What Product Marketing Looks Like in an AI-Disrupted World

Product marketing sits at a fascinating intersection in the AI transformation conversation. It combines strategic work that seems quintessentially human, such as positioning, messaging, and customer empathy, with production-heavy execution work that is ripe for AI adoption and optimization: sales decks, competitive battlecards, core product content production. As we look toward 2026-2027, what does the AI-intentional LINKproduct marketing function actually look like?

The answer isn't as simple as "AI takes over the repetitive stuff." The shift is more structural, more nuanced, and frankly more interesting than that.

For a deep and fairly authoritative dive into specifics, I highly recommend the Product Marketing Alliance’s “AI-Driven Product Marketing” playbook. It goes into detail on tools, trends, and implementations.

The Traditional PMM Pillars Are Under Pressure

Before we get to where things are heading, it's worth calling out the core pillars of product marketing that AI is already touching: product positioning and messaging, competitive intelligence, sales enablement content and training, go-to-market launch execution, and customer research and voice-of-customer analysis, with some areas I likely missed. In short, depending on your organization, if you are in product marketing there’s a strong change AI is already a core part of your daily workflow.

Some examples:

• Sales Enablement: The State of Sales Enablement Report 2025 found that 90% of organizations are either using AI for go-to-market efforts or planning to start. Battlecards are a good case study. Klue reports that AI-generated battlecards can be produced in under 60 seconds, versus the hours it once took. One SaaS company using AI-powered competitive intelligence saw a 65% reduction in time spent researching competitors. That's not marginal improvement; that's a fundamental workflow change.
• Competitive Intelligence: The most visible impact is in competitive intelligence analysis and content. According to the Competitive Intelligence Alliance, there's been a 76% year-over-year increase in AI adoption within CI teams, with 60% now using AI daily. What used to require hours of manual research and endless spreadsheet slogging more often now happens rapidly in the background.
• Product Messaging & Launch/GTM: AI is also now commonly handling first-draft messaging frameworks, launch/GTM asset production (emails, landing pages, one-pagers), customer call sheets and transcriptions, and personalized (at scale) sales collateral generation.

The AI-Intentional Product Marketing Model

Here is where I think the important conversation needs to happen, and where the "AI-first" framing that seems to be dominating the marketing narrative needs some nuance. As I've written previously, the distinction between AI-enabled, AI-augmented, and AI-first matters. Most PMM teams are likely somewhere on the journey between enabled and augmented. The question facing all marketing leaders, including product marketers, is whether "AI-first", where AI is the default and human involvement requires justification, is even the right target.

Especially in product marketing, for reasons noted below, I believe the AI-intentional model I discussed in that blog post linked above is the better model. This means intentionally evaluating and adopting AI and Agents not as blanket solutions, but as dictated by the risk factors, potential ROI, and technology and talent maturity. And critically for product marketing, and undeniably human factors that are central to the role.

BCG's research does a nice job framing the big picture opportunity that is top of mind for many CMO's: AI's real opportunity is helping CMOs "reinvent their entire operating model," not just automate repetitive tasks. For product marketing specifically, that means structural shifts in how teams are built and how work flows, but also important conversations that have to happen about where and when to keep AI out (or at least at a measured distance).

The PMM team in this world most likely becomes smaller but more senior and strategic. New hybrid roles emerge, most likely as direct evolutions current PMMs, who build and orchestrate AI agents and validate output rather than produce their own work from scratch. As a team less time on is spent on production, more time on the big picture and undeniably human parts of the job such as strategy, relationship building, talking to customers, and collaborating with sales and product.

The evolution of the workflow looks similar to many other marketing functions adopting AI: AI as the brainstorming partner, data collector/synthesizer, and "first draft" author. Human PMMs as editors, strategists, quality checks, and critical relationship drivers with customers and internal stakeholders.

The Uniquely Human Work of the Product Marketer

Harvard's analysis raises a concern I should have mentioned above, and is worth exploring in a future post: AI may eliminate entry-level marketing jobs, which begs the question of where the pipeline for future marketing leaders comes from. Are we looking at a "great hollowing out" of the marketing profession as we strive for production efficiencies with AI?

But their research also touches on what AI can't, at least for now, do well: validate accuracy, exercise ethical judgment, and maintain brand perspective. Similarly, Gartner projects that AI will handle around 30% of marketing operations by 2030, but humans will remain essential for storytelling, ethics, and brand judgment.

And this is what I call the "uniquely human work of the product marketer." Product marketing can both gain immensely from intentional adoption of AI, but it also must be protected from from overzealous AI enthusiasm to some degree. So much of makes a great product marketing team is undeniably human, in a way no LLM can mimic (at least for now):

• Customer connection and empathy: A, or perhaps the, central role of the product marketer is being the eyes and ears of the wider marketing team and by extension the overall organization. They need to be out talking to customers, not just recording their conversations but actually listening and connecting with them to truly understand their needs. AI can help synthesize and analyze what's captured, but it takes a talented human product marketer to empathize.
• Sales and product relationships: Product marketing sits by design at the nexus of the go-to-market (GTM) engine, connecting and aligning efforts of their counterparts in sales on one side and the product team on the other. Again, AI can provide a valuable assist, but these are human conversations, connections, and above all trust that needs to develop in order to make a GTM engine hum.
• Narrative storytelling: Perhaps the most important output of product marketing is the narrative messaging and storytelling that conveys the product value and competitive positioning to potential customers and partners. As Gartner noted above, humans are still central to true, authentic storytelling even as LLM's continue to gain ground.

The Tensions PMM Leaders Have to Navigate

I'm a big fan of highlighting "tensions" as a concept for leaders to navigate; it's the first dimension of my Disruption-Fluent Marketing Framework for this reason. When product marketing leaders consider their AI-intentional adoption plans, there are several tensions they need to take into account.

Speed vs. Authenticity. AI can produce content and messaging faster, and with increasing quality and rigor, but does it sound like your brand or like everyone else's? WSI World calls this the "homogenization crisis", a term I echoed in my own discussion around AI pragmatism and brand authenticity: when too many brands publish interchangeable AI content, created by the same best practices and algorithms, differentiation disappears into an amorphous blob. The strongest brands, they argue, are building "hybrid content systems" where AI handles research and production efficiency while leaning on humans for empathy and creativity.

Scale vs. Depth. More content doesn't mean better, something content marketers have known for years. This is especially true when it comes to the sales enablement content most product marketing teams spent the bulk of their time on. Sales teams can drown in, and outright reject in turn, AI slop just as easily as customers can. The product marketer's judgment about what to produce, what not to, when to infuse their own voice vs defer to AI, becomes more valuable than ever.

Efficiency vs. Customer Intimacy. If product marketers spend less time out in the field talking to real customers and more time immersed in AI prompts, what gets lost? The qualitative insights that emerge from sitting in a room with a prospect or customer, watching them use the product or react to a demo, hearing the words they use to describe their challenges and the tone and tension embedded in them: that's not easily replaced by AI-driven analysis of call transcripts.

A Parting Thought

Every marketer on the planet is under immense pressure to become fluent in AI and show they are adopting it in their teams and work. Pressure that comes from professional peers and industry (it's hard to not be concerned about being seen as a late-adopter or laggard) and certainly from within the organization (CEO/CFO/CMO's pushing for innovation and cost efficiencies that are the promise of AI).

The successful marketers, and in my view especially product marketers, will be the ones who navigate an AI-intentional adoption strategy while never forgetting what makes great product marketing undeniably human.