Congratulations on your degree. AI got the same one last Tuesday. 😅

The PwC data is the counterweight. Their 2025 Global AI Jobs Barometer (built on close to a billion job ads across six continents) found that jobs requiring AI skills grew 7.5% year on year, even as total job postings fell 11.3%. Workers with AI skills commanded a 56% wage premium over peers in the same roles without them, up from 25% the previous year. The most AI-exposed industries are seeing 3x higher growth in revenue per employee than the least exposed.

Read those two studies together, and the picture is clear. The problem is not AI. The problem is AI skills without domain depth, and domain depth without AI skills. Both halves are table stakes now.

What AI is actually replacing at entry level

It is worth being specific about this, because the generic “AI will take all marketing jobs” framing is useless. The tasks getting automated are the ones that made up most of a junior’s first 18 months: first-draft copy, basic SEO keyword research, social scheduling, templated email campaigns, routine reporting, data cleaning, and simple ad variant generation. These are structured, repeatable outputs… exactly what generative AI does best.

This is also why it stings. These were the training wheels. They were the work that built context, client instinct, and judgment. Without access to them, graduates either skip that phase entirely (and lack the judgment) or find ways to manufacture the equivalent experience themselves.

The Stanford team put it directly: AI displaces codified knowledge while complementing experiential wisdom. Your degree gave you codified knowledge. Employers still need the other kind, and the only way to get it is to work, which brings us back to the problem of getting hired in the first place.

The move that actually works

According to LinkedIn’s 2026 Skills on the Rise report, performance analysis is now the top marketing skill employers are hiring for, ahead of AI literacy, which held that position in 2025 and now sits second. This tells you something useful: AI literacy has become a baseline expectation, not a differentiator. The differentiator is being able to look at what a campaign produced and tell someone (clearly, specifically) what it means and what to change.

So the frame I suggested at RELACTION was this: stop thinking of yourself as someone who knows marketing, and start thinking of yourself as someone who can build and run a small marketing system (in different words, I guess).

One person who understands the fundamentals, knows how to brief and direct AI tools, and can automate the repeatable parts of the work is genuinely more productive per head than a traditional small team. That is the operating model of every lean agency that has survived the last two years.

The PwC data supports this. AI-exposed industries saw productivity growth nearly quadruple since 2022, from 7% to 27% between 2018 and 2024. That productivity lives somewhere. Right now, it lives mostly in people who know how to extract it.

Productising your work

This word gets thrown around, but the idea is simple. Repeatable marketing work, such as monthly reporting, content calendar production, lead capture sequences, and campaign templates, can be turned into a documented, automated system rather than something you rebuild from scratch each time. Once it is a system, it is faster, more consistent, and it scales without more headcount.

For an early-career professional, building these systems is both a productivity multiplier and the most credible portfolio you can show an employer. Not a certificate. Not a course completion badge. An actual workflow that does something useful, documented and reproducible. 👌

Think about it from the hiring manager’s side. Two candidates with similar degrees and similar grades. One shows you their LinkedIn profile and a certificate. The other shows you a Make workflow that automatically pulls campaign data from three sources, formats it, and emails a weekly summary to a client, and can tell you what it took to build it and what broke along the way.

That is not even a close call.

Pick one or two things and stay there

This is the piece of advice I find hardest to get young marketers to take seriously, probably because it feels counterintuitive when you are trying to appear employable across as many areas as possible.

The problem with positioning yourself as a generalist marketer is that there are tens of thousands of generalist marketers, and AI has made it even harder to stand out in that crowd.

When someone has a specific problem (email deliverability, B2B lead generation, automation for e-commerce) and they are looking for someone to help them with it, they are not searching for “marketer.” They are searching for the person who talks about that thing all the time.

This is what works for me, and I am not claiming it is the only approach, but the evidence in my own case is hard to argue with. For years, I have talked consistently about two things: email marketing and AI-driven marketing automation. That is it. Not content marketing, not influencer strategy, not brand positioning.

Two topics, repeated consistently, across every format and platform I use. The result is that when someone has a problem in one of those two areas, there is a reasonable chance they already know who I am before they contact me. That does not happen if you cover twelve topics.

For a fresh graduate, this is actually easier than it sounds. You do not need years of proof. You need a few months of consistent, specific output in one vertical, written content, documented experiments, short videos, whatever format suits you, focused on one specific corner of marketing. Pick something you find really interesting, because you will need to talk about it for longer than feels comfortable. The depth compounds over time. The breadth does not.

What to build in the next 90 days