Are marketing specialists out of style?

The future of marketing belongs to those who can flex between strategy and execution, human insight and machine intelligence. This week, we explore what that means from two angles: first, why the age of specialists is giving way to the rise of the marketing generalist; and second, how Agentic AI is making that flexibility possible through tool use—letting AI decide when and how to act.

If you want to stay ahead in a world where boundaries blur between marketer and machine, this issue is for you.

Summary: According to new 2025 research from Marketing Week, roughly 15% of B2B marketers say they’ve cut back on agency spending over the past year thanks to their growing use of AI — while fewer than 2% reported increasing that spend.
Why it matters: B2B marketers are now expecting more from agencies as AI becomes standard, shifting spend, delivering higher expectations, and transforming how client–agency relationships work.

Summary: Even as AI tools proliferate in marketing, several major brands are launching campaigns that explicitly reject AI in favor of “real human interaction” as a trust-builder.
Why it matters: For B2B marketers, this is a reminder that just because you can use AI everywhere doesn’t mean you should—especially if authenticity and trust are key differentiators for your brand.

Summary: Marketers are increasingly moving from “we’ll try AI” toward structured approaches—selecting high-friction workflows (e.g., lead-qualifying, dashboards) and applying frameworks that align with business impact.
Why it matters: This gives strong tactical guidance for mid-level marketers: instead of chasing every shiny AI tool, pick a high-impact process and apply AI there with measurement and structure.

A great piece by Emily Kramer co-founder of MKT1, recently explored how AI is changing what it means to be a marketer. Their argument stuck with me: as AI expands what one person can do, the balance between generalists and specialists in marketing is shifting—fast. (Read their full article here.)

For years, large marketing orgs were built around specialists—people who went deep in one craft like demand gen, content, or ops. Generalists mostly sat in management, connecting all the moving parts. But that model doesn’t fit the AI era.

AI gives every marketer the ability to work across channels, automate execution, and even generate creative. The real differentiator now isn’t how deep you can go in one lane—it’s how well you can connect the dots between them.

That’s where the Gen Marketer comes in: a new kind of marketing generalist built for the generative AI age.

Here’s what defines a Gen Marketer:

Each major marketing wave created new specialists—the rise of digital, social, and PLG.

This one’s different. AI isn’t spawning new silos; it’s dissolving them.

The marketers who will stand out next aren’t defined by their title. They’re defined by their ability to see across systems, to think holistically, and to orchestrate humans and machines toward the same goal.

So will specialists go extinct? I don’t think so, not in massive organizations at least, but it’s a good bet that there will be fewer of them.

Quick Recap for my new subscribers: Each week for the next several weeks, this project playground feature will follow the latest Agentic AI course syllabus from DeepLearning.AI, taught by Andrew Ng (an AI legend), recapping what I learn each week through a product marketing lens. We’ve covered Modules 1 and 2 so far. In a few weeks, part two of this project will actually be building an agentic AI marketing workflow with me. 

From DeepLearning.AI’s “Deep Learning & Agentic AI” course

Tool use is where agentic AI starts to look a lot more like a teammate than a chatbot.
Instead of simply responding to prompts, the model learns when to take action — and which tools to use to get the job done.

Tool use allows an LLM to decide when to reach for external tools — like a calculator, web browser, or CRM connector — to complete parts of a task.

For product marketers:
Imagine an AI agent that can read your product positioning doc from Google Drive, analyze campaign metrics in HubSpot, and then generate new messaging recommendations based on what’s underperforming — all without you manually copying data between systems.

To get an LLM to use a tool, three things need to happen:

This pattern moves AI from simple prompt-response behavior to adaptive decision-making.

MCP is the emerging standard for how models access tools and data securely.
Think of it as a universal handshake between AI models and external systems.

Example:
When ChatGPT or Claude wants to pull insights from your Google Drive, it does so via MCP — securely connecting the client (AI) to the server (your data source).

Tool use transforms LLMs from idea generators into action-takers.

For product marketers, that means building AI systems that can:

Model Context Protocol

Yes, this has been featured before in this section, but it’s super important!!

Definition: MCP is the emerging standard for how models access tools and data securely. Think of it as a universal handshake between AI models and external systems.

As always, stay curious and have fun!

Best,
Skyler Neal