrive with clear deliverables and defined success criteria. Strategists must make decisions with incomplete information, set direction without guaranteed outcomes, and maintain their confidence in the face of uncertainty. This comfort with the uncertainty is something most people actively have to develop.

None of these changes are dramatic on their own. But together, they fundamentally represent your relationship with your work and your identity as a professional.

Practical Ways To Start Making The Shift

Knowing the shifts are necessary and actually making them are two different things. The transition tends to go better when it’s approached deliberately rather than waited for.

1. Find A Mentor

The guidance of someone who has successfully moved from specialist to strategist is difficult to replicate through reading alone. They can help you see the blind spots that are hardest to identify from inside your own perspective.

2. Ask Different Questions

Strategically minded people shift their perspectives, question things, and look at things from a broader viewpoint. Good questions signal a different way of thinking and position you as someone operating at a higher level.

3. Make Your Thinking Visible

Strategists don’t just produce results; they also share the reasoning behind those results. When you point out a pattern, name a risk, or articulate a trade-off, you’re demonstrating your strategic capacity. This visibility is more important than most people can imagine.

4. Protect Time For Thinking

This one seems simple, yet it’s constantly overlooked. If your calendar is filled with execution tasks, there’s no room for the kind of reflection required for strategic thinking. Treating thinking time as non-negotiable is a structural change, and it has to happen before the thinking can.

The Transition Is The Work

Most people see strategy as the goal and execution as the means to get there. But in my opinion, this perspective misses the actual challenge.

The transition from executor to strategist is confusing precisely because it requires unlearning the behaviors that are rewarded. Habits that earn you recognition, like staying in the details, solving every problem handed to you, and being the most trusted person in the room, are habits you need to change consciously.

This isn’t an easy or comfortable process. And it doesn’t happen automatically with a title change or promotion.

Marketing professionals who successfully make this transition have one thing in common. They stop waiting for permission to think strategically and start practicing where they already are.

They ask harder questions. They make their logic visible. They assign tasks not because they have to, but because they understand the leverage it creates.

Execution gets you hired. Strategic thinking gets you heard. And ultimately, it gets you followed.

You already have the instincts that got you this far. The next step is to develop those that will take you further.

You may also want to check this out: “How To Accelerate Your SEO Career.”

More Resources:

Featured Image: eamesBot/Shutterstock

Ecommerce MGMT 0 Comments
Apr 28 2026
The AI Skills Salary Premium via @sejournal, @Kevin_Indig

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

I normally write about strategy and search behavior, not labor markets. But the SEO job market is the clearest leading indicator I’ve seen of how companies are actually valuing AI skills, so I followed the data off the usual map.

946 SEO job postings show companies are willing to pay a premium for AI skills. But the signal is buried in descriptions, and the salary premium only truly activates at mid-level and above.

SEO jobs that mention AI in the title pay $113,625 at the median compared to $89,438 for jobs that don’t. That 27% gap is live in the market right now; it’s not a projection.

In this memo, I’m covering:

  • Where the 25-27% AI pay premium actually shows up in SEO postings.
  • Why screening jobs by title filter misses four out of five of the roles paying more.
  • How to position your resume (or your job description if you’re a hiring manager) so the right opportunities land on your side of the table.

About this data:

  • 946 full-time SEO roles from SalaryGuide.com were included in this analysis, posted December 2025 through March 2026, deduped at company + job title.
  • Salary midpoints from the 41.8% of roles that disclosed pay.
  • “AI mention” means the title or description contains “AI,” “LLM,” “AEO,” “GEO,” “Answer Engine Optimization,” or “Generative Engine Optimization.”

Companies Pay 27% More Salary For AI Skills

AI in the job title commands the bigger salary premium, but the description signal covers far more ground. Only 146 jobs carry AI in the title. 563 include it in the description. The description bucket captures 4x more roles and still delivers a 25% median salary lift over non-AI descriptions ($100,000 vs. $80,000).

Image Credit: Kevin Indig

The dollar deltas are $24,187 for the title bucket and $20,000 for the description bucket. Compounded across salary negotiations over a career, neither is marginal.

The AI Requirement Is Hidden In The Job Description

Only 15.5% of SEO postings include AI in the title. 59.5% require it somewhere in the description. Employers are building AI into the role without putting it in the headline.

At senior levels, the pattern becomes near-universal:

  • 78.3% of director/executive descriptions mention AI.
  • 67.4% of manager descriptions do.

Even at mid-level, one in two job postings includes it.

A hangup here? Filtering job searches by AI in the title misses 80% of AI-required roles. The requirement sits in the body text, not the headline.

Image Credit: Kevin Indig

The AI Skill Premium Grows With Seniority

At entry-level positions, AI skills in the description carry a slight negative premium (-2.3%). Employers don’t pay new grads more for knowing AI.

The signal flips at mid level (+14.3%), then compounds sharply at the management layer.

Image Credit: Kevin Indig

A director with AI in the description earns $35,250 more at the median than one without. Senior roles may earn more, but the premium is due to AI judgment (instead of tool skills). The market pricing is applied accordingly. Junior candidates may need AI on their resume to get the interview, but getting paid more for AI skills happens at mid-level and above.

9+ Years In, AI Skills Are Assumed

Experience requirements tell the same story with a steeper slope: For junior 0-1 year roles, 40.9% mention AI in the description. For roles requiring 9+ years of experience, that number is 92%.

Image Credit: Kevin Indig

At 9+ years, AI isn’t listed as a differentiator. Instead, it’s embedded in the role definition.

The 8% of senior postings that don’t mention it are the outliers.

The Market Has Decided, But The Titles Haven’t Caught Up

Even if the salary premium compresses later, pricing your skills against job description-level signals is still the right move today.

1. If you’re a job candidate: Screen descriptions, not titles. The title filter misses 80% of the AI-required roles and the 25-27% premium that rides with them. Put AI evidence in the top one-third of your resume, or it won’t register for the postings that pay more.

2. If you’re a hiring manager: Your pay bands are already two-tier, whether you’ve formalized it or not. Roles requiring AI pay more at the median, and most of yours don’t say so upfront. Close that gap now.

3. Mid-career and up: This is where the premium actually compounds. If you’re 4+ years in and AI doesn’t appear in the first one-third of your resume, you’re pricing yourself against an outdated market.

Quote from Josh Peacok, founder of Search for Hire:

Having been on hundreds of discovery calls with companies hiring SEOs and having built out hundreds of search teams at Search for Hire, the pattern is undeniable: SEO talent is being priced on two axes now: fundamentals and AI capability. The candidates commanding a premium aren’t the ones who can use ChatGPT, they’re the ones who can build scalable systems with it. But AI without precision judgment can take you a long way in the wrong direction, fast. The real unicorns combine that build capability with deep technical skill, strategic thinking and the ability to sit in front of a client. That combination barely exists and when it does, it doesn’t stay on the market long.

More Resources:

Featured Image: beast01/Shutterstock; Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
Apr 7 2026
From T-Shaped To M-Shaped: The PPC Career Evolution Nobody Is Talking About

Ask any PPC professional what career shape they are working toward, and most will say T-shaped. One deep specialism, broad supporting knowledge across adjacent areas. It became the dominant career framework in marketing over the last decade, and for good reason. In a world where platforms were simpler and clients valued versatility, the T-shaped practitioner was exactly what the market wanted.

That model is no longer enough.

Not because T-shaped practitioners are bad at their jobs or the model does not work anymore. Most are excellent. But the conditions that made T-shaped the right target have changed fundamentally, and the practitioners commanding the highest compensation in 2026 are not T-shaped. They are something more evolved: M-shaped. Two or three deep pillars of expertise, sitting on a broad foundation of knowledge across five to seven adjacent domains. It looks like a generalist from a distance and like a specialist up close, depending on which conversation you are in.

I want to make the case that M-shaped is not just an incremental upgrade on T-shaped. It is a fundamentally different career posture, built for a fundamentally different market.

Why T-Shaped Made Sense, And Why It Is No Longer Enough

The T-shaped model solved a real problem. Early in a career, being good at one thing gets you hired. Being good at only one thing gets you stuck. T-shaped gave practitioners a path: Go deep first, then build outward. It worked particularly well in agency environments where account managers needed enough breadth to have intelligent conversations across channels without needing to own them all.

The problem is that AI has quietly made T-shaped the new floor, not the ceiling. The State of PPC 2026 report, with over 1,306 responses, suggests that the skills now expected of a competent PPC manager include data analysis, first-party data activation, creative testing strategy, attribution modeling, prompt engineering, and scripting. That is not a job description for a specialist. It is the broad knowledge layer of a T-shaped practitioner, repackaged as the baseline requirement.

When the broad layer of your T becomes everyone’s minimum viable requirement, the T itself stops being a differentiator. What differentiates you now is what sits on top of it.

There is also a structural issue that the T-shaped model was never designed to address. A single deep specialism creates a single point of failure. If your specialism is automated, commoditised, or simply stops being valued by clients, you are exposed. Practitioners who built their identity around a single skill have already felt this. The M-shaped model spreads that risk across multiple pillars without sacrificing depth.

What M-Shaped Actually Means In PPC

M-shaped is not a new term, but it has barely been applied to paid media specifically. In talent and HR circles, it describes a senior professional with multiple areas of genuine depth connected by a wide base of contextual knowledge. Think of the shape literally: two or three peaks, not one, all sitting on the same broad foundation.

In a PPC context, the broad foundation could cover seven domains. Not mastery of each, but enough fluency to be credible, to ask the right questions, and to connect dots across them:

Broad knowledge layer (the base of the M) What fluency looks like in practice
Google Ads and paid search fundamentals Understanding platform mechanics, bid strategy, and campaign architecture at a working level.
Creative strategy Briefing creative from a performance hypothesis, not an aesthetic preference.
Data and analytics fundamentals Enough to interpret a dataset, build a basic model in Google Sheets or Looker Studio, and know when the numbers you are looking at are telling you something real versus something misleading.
Audience and first-party data Knowing what signals matter and how first-party data integrate.
Business fundamentals Reading a P&L, understanding margin, talking to a CFO.
Reporting and data visualisation Turning raw data into a decision, not just a dashboard.
CRO basics Enough to understand where paid traffic lands and why conversion rate affects the economics of every campaign you run.

On top of that base, the M-shaped PPC professional has two or three peaks. These are not sub-specializations within PPC. They are complementary disciplines that sit alongside it. The difference matters. Going deeper on Smart Bidding or Performance Max is valuable, but it is still PPC. Building genuine expertise in data engineering, CRO, SEO, business consulting, or marketing attribution is something different. It takes you into rooms and conversations that pure PPC expertise does not open. That is what the second and third peaks are for.

My own peaks are measurement and attribution strategy, AI-driven automation and scripting, and high-value commercial consulting. Importantly, these are not just deeper layers within PPC. They are distinct disciplines in their own right, each requiring a different knowledge base and opening access to different conversations. Attribution sits at the intersection of PPC and broader data strategy. Automation and scripting sit at the intersection of PPC and engineering. Consulting sits at the intersection of all of it and commercial strategy. That is the point. The peaks of an M-shaped profile should take you somewhere your PPC foundation alone cannot reach.

The specific peaks will differ for every practitioner. What matters is that they are genuinely deep, that they are visible, and that they are connected to each other and to the broad base in a way that makes sense commercially.

A sample M-shaped skillset could look like this:

Image from author, March 2026

Why M-Shaped Is Where The Premium Compensation Actually Lives

The salary data backs this up in a way that is hard to ignore. Duane Brown’s PPC Salary Survey 2026 shows that U.S. freelancers with 10 to 15 years of experience earn a median of $202,895, compared to $123,545 for agency practitioners at the same experience level. That is a gap of nearly $80,000 for the same years on the clock.

That premium is not explained by experience alone. It is explained by the ability to operate across disciplines. The practitioners earning at that level are not running campaigns for retainer fees. They are being engaged as experts who can bridge PPC with adjacent high-value problems: a consultant who understands both automation and business strategy, a specialist who can speak to attribution in a language the CFO recognises, a practitioner who can connect first-party data infrastructure to paid media outcomes. The peaks make that possible. The base alone does not.

The in-house data tells a similar story. The same survey shows a median of $170,000 for in-house practitioners with six to nine years of experience, against $90,000 for their agency counterparts at the same stage. That $80,000 gap reflects something structural: in-house senior roles, particularly growth-oriented ones, tend to be built around practitioners who own multiple critical functions rather than managing a portfolio of client accounts. They are hired for their peaks, not their base.

Agencies have to spread expertise across too many clients to let anyone go truly deep. In-house is where M-shaped profiles find the room to build.

This is worth sitting with if you work in an agency. Agency environments are excellent for building a range. You see more campaigns, more industries, more budget levels in two years at a good agency than you would in five years in-house. But agencies have a structural ceiling on depth: there are too many clients, too many accounts, too much context-switching for any one practitioner to genuinely own a problem from end to end. The practitioners who break through that ceiling are the ones who build their peaks outside the day job, through side projects, consulting work, speaking, writing, and building tools, and use the agency as the base, not the destination.

The Counterargument Worth Addressing

The obvious pushback to all of this is that M-shaped sounds good in theory but is unrealistic in practice. Most practitioners do not have the time or the organizational support to develop multiple genuine areas of deep expertise while also managing a full workload. And they are right that it cannot happen overnight.

But I think this objection confuses building M-shaped with being M-shaped. You do not arrive at M-shaped by trying to become an expert in three things simultaneously. You arrive there by going deep in one area first, then, once that pillar is solid enough to be commercially useful, identifying a second area where your first pillar gives you a natural edge. Measurement and attribution, for example, becomes a much more tractable second pillar once you already understand automation. If you know how Performance Max actually allocates budget, what signals Smart Bidding consumes, and where platform reporting diverges from reality, you are not approaching attribution as an abstract measurement problem. You are solving a specific one: how do you build a framework that accounts for what you already know the platform is doing wrong? That prior knowledge makes you faster, more credible, and harder to replace than someone who learned attribution in isolation.

The progression is not linear, and it is not fast. But the practitioners commanding $150,000 to $200,000 in this industry did not get there by deepening a single specialism forever. They got there by building a second peak, and then finding a way to connect the two.

What This Means For Where You Invest Next

If the argument holds that T-shaped is the new floor and M-shaped is where the premium lives, then the practical question is how to identify which second or third peak to build.

My honest advice is to start from your first peak and ask what adjacent problems your clients or employers consistently struggle with that you are currently not equipped to solve. If your peak is campaign automation, the adjacent problem is probably measurement: clients who have great automation in place but no reliable way to attribute outcomes to it. If your peak is creative performance, the adjacent problem is probably first-party data and audience strategy: clients who are producing great creative but targeting it at the wrong signals.

The peaks that compound best are the ones that are genuinely complementary, where depth in one makes you better at the other and more valuable to the businesses you work with. That is what separates M-shaped from simply having two T-shapes that happen to coexist in the same person.

The State of PPC 2026 report is unambiguous on the wider context: the performance gap between sophisticated advertisers and the average is wider than it has ever been. Platforms are not becoming more transparent, privacy constraints are not loosening, and competition is not decreasing. In that environment, the practitioners who will win are not the ones who are good at everything. They are the ones who are indispensable at two or three things that matter deeply to the businesses they serve.

T-shaped got a lot of us to where we are. M-shaped is what gets us to where the market is heading, and to a point where your career becomes genuinely difficult to commoditise or replace.

One last thing worth saying clearly: Do not be discouraged by this. M-shaped is not a certification you earn or a checklist you complete in a training sprint. It is the professional identity you build over a career.

The practitioners I know who have reached it did not set out to become M-shaped. They went deep on one thing, got good enough that it opened a door to something adjacent, walked through it, and repeated the process. That takes years, sometimes a decade or more. The fact that it takes that long is precisely why it is worth building. Anything that can be acquired in two or three years can be acquired by everyone. What you are working toward is something that cannot.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Feb 13 2026
15 Smarter Interview Questions For Hiring Digital Marketers In 2026 via @sejournal, @brookeosmundson

Hiring a digital marketer is no longer about finding someone who knows a few platforms well.

Most candidates can talk through Google Ads, social media, or analytics tools at a surface level. That is table stakes now. What separates a strong hire from a risky one is how they think when performance shifts, privacy rules change, or the data does not point to an obvious answer.

Marketing leaders today need people who can connect tactics to business outcomes, explain tradeoffs clearly, and adapt without panicking when the playbook changes. That is hard to uncover with generic interview questions.

The goal of this list is simple. These questions are designed to help you understand how a candidate approaches real-world problems, not just how well they have memorized terminology.

In many cases, the “why” behind their answers matters more than the answers themselves.

Here are 15 crucial interview questions to help you hire your next digital marketing teammate.

Tactical Knowledge Questions

The first set of questions focuses on an individual’s tactical knowledge of digital marketing.

1. How Do You Use AI And Automation To Improve Your Campaigns?

AI and automation aren’t just buzzwords anymore. They’re tools shaping how marketers work.

This question uncovers whether the candidate is using these tools for better performance or simply riding the hype wave.

  • What to listen for: Candidates should provide specific examples, such as using AI for bid adjustments in PPC or helping analyze campaign data for better optimizations. Red flags include vague responses or over-reliance on automation without understanding its impact.

2. What’s Your Approach To Building And Refining Audience Segments For Targeted Campaigns?

Audience targeting has become more nuanced, and it’s a skill you can’t skip.

This question dives into their strategy for reaching the right people at the right time.

  • What to listen for: Specific techniques like combining customer relationship management (CRM) data with platform insights or testing lookalike audiences. Be wary of candidates who rely solely on pre-set audience templates without customization.

3. How Do You Decide Which Channels Deserve Budget When Resources Are Limited?

This reveals prioritization, business thinking, and restraint. It also exposes whether the candidate understands incrementality, testing, and opportunity cost.

  • What to listen for: Thoughtful discussion around goals, marginal returns, test budgets, and tradeoffs. A red flag is defaulting to “we should be everywhere” without a rationale.

4. How Do You Leverage First-Party Data To Inform Your Campaigns?

First-party data is becoming increasingly valuable as the reliance on third-party cookies still remains questionable. This question uncovers how a candidate adapts to this shift of having a privacy-first mindset.

  • What to listen for: A candidate may talk about strategies like email segmentation, loyalty programs, or even how they’ve approached capturing first-party data to ensure they’re able to properly use them in campaigns. A potential red flag is relying on outdated cookie-based methods without a backup plan.

5. Can You Share An Example Of Using Cross-Platform Advertising That Has Driven Results?

As digital marketers, we know most campaigns aren’t “one and done” on a single platform. Candidates need to show how they think holistically about digital ecosystems.

  • What to listen for: Strong examples include integrating Google Ads with Meta campaigns or leveraging TikTok for awareness and retargeting on a different platform. A red flag is a candidate focusing only on one platform without considering how they interconnect and inform each other.

6. How Do You Decide What Metrics Matter Most When Reporting Performance?

Explaining results is just as important as achieving them. This question gets into their communication skills and ability to tell a story with data.

  • What to listen for: Clear alignment between business goals and metrics, plus examples of simplifying reports. Red flags include metric dumping or platform-first reporting. Examples of preferred reporting platforms and formats are a plus.

Strategic Knowledge Questions

It’s not only important to know how to do the job, but also to know why you’re doing what you’re doing.

The next set of questions allows you to dive deeper into the candidate’s mindset and see if they can put the strategic pieces together for clients.

7. How Do You Stay On Top Of Industry Changes, And What’s Something You’ve Learned Recently That Impacted Your Work?

The digital landscape changes every single day.

If someone isn’t staying current with best practices and platform changes, it can be detrimental to client success. You need to have someone on the team who is fully aware of any changes in the industry that could impact performance.

  • What to listen for: Understanding what methods a candidate uses to stay “in the know” is important. If a candidate says they’re too busy to set aside time to read up on trends, I’d consider that a red flag.

8. Have You Had To Pivot A Campaign Due To Changing Data Privacy Regulations?

Data privacy laws have changed the name of the game, especially in PPC.

This question tests how the candidate navigates regulations while keeping campaigns effective and compliant.

  • What to listen for: Look for examples like shifting to first-party data or adjusting targeting strategies in light of GDPR or CCPA. Red flags include ignoring compliance issues or struggling to adapt when audience data becomes restricted.

9. How Do You Measure Success Across Different Types Of Campaigns?

Success isn’t one-size-fits-all. The answer should show how they align goals, metrics, and performance analysis for various strategies.

  • What to listen for: Candidates should mention setting specific KPI goals based on the channel and objective of a campaign. Be wary of those who rely on vanity metrics like impressions without tying them to business outcomes.

10. How Do You Explain Complex Answers To A Client Or Someone In A C-Suite Role?

This will inevitably happen in any digital marketing role. It’s easy when you’re working as a team, and everyone knows the ins and outs of acronyms, in the weeds content.

Sometimes, you need to explain something like you’re talking to a third grader. Less is more.

  • Green flags to listen for:
    • Candidates who know how to navigate their language based on the role of the person they’re talking to.
    • When a candidate has the knowledge of basic business questions that the role cares about.
    • They know how to explain the “why” behind performance peaks and valleys.
  • Red flags to listen for:
    • Does the candidate dance around this question?
    • Is this candidate someone who might have difficulty thinking on their feet?
    • Do they believe in sharing too much data in order to avoid questions?

Culture & Fit Questions

This last set of questions is really looking at the long-term impact of your digital marketing hire.

You’re not looking to hire temporarily; you’re hiring for the long haul.

You want to feel confident in your candidate selection based on their character, the ability to collaborate with others (teams and clients), and, of course, the empathy factor.

11. What Is Your Management Style, And How Do You Ensure Alignment Within A Team?

Leadership and collaboration are critical in marketing roles.

This question helps assess how their approach complements your team dynamics.

  • Green flags to listen for: Strong candidates will mention fostering open communication, using clear goal-setting frameworks, or adapting their style to individual team members.
  • Red flags to listen for: If you notice any micro-management tendencies, or when the candidate avoids conflict resolution.

12. How Do You Balance Working Independently With Collaborating Across Departments?

Similar to the question above, digital marketers often juggle solo tasks with cross-functional initiatives.

Everyone performs their duties well in different scenarios. In some cases, digital marketers are required to work alone, on a team, or both.

This question highlights their adaptability to working together as a team versus in a silo.

  • What to listen for: Examples of successfully managing independent projects while aligning with other team departments. Be cautious of candidates who struggle to collaborate, communicate, or prefer working in silos.

13. Can You Describe A Time You Contributed To Maintaining A Positive Team Culture?

A strong company culture is key to retention and productivity.

This question reveals how they value and influence workplace dynamics.

  • What to listen for: Specific instances where they recognized a fellow colleague, facilitated team bonding, or helped resolve conflicts. Avoid candidates who dismiss culture-building as unimportant.

14. How Do You Handle Constructive Feedback, Both Giving And Receiving It?

Feedback is essential for any type of growth. This question assesses their ability to engage in productive conversations.

  • What to listen for: Look for examples of accepting feedback gracefully, acting on it, and offering constructive criticism thoughtfully. Red flags include defensiveness or avoiding difficult conversations.

15. What Are You Looking For In This Role?

Personally, I used to cringe at this question. Now, I find myself asking this to anyone I interview.

Bringing in a new person to an organization costs a lot of time and money. Think of all the training that goes into a new hire, the staffing that’s required to help train and mentor them, etc.

  • What to listen for: If they don’t have a clear answer, that’s a potential red flag. Are they simply looking for a stepping-stone position? While there’s nothing wrong with that, it’s better to know upfront to align expectations for both parties.

At the end of the day, do their motives fit in with your company’s culture and values? If not, they likely aren’t the right candidate.

The Real Goal Of These Interview Questions

Strong digital marketers are not defined by how many platforms they have used.

They stand out because they can explain their decisions, adapt when conditions change, and connect day-to-day execution back to business outcomes. Those traits rarely show up on a resume, but they surface quickly in the right conversation.

Use these questions as a framework, not a script. Listen for clarity of thought, intellectual honesty, and comfort with uncertainty.

The best candidates will not pretend to have all the answers. They will show you how they think through the hard ones.

At the end of the day, you are not hiring someone to manage channels. You are hiring someone to help steer growth.

These questions help you figure out who is actually ready for that responsibility.

More Resources:

Featured Image: Elenyska/Shutterstock

Ecommerce MGMT 0 Comments
Sep 15 2025
The CMO Vs. CGO Dilemma: Why The Right Leader Is Critical For Success  via @sejournal, @dannydenhard

Unless you have been living under a rock, you would have seen or experienced the evolution of marketing in recent years; often centered around the marketing leader and the chief marketing officer (CMO) role.

The CMO role has come under fire for performance, for the lack of big bang delivery, for not moving away from vanity metrics, and often being overly defensive at the leadership table.

Marketing Leadership Is Harder Than Ever

In coaching CMOs and equivalent titles, there are several recurring themes, one of which stands out in almost all coachees: Your job as a CMO is being a company executive first and then being a department leader.

You are in the C-Suite to represent the business needs, and business needs will trump your department and team needs, often going against how you are wired.

The business needs and the department needs shouldn’t be different. However, they are often at odds, especially when you, as the leader, haven’t placed the right guardrails; what often occurs is that you have followed poorly thought-through goals, key performance indicators (KPIs), and enabled disconnected objectives and key results (OKRs).

In other scenarios, the CMO role is being removed and not replaced, and the CMO title is removed. Repeatedly being replaced with VP, director, or “head of” titles, often resulting in the marketing leader not being in the C-Suite and regularly reporting one to two steps removed from the CEO.

Enter The Chief Growth Officer (CGO)

There are often reasons why there is a rebrand or title change within the C-Suite:

  • It is deliberate, changing the internal comms of the role. It demonstrates that, as a business, you are moving from marketing to growth or from old to new.
  • The removal of the previous CMO and legal requirements will dictate a change in title or a shift in job and description of the role.
  • If you work at a startup, it is often evolving the narrative with investors, which often helps frame previous struggles and drives the message that you are concentrating on growth.
  • There is also a showing of intent to the industry, often sending out press releases to show you are moving towards growth.

The Difference Between Marketing & Growth

The truth: The difference between marketing and growth setups is either negligible or a huge gulf.

Many confident marketing leaders would set up their teams in a very similar way; they would similarly set goals, but the department would work and operate in small ways.

The “Huge Gulf” Difference In Operating Includes:

  • Removing siloed teams of specialists.
  • Reducing and reframing the former way of defensive actions (Marketers have the hardest job and everyone thinks they can do marketing. Marketers have had to protect doing things that don’t scale and aren’t easily attributable).
  • Moving from not being connected to a truly cross-functional department.
  • Intentional reporting and proactively marketing more frequently and aggressively internally, which is the lost art in many marketing departments.

Like the best marketing organizations, the best growth departments are hyper-connected. They are intertwined cross-functionally, and they are pushing numbers constantly, reporting on the most important metrics and being able to tell the story of how it’s all connected. Reporting which KPI connects to which goal, how each goal connects up to the business objective, and how the brand brings performance.

Why The CGO Role Is Different

Skill Gaps

There are specific skill sets that differentiate successful CGOs from traditional CMOs – areas that often come up and stand apart marketing and growth. These include data fluency and the ability to crunch data themselves, adopting an experimentation-first mindset, being able to test, learn, and iterate as second nature, and everything CGOs do has revenue attribution baked in.

Customer Journey Ownership

Many CGOs are taking ownership of the entire customer lifecycle, and are happy to jump into product analysis and request missing product feature builds. There are many CMOs who struggle with the shift from leads and marketing qualified leads (MQLs) to customer lifetime values (CLVs).

Technology Integration

Often, CGOs have a greater understanding of tech stacks and the investment required in technical tools, and are more than comfortable working directly with product and engineering teams. Often the Achilles’ heel of CMOs.

Measurement Evolution

Growth leaders will often have sophisticated attribution models and real-time performance dashboards, focusing on performance across the board and being on top of numbers. Many CMOs can struggle with getting into the weeds of data and being able to talk confidently with the executive committee members.

External Stakeholder Management

CGOs will often have direct relationships with investors and board members, whereas “traditional CMOs” are regularly disconnected and have limited relationships with important management and investors.

Growth Department Challenges

In coaching CGOs, there are unique pressures that emerge in their sessions. The business requires its growth department to be accountable for every number and drive business performance through (almost all) marketing activities. No easy task.

The growth leader must evolve the former marketing approach into a fresh growth approach, which requires a new culture of performance, tactical refresh, a dedicated approach within teams in the department. That has to transform traditional disciplines following historical goals and tactics into the new growth approach. It’s no mean feat, especially in long-serving teams and traditional businesses.

The Long-Term Impact

Having built growth departments, holding both CMO and CGO titles, many long-term impacts are overlooked:

  • Stagnating Careers: Many team members can see their career stagnate if they are not brought onto the growth journey, and can feel because of their discipline, they are not considered a performance channel.
  • Specialist Struggles: In many marketing departments, there is a larger number of specialists and many specialists struggle with more integrated ways of working. It will be important for specialists to attempt to learn other skills and appreciate their generalist colleagues who will rely on them. Specialists are often those impacted most by the “marketing to growth” move.
  • Generalist Growth: Generalists are a crucial part of the move towards growth, often being relied upon to act as the glue and as the bridge. Generalists will need to understand the plan and connect with their specialist department colleagues, and help to shape and reshape.
  • Team Members Lost In The Transition: In any changeover, there will be team members who get lost. They will report to or through new managers, and will drift or will feel lost, and their performance will be hit. It is critical that all team members understand their plan and feel they are brought on the journey. Many middle managers are actually lost first. Ensure you keep checking in and have a plan co-created with the department lead.
  • Minding The Gap: The gap between teams can grow, and many teams can struggle to adapt to the change quickly enough. This also occurs when performance-based CGOs can overlook brand and retention teams.
  • Cultural Issues: Humans are averse to change. Now, opting out is the default, not opting in. It is on the team leads and the department head to bring everyone on the journey and make the hard decisions when members will not opt in.

The Path Forward: Lead Your Marketing Leadership Evolution

The shift from CMO to CGO isn’t just about changing titles or acting differently; it’s about fundamentally reimagining how marketing drives business growth.

For marketing leaders reading this, the question isn’t whether this evolution will happen, but how quickly you can adapt to lead the charge for departmental and business success.

Something I share in coaching is, if you’re a current CMO (or equivalent), you should step back and ask yourself the following questions:

  1. Are you already operating as a “CGO”?
  2. Are you deeply embedded in revenue conversations?
  3. Are you able to connect and drive cross-functional alignment and drive change?
  4. Do you positively obsess over business metrics that matter beyond your department?

If the answer is yes, you’re already on the right path. If not, it’s time to evolve before the decision is made above you or for you.

If this fills you with dread, then I can only be direct: You will have to learn to change your approach or get used to feeling the heat of business evolution.

For organizations considering this transition, remember that the best CGOs don’t just inherit marketing teams; they proactively transform them.

They build a culture where every team member understands their direct impact on business growth, where specialists learn to think and operate as generalists, and where the entire department becomes a revenue-generating engine rather than being considered a cost center.

Smart marketing leaders can also lead this transformation, but being able to prove they can evolve themselves and the people around them to this new way of working is critically important. A word to wise: Do not put yourself forward without knowing you are will be an essential leader in this new operating model and when it struggles you will be the leader they look to get the new system back on track.

The companies that get this transition right will see marketing finally claim its rightful seat (back) at the strategic table.

Those that don’t risk relegating their marketing function to tactical execution will see many of their competitors pull ahead with integrated growth strategies.

The choice now is yours: Evolve your marketing leadership to meet the demands of modern business, or watch as your competitors rewrite the rules of growth, while you’re struggling with metrics and influencing your business cross-functionally.

The future belongs to leaders who can bridge the gap between marketing’s art and growth’s science. The title will change and revert, but the question is: Will you be one of the modern marketing leaders, or could you be left behind?

More Resources:

Featured Image: Anton Vierietin/Shutterstock

Ecommerce MGMT 0 Comments
Sep 4 2025
Ask A PPC: How Do I Nail A PPC Job Interview For Google & Meta Ads? via @sejournal, @navahf

It is a wild job market right now, and if you’re applying for a PPC role, you’re probably feeling the pressure to stand out in interviews that are increasingly demanding and often unclear in their expectations.

Whether you’re interviewing for a specialist, manager, or hybrid media role, one thing is certain: You need to be ready to demonstrate platform expertise, strategic thinking, and the ability to connect performance with business outcomes.

One reader put it this way:

“I’m preparing for a performance marketing job, specifically in PPC, and I want to focus on Google and Meta ads. Have you any advice that would help me with interview preparation for these roles?”

This question is particularly timely because it doesn’t just ask about one platform. It is looking for dual fluency in Google and Meta, which represent paid search and paid social. That nuance matters.

Stopping there, however, is a mistake. Savvy employers will appreciate an applicant who can speak to Microsoft Ads, TikTok, LinkedIn, Pinterest, Reddit, and emerging platforms, even if those channels are not in scope right now. That breadth of perspective signals that you’re not just a button-pusher; you’re a strategist.

Below is a breakdown of the three core areas most interviewers will evaluate: Paid Search, Paid Social, and General Marketing and Culture Fit.

Paid Search Interview Prep (Google, Microsoft, Etc.)

Modern paid search, especially within Google, demands more than keyword-level tactics. You need to understand how campaigns serve business objectives.

Expect strategy questions like, “X business has Y budget and Z goals – what kind of campaign would you run and why?” Strong candidates will be able to discuss budgeting frameworks, auction mechanics, audience segmentation, and creative message mapping.

You will likely be asked about reporting. Expect to reference tools like Looker Studio, Google Analytics 4, Power BI, Adobe, or Triple Whale. Even speaking confidently about one tool while showing awareness of others can be impressive.

Mention tools like Microsoft Clarity when discussing conversion rate optimization. Behavioral analytics insights reinforce that you understand the full user journey and do not treat campaigns as isolated events.

One frequently asked question involves account structure. You might be asked, “Why would you structure a campaign/account this way?” Never cite “best practices” or default methods as your rationale. Interviewers want reasoning rooted in context, goals, and a test-and-learn approach.

Stay current on innovations. Be ready to speak about features such as Performance Max, audience expansion tools, or any other platform updates that impact strategy. Share why you find them valuable and how you would explain their relevance to a client.

To stand out even further, draw comparisons between Google and Microsoft Ads, or highlight how Reddit and Amazon are bringing new energy to the paid search space.

Paid Social Interview Prep (Meta, TikTok, LinkedIn, Etc.)

Paid social requires creative fluency, audience empathy, and an understanding of privacy constraints. These platforms are less about exact keyword intent and more about relevance, scale, and emotional resonance.

Prepare to talk about platform-specific ad types and creative strategies. Discuss how you would use Facebook, Instagram, WhatsApp, and Threads, and how your tactics might differ on TikTok, LinkedIn, YouTube Shorts, or Reddit.

Understand how platforms organize their campaign hierarchies. For instance, Meta emphasizes the ad set level for budgeting and targeting, whereas Google does not. Create a reference sheet for yourself so you can confidently speak to the differences during interviews.

Expect questions around creative production and reporting. Interviewers may ask, “What would you do if the client is picky about creative but refuses to supply any?” or “How would you prove that your campaign delivered results if the client questions the attribution?” These are behavioral and strategic tests rolled into one.

Be prepared to explain your approach to budgeting. Paid social often involves very large or very small budgets, and employers want to hear how you allocate funds based on audience size, objective, and creative lifecycle.

Show an understanding of creative testing frameworks, including how you develop variations of hooks, visuals, or calls to action across placements and formats.

General Marketing And Culture Fit

Some parts of the interview will focus less on tactics and more on how you think and collaborate. These are just as important to prepare for.

Be ready to answer questions like, “Tell me about a campaign that worked – and one that didn’t.” Use those stories to demonstrate analytical thinking, cross-functional collaboration, and your ability to learn from both success and failure.

You will also likely get questions about how you communicate performance. You might be asked how you handle underperformance and how you keep stakeholders aligned and informed during those periods.

Come prepared with thoughtful questions of your own. Ask, “What’s behind hiring for this role?” This can give insight into whether the role is tied to growth, turnover, or team restructuring. It also helps you gauge whether expectations are realistic.

Another useful question is, “What does success look like in this role?” This will tell you whether the role is tied to long-term strategic goals or short-term revenue. Follow that up with, “How will I be measured in the first six months versus the next two years?” This demonstrates that you are serious about growth and longevity.

Culture questions are also important. Asking, “Do people tend to hang out or do their own thing?” invites a conversation about the team dynamic, without feeling overly formal or forced.

Preparation Support

You do not need to prepare alone. Use AI tools like ChatGPT, Copilot, or Gemini to help you simulate interviews, organize your thoughts, or analyze job descriptions. Ask the AI to role-play as an interviewer and challenge you with platform-specific or scenario-based questions.

Use those tools to map out which metrics, frameworks, and features align with each platform. You want your prep to feel structured so you can walk into the interview with clarity and confidence.

Ultimately, interviews are not just an audition. They are a dialogue. Prepare thoroughly, think critically, and lead with the mindset of a strategist. That is how you stand out in a sea of applicants, and that is how you set yourself up for success.

If you have a PPC question you want answered in a future edition of Ask the PPC, send it in. Whether you’re prepping for interviews, troubleshooting performance issues, or pitching channel expansion, we are here to help.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
Aug 4 2025
Which SEO Jobs AI Will Reshape & Which Might Disappear via @sejournal, @DuaneForrester

You’ve probably seen the headlines like: “AI will kill SEO,” “AI will replace marketing roles,” or the latest panic: “Is your digital marketing job safe?”

Well, maybe not those exact headlines, but you get the idea, and I’m sure you have seen something similar.

Let’s clear something up: AI is not making SEO irrelevant. It’s making certain tasks obsolete. And yes, some jobs built entirely around those tasks are at risk.

A recent Microsoft study analyzed over 200,000 Bing Copilot interactions to measure task overlap between human job functions and AI-generated outputs. Their findings are eye-opening:

  • Translators and Interpreters: 98% overlap with AI tasks.
  • Writers and Authors: 88% overlap.
  • Public Relations Specialists: 79% overlap.

SEO as a field wasn’t directly named in the study, but many roles common within SEO map tightly to these job categories.

If you write, edit, report, research, or publish content as part of your daily work, this isn’t a hypothetical shift. It’s already happening.

(Source: Microsoft AI Job Impact – Business Insider – follow through this link to reach the download location for the original PDF of the study. BI summarizes the information, but links to MSFT, which in turn links to the source for the PDF.)

What’s Actually Changing

AI isn’t replacing SEO. It’s changing what “search engine optimization” means, and where and how value is measured.

In traditional SEO, the focus was clear:

  • Rank high.
  • Earn the click.
  • Optimize the page for humans and crawlers.

That still matters. But, in AI-powered search systems, the sequence is different:

  1. Content is chunked behind the scenes, paragraphs, lists, and answers are sliced and stored in vector form.
  2. Prompts trigger retrieval, the LLM pulls relevant chunks, often based on embeddings, not just keywords. (So, concepts and relationships, not keywords per se.)
  3. Only a few chunks make it into the answer. Everything else is invisible, no matter how high it once ranked.

This new paradigm shifts the rules of engagement. Instead of asking, “Where do I rank?” the better question is, “Was my content even retrieved?” That makes this a binary system, not a sliding scale.

In this new world of retrieval, the direct answer to the question, “Where do I rank?” could be “ChatGPT,” “Perplexity,” “Claude,” or “CoPilot,” instead of a numbered position.

In some ways, this isn’t as big a shift as some folks would have you believe. After all, as the old joke asks, “Where do you hide a dead body?” To which the correct answer is “…on Page 2 of Google’s results!”

Morbid humor aside, the implication is no one goes there, so there’s no value, and while that sentiment actually drops a lot of the real, nuanced details that actual click through rate data shows us (like the top of page 2 results actually has better CTRs than the bottom of page 1 typically), it does serve up a meta point: If you’re not in the first few results on a traditional SERP, the drop off of CTRs is precipitous.

So, it could be argued that with most “answers” today in generative AI systems being comprised of a very limited set of references, that today’s AI-based systems offer a new display path for consumers, but ultimately, those consumers will only be interacting with the same number of results they historically engaged with.

I mean, if we only ever really clicked on the top 3 results (generalizing here), and the rest were surplus to needs, then cutting an AI-sourced answer down to some words with only 1, 2 or 3 cited results amounts to a similar situation in terms of raw numbers of choice for consumers … 1, 2 or 3 clickable options.

Regardless, it does mark a shift in terms of work items and workflows, and here’s how that shift shows up across some core SEO tasks. Obviously, there could be many more, but these examples help set the stage:

  • Keyword research becomes embedding relevance and semantic overlap. It’s not about the exact phrase match in a gen AI result. It’s about aligning your language with the concepts AI understands. It’s about the concept of query fan-out (not new, by the way, but very important now).
  • Meta tag and title optimization become chunked headers and contextual anchor phrases. AI looks for cues inside content to determine chunk focus.
  • Backlink building becomes trust signal embedding and source transparency. Instead of counting links, AI asks: Does this source feel credible and citable?
  • Traffic analytics becomes retrieval testing and AI response monitoring. The question isn’t just how many visits you got, it’s whether your content shows up at all in AI-generated responses.

What this means for teams:

  • Your title tag isn’t just a headline; it’s a semantic hook for AI retrieval.
  • Content format matters more: bullets, tables, lists, and schema win because they’re easier to cite.
  • You need to test with prompts to see if your content is actually getting surfaced.

None of this invalidates traditional SEO. But, the visibility layer is moving. If you’re not optimizing for retrieval, you’re missing the first filter, and ranking doesn’t matter if you’re never in the response set.

The SEO Job Risk Spectrum

Microsoft’s study didn’t target SEO directly, but it mapped 20+ job types by their overlap with current AI tasks. I used those official categories to extrapolate risk within SEO job functions.

Image Credit: Duane Forrester

High Risk – Immediate Change Needed

SEO Content Writers

Mapped to: Writers & Authors (88% task overlap in the study: 88% of these tasks an AI can do today).

Why: These roles often involve creating repeatable, factual content, precisely the kind of output AI handles well today (to a degree, anyway). Think meta descriptions, product overviews, and FAQ pages.

The writing isn’t disappearing, but humans aren’t always required for first drafts anymore. Final drafts, yes, but first? No. And I’m not debating how factual the content is that an AI produces.

We all know the pitfalls, but I’ll say this: If your boss is telling you your job is going away, and your argument is “but AIs hallucinate,” think about whether that’s going to change the outcome of that meeting.

Link Builders/Outreach Specialists

Mapped to: Public Relations Specialists (79% overlap).

Why: Cold outreach and templated link negotiation can now be automated.

AI can scan for unlinked mentions, generate outreach messages, and monitor link placement outcomes, cutting into the core responsibilities of these roles.

Moderate Risk – Upskill To Stay Relevant

SEO Analysts

Mapped to: Market Research Analysts (~65% overlap).

Why: Data gathering and trend reporting are susceptible to automation. But, analysts who move into interpreting retrieval patterns, building AI visibility reports, or designing retrieval experiments can thrive.

Admittedly, SEO is a bit more specialized, but bottom or top of this stack, the risk remains moderate. This one, however, is heavily dependent on your actual job tasks.

Technical SEOs

Mapped to: Web Developers (not perfect, but as close as the study got).

Why: Less overlap with generative AI, but still pressured to evolve. Embedding hygiene, chunk structuring, and schema precision are now foundational.

The most valuable technical SEOs are becoming AI optimization architects. Not leaving their traditional work behind, but adopting new workflows.

Content Strategists/Editors

Mapped to: Editors & Technical Writers.

Why: Editing for humans and tone alone is out. Editing for retrievability is in. Strategists now must prioritize chunking, citation density, and clarity of topic anchors, not just user readability.

Or, at least, now consider that LLM bots are de facto users as well.

Lower Risk – Expanded Value And Influence

SEO Managers/Leads

Mapped to: Marketing Managers.

Why: Managers who understand both traditional and AI SEO have more leverage than ever. They’re responsible for team alignment, training decisions, and tool adoption.

This is a growth role, if guided by data, not gut instinct. Testing is life here.

CMOs/Strategy Executives

Mapped to: Marketing Executives.

Why: Strategic thinking isn’t automatable. AI can suggest, but it can’t set priorities across brand, trust, and investment.

Executives who understand how AI affects visibility will steer their companies more effectively, especially in content-heavy verticals.

Tactical Response By Role Type

Every job category on the risk curve deserves practical action.

Now, let’s look at how people in SEO roles can pivot, strengthen, or evolve, based on clear, verifiable capabilities.

High-Risk Roles: SEO Content Writers, Editors, Link Builders

  • Shift from traditional copywriting to creating structured, retrieval-friendly content.
  • Focus on chunk-based writing: short Q&A blocks, bullet-based explanations, and schema-rich snippets.
  • Learn AI prompt testing: Use platforms like ChatGPT or Google Gemini to query key topics and see if your content is surfaced without requiring a click.
  • Use gen AI visibility tools verified to support AI search tracking:
    • Profound tracks your brand’s appearance in AI search results across platforms like ChatGPT, Perplexity, and Google Overviews. You can see where you’re cited and which topics AI engines associate with you.
    • SERPRecon offers AI-powered content outlines and helps reverse-engineer AI overview logic to show what keywords and phrasing matter most. So, use a tool like this, then take the output as the basis for your query fan-out work.
  • Reinvent your role:
    • Write in chunks that AI can cite.
    • Embed trust signals (clear sourcing, authoritativeness).
    • Collaborate with data teams on embedding accuracy and chunk performance.

Moderate-Risk Roles: SEO Analysts, Technical SEOs, Content Strategists

  • Expand traditional ranking reports with retrievability diagnostics:
    • Use prompt simulations that probe content retrieval in real-time across AI engines.
    • Audit embedding and semantic alignment at the paragraph or chunk level.
  • Employ tools like those mentioned to analyze AI Overviews and generate content improvement outlines.
  • Monitor AI visibility gaps through new dashboards:
    • Track citation share versus competitors.
    • Identify topic clusters where your domain is cited less.
  • Understand structured data and schema:
    • Use markup to clearly define entities, relationships, and context for AI systems.
    • Prioritize formats like FAQPage, HowTo, and Product schema, where applicable. These are easier for LLMs and AI Overviews to cite.
    • Align semantic clarity within chunks to schema-defined roles (e.g., question/answer pairs, step lists) to improve retrievability and surface relevance.
  • Join or lead internal “AI-SEO Workshops”:
    • Teach teams how to test content visibility in ChatGPT, Perplexity, or Google Overviews.
    • Share experiments in prompt engineering, chunk format outcomes, and schema effectiveness.

Lower-Risk Roles: SEO Managers, Digital Leads, CMOs

  • Sponsor retraining initiatives for semantic and vector-led SEO practices.
  • Revise hiring briefs and job descriptions to include skills like embedding knowledge, prompt testing, schema fluency, and chunk analysis.
  • Implement AI-visibility dashboards using dedicated tools:
    • Benchmark brand presence across search engines and generative platforms.
    • Use insights to guide future content and authority decisions.
  • Keep traditional SEO strong alongside AI tactics:
    • Technical optimization, speed, quality of content, etc., still matter.
    • Hybrid success requires both sides working in sync.
  • Set internal AI literacy standards:
    • Offer training on retrieval engineering, LLM behavior, and chunk visibility.
    • Ensure everyone understands AI’s core behaviors, what it cites, and what it ignores.

Reframing The Opportunity

This isn’t a “get out now” scenario for these jobs. It’s a “rebuild your toolkit” moment.

High overlap doesn’t mean you’re obsolete. It means the old version of your job won’t hold value without adaptation. And what gets automated away often wasn’t the best part of the job anyway.

AI isn’t replacing SEO, it’s distilling it. What’s left is:

  • Strategy that aligns with machine logic and user needs.
  • Content structure that supports fast retrieval, not just ranking.
  • Authority based on more, deeper, sometimes implied, trust signals, not just age or backlinks. Like E-E-A-T++.

Think of it this way: AI strips away the boilerplate. What’s left is your real contribution. Your judgment. Your design. Your clarity.

New opportunity lanes are forming right now:

  • Writers who evolve into retrievability engineers.
  • Editors who become semantic format strategists.
  • Technical SEOs who own chunk structuring and indexing hygiene.
  • Analysts who specialize in AI visibility benchmarking.

These aren’t job titles (yet), but the work is happening. If you’re in a role that touches content, structure, trust, or performance, now is the time to sharpen your relevance, not to fear automation.

Final Word

The fundamentals still matter. Technical SEO, content quality, and UX don’t go away; they evolve alongside AI.

No, SEO isn’t dying, it’s becoming more strategic, more semantic, more valuable. AI-driven retrievability is already redefining visibility. Are you ready to adapt?

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: /Shutterstock

Ecommerce MGMT 0 Comments
Jul 10 2025
Ask An SEO: Should I Hire Candidates Who Can Use AI Tools Or Have Traditional Skills? via @sejournal, @HelenPollitt1

In this week’s Ask An SEO, a marketing manager asks which SEO skills are most valuable to look for in candidates today, especially with AI in the mix:

“I’m a marketing manager who’s been tasked with hiring our first in-house SEO specialist.

With AI tools becoming more prevalent, what skills should I prioritize when interviewing candidates in 2025? Are traditional SEO skills still as valuable, or should I focus more on candidates who can work alongside AI tools?”

This is a great question, and one I imagine a lot of hiring managers in the marketing industry are asking themselves.

For years, we’ve been looking for SEO professionals with skills that will help our websites thrive in Google, Bing, and Yandex. But, what skill set is needed for the emerging markets of ChatGPT, Perplexity AI, and Claude?

And what about keyword research, content creation, and technical audits? Are they still useful activities for SEO professionals to carry out manually when there are so many AI tools purporting to be able to do this for you now?

What Traditional SEO Skills Are Still Needed

We often think of skills within traditional SEO fitting roughly into three categories: technical, content, and authority-building. Are these still needed in the era of large language model (LLM) platforms and tools?

Technical SEO Skills

Ensuring that a website can be crawled, rendered, parsed, and indexed effectively by bots has been a staple of SEO for a long time.

If the bots can’t access the pages you want to have ranked, can’t read the content on them, or find the page to be unfriendly for users, you will struggle in the traditional search engine results pages (SERPs).

This isn’t all that different in the new world of generative engine optimization (GEO). Bots still need to be able to access content on your website, read it, and understand it.

Technical SEO skills will continue to be important to online visibility in the new era of organic discovery.

An excellent SEO will be someone who can utilize AI tooling to automate and speed up the checks they are already performing. The really valuable technical SEO skills will still be analyzing, prioritizing, and communicating the issues when they are discovered.

Good technical SEOs have been looking at ways to automate their processes using Python and Structured Query Language (SQL) for a while now.

AI is enabling them to do this quicker, and for those who are newer to those languages, to automate their processes more easily.

Hire SEO specialists who are excited to use AI tools to enhance their work, not replace it entirely.

You will still need SEO pros to be creative in problem-solving and working within the confines of your organization’s technology, resources, and capabilities.

Read more: 15+ Technical SEO Interview Questions For Your Next Hires 

Content Skills

AI-written content has been a hot topic for a couple of years now. Can AI replace human writers? Should you hire with content creation and marketing skills in mind, or can you leave that purely to AI now?

I would suggest that any SEO hire you make needs to understand how to craft engaging copy that clearly defines the brand and meets the needs of users at each stage of the buying journey.

This hasn’t changed much from when SEO pros brief writers and graphic designers in content creation. We still need SEO specialists to understand how to request engaging content, whether that be through AI or human creators.

The ability to define what will be engaging content through research (whether keywords or prompts) and how users engage with it (whether on the brand site or within the LLM’s answer) is still critical.

Read more: Generative AI And Social Media: Redefining Content Creation

Authority-Building Skills

Previously, there was an evolution in SEO from regarding authority building as getting backlinks by whatever means necessary, to acquiring links through engaging and relevant content.

For optimization in LLMs, the desire is more to cement a brand’s positioning and sentiment through mentions on other authoritative websites.

The skill set needed to acquire authoritative links through digital PR will not be that different from what’s needed to acquire mentions.

In fact, good digital PRs have recognized for a while now that brand mentions are valuable in their own right.

There is a need to understand the publisher who is being targeted, what they write about, when best to contact them, and how. This could well be automated to a good degree by AI.

However, the really excellent PRs build up relationships with their contacts, so they are front-of-mind when a story is breaking. This is something AI will struggle to replace.

When hiring for the digital PR side of SEO, look at their relationship-building skills in particular.

Read more: 3 Types Of PR & SEO Funnels That Will Maximize Conversions

Analytical Skills

AI has (thankfully!) taken much of the pressure off SEO professionals to be efficient mathematicians, proficient in Excel formulae, or, at least, having a good percentage calculator tool bookmarked.

Summarizing increases and decreases in key performance indicators (KPIs) is something AI can handle. It can highlight correlations between metrics and identify likely causes. AI can also summarize this all into a compelling report.

But, it still needs a human to determine if its recommendations are valid and a viable course of action.

A good SEO will be someone who can utilize the AI tools to draw conclusions and highlight issues, while retaining strategic oversight.

Strategy

That leads on to strategic skills. Good SEO pros will be able to utilize AI tooling for processes while drawing on their own deep contextual understanding and common-sense reasoning.

Hire SEO professionals who are adept at considering the moral and ethical implications of marketing and who can adapt to novel situations.

AI tooling will not be able to build trust with senior stakeholders. It will not be able to inspire and influence them. It definitely will not be able to manage egos and emotions like a good SEO has to.

Skills That Help In Emerging Markets

Beyond the skills that we’ve long been looking to hire for in SEO, it’s important to find people who are able to thrive in a burgeoning environment.

Great SEO pros have been cultivating these skills throughout their careers. Bad SEO professionals have scraped by on second-hand knowledge and following templated procedures.

Experimental Approach

Make sure they have the ability to experiment and apply their learnings.

We’re entering a new phase of SEO where what worked before might not work again. There are no experts in GEO yet; we’re all having to learn as we go along.

Make sure your candidates are willing to learn from trial and error.

Understanding Of How To Work With Uncertainty

The days of following an audit template are both long-gone and a way off. We can’t just apply what we know from SEO directly to GEO.

We need to learn what works in those new platforms. That means good SEO pros are going to have to be comfortable with the uncertainty in their industry again.

Seasoned SEO professionals will remember back to this during their formative years in the industry, but newer SEO specialists will need to break free of the “this is what works for SEO” mentality and be OK with adapting on the fly more.

Ability To Problem Solve And Investigate

This means they will really need to be keen problem-solvers. SEO, at its root, has always been about problem-solving.

With the suite of AI tooling growing, the temptation to delegate critical thinking to a machine will be great.

However, SEO pros will still need to be able to take a step back, consider all the context and angles, and work toward a solution given the resources and constraints they face.

This means that they cannot rely solely on AI to help them.

Read more: LinkedIn Lists Top 15 In-Demand Skills, Makes Related Courses Free

Hire For Complementary Skills

The answer to your question is yes. To both.

You need someone who can work alongside AI tools as well as having traditional SEO skills.

The experience and qualities of a seasoned SEO professional will still be extremely useful in the emerging world of LLMs and AI tooling.

It would be a risk to your organic performance if you hire solely based on whether the candidate can utilize AI tools well.

However, you do want to make sure the SEO pro is using all of the advantages that AI can bring. They need to be able to adapt to new technology and processes.

How To Interview For SEO Skills That Complement AI Solutions

The curiosity about new technology. The desire to experiment and adapt. Having an open mind to change. These are all attributes of good SEO professionals that are more important now than ever before.

When considering whether an SEO professional is a likely good fit for your role, find out their approach to new situations.

See how they have adapted in the past to changes in SEO that needed a change of tactics.

Ask them how they have diagnosed and responded to algorithm updates, or expanded their skill sets to include social media search engines.

Summary

In essence, the need for traditional SEO skills is not diminishing. However, great SEO professionals will be those who can adapt their skill set to work in GEO, as well as make the best use of new AI tooling available to them.

Alongside that, problem-solving, experimentation, and a keen strategic approach are what to look for in your next SEO hire.

More Resources:

Featured Image: Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments