AI is changing what software looks like entirely.

The lines between product and workflow are blurred. Interfaces are consolidating to chat windows where we work with agents. And the job of explaining what software does and why it matters just became more complex and important.

That challenge represents a big opportunity for marketers.

AI changed the product. Now someone has to explain it

We’ve all seen the posts about storytelling being one of the most in-demand skills in tech, with companies paying mid-six-figure salaries for people who can humanize their narratives and reach the right communities.

As new software companies introduce themselves and established players reinvent in the age of AI, the need for storytelling talent spans marketing roles:

• Social media and community professionals who understand what it takes to create platform-native content and build community.
• Communications and content professionals who can capture the company voice and value.
• Creator-focused marketers who can activate internal and external influencers.
• Creatives who can bring narratives to life across visual mediums.

There’s another marketing opportunity brewing in this disruption that might be most foundational of them all. And it’s one I’ve been close to in my recent work:

Product marketing for AI’s software revolution.

I spent five years as head of marketing at Talespin, an AI and VR startup with customers like Accenture, Meta, and PwC that built its enterprise distribution from scratch. In 2024, Talespin was acquired by Cornerstone OnDemand—an enterprise workforce development platform with 7,000 customers and 25+ years in market.

That transition has given me a front-row seat to both of the product marketing challenges I’m about to describe.

The SaaS battle: distribution vs. innovation

AI sparked a race across software industries between startups and incumbents. The question is whether the startup will get distribution before the incumbent innovates.

On one side: AI-native companies with new technology that solves customer problems, but narrow distribution, small sales functions, and brand trust that’s still being earned.

On the other: enterprises with the customers, contracts, and established brand channels, but the challenge of figuring out how to bring AI’s innovation to established platforms.

Wix Vice President of Marketing Paula Ximena Mejia captured the tension in a recent LinkedIn post:

“Companies are going to have to rethink what they actually are. Most will do this too slowly to save themselves. Product Marketing is about to become a foundational part of that survival strategy. Because before a company can rebuild, it has to answer a few hard questions: What do we actually own that cannot be replicated? What part of this workflow cannot disappear? If we were starting today, would we build the same product?

Those are not messaging questions, they are strategy questions that surface first in positioning and market analysis, and in the work of understanding why customers really choose you.” - Paula Ximena Mejia

Those are the strategy questions product marketers help companies answer.

The distribution climb for AI-native companies

I’ve been on teams that fought this battle. And it’s not easy.

To put things into perspective, I’ve seen full-time program managers dedicated solely to one consulting partner — at 100-person companies. One percent of our entire workforce, focused on a single relationship.

That’s the kind of investment it can take to earn enterprise distribution as a startup. At Talespin, our executives and commercial teams spent the better part of a year closing partnerships with firms like Accenture, Deloitte, Meta, and PwC. And while those partnerships added genuinely valuable scale to our GTM, the cost to get there — in time, resources, and organizational focus — was substantial.

Getting commercial partnerships done takes months. It also requires a deep bench of talented partnership, legal, marketing, and sales team members who know how to navigate procurement, standing up enablement programs, and managing risk-averse stakeholders.

That kind of distribution is earned and it doesn’t come fast.

Winning Fortune 1000 customers directly follows the same pattern. Long enterprise sales cycles demand the same investment in marketing programs, enablement, and commercial talent.

There’s also the roadmap pressure that comes with big customers and commercial partners. When a handful of enterprise logos represent a major chunk of your revenue, their use cases dominate internal conversations and board meetings. Those requests are hard to ignore — even when they pull you away from the bets you know you need to make.

This is the reality AI-native companies face. The product is often ahead of the go-to-market foundation. They’re building fast and selling fast, but the GTM infrastructure — the positioning, messaging, launch playbooks, partner enablement processes, competitive intelligence, and enabled commercial teams — often lags behind.

That gap is a PMM opportunity.

The AI-innovation race in the enterprise

I’m in the enterprise lane of the race now, and the advantages and challenges feel different.

The pressure for established software companies to ship AI features is intense — you hear it from the board, customers, analysts, and the market.

Everyone wants to know what your AI strategy is. Will you launch new AI-native products to keep pace with the flashy startups? Will the software that customers have trusted for years evolve to include agents and generative AI features?

The answer is likely yes. But adding AI to an existing product is harder than it sounds. You can’t just bolt a chatbot onto your architecture and call it a day.

The enterprise products that are navigating this phase the best are refining their value proposition and rallying around it — “AI-powered” or otherwise.

That strategy refresh requires someone who can look at an established enterprise platform and ask: what do our customers actually need to get done? What part of this product creates value regardless of whether it’s delivered via AI or otherwise? Where does our positioning create confusion, and where is it creating competitive advantage to lean into?

At Cornerstone, that work is happening across the business on several levels:

• Product and engineering are shipping AI capabilities to our platform while exploring new, complementary AI-native offerings.
• Product marketing is doing some of the most complex positioning work I’ve seen in my career. On one side, we’re writing the next chapter in the story of our market-leading platform, including its roadmap for AI capabilities. On the other, we’re trying to do justice to the new AI offerings and partnerships coming into the portfolio—products like Cornerstone for Agentforce and Cornerstone for Microsoft Copilot. And threading both into a single, cohesive narrative that captures their complementary benefits.
• Commercial teams are building those AI narratives and roadmaps into every customer conversation, arming themselves with the proof points and competitive context to speak credibly to what we’re building in a market where every competitor, enterprise or AI-native newcomer, has their own AI play.

I asked my boss Sudeep Cherian, VP of Product Marketing at Cornerstone OnDemand, what he’s been seeing in this transition:

“When I talk to product marketing peers and friends across the industry, we’re all comparing the same notes. SaaS is going through fundamental disruption — not just at the product level, but at the strategy level. New product launches, new pricing models, positioning overhauls from the ground up. The scope of what’s being asked of PMM teams right now feels pretty unprecedented.

And I don’t know exactly where it all lands, but I’m convinced product marketers have some of their most important work and their greatest career opportunities ahead of them.” — Sudeep Cherian, Vice President of Product Marketing, Cornerstone OnDemand

Having lived both sides of this, I know the PMM needs are real on each end of the spectrum. Startups need someone to build the foundation. The enterprise needs someone to reinvent it.

The PMM opportunity

Both competitors in the software market race need good marketing up and down the funnel. And that includes the foundational strategy and positioning that product marketing teams build.

Product marketing thought leader Yi Lin Pei reflected on what she’s seeing in her PMM advisory work in a recent newsletter:

“The core PMM craft isn’t going away. If anything, AI is raising the bar for quality, which means the fundamentals matter more than ever. In fact, from what I’m seeing in my own advisory work, demand for positioning and messaging has actually increased, not decreased.” — Yi Lin Pei

The demand for core PMM work is there. And it’s taking a different shape at each type of company.

AI-native company PMM needs

AI-native companies need GTM execution for their early product-market fit. Things like:

• Messaging and positioning for their core offerings that leads with value, not technology. Customers don’t care how you do it. They care what it does for them. AI needs to be the “how,” not the “what.”
• Product launch standards and go-to-market playbooks for teams moving fast without established process.
• Customer feedback cycles and roadmap influence programs to make sure the tech vision is solving the right problems.
• Competitive analysis to sharpen positioning and anticipate the market—especially in new categories or emerging segments of existing categories. The video production software market is one example, with a new segment emerging thanks to AI companies like HeyGen, Invideo, and Lumen5— a whole swath of new companies each player needs to keep tabs on.
• Vertical and industry strategies to capitalize on early demand signals before the window closes.

Enterprise AI PMM needs

On the enterprises side, companies need GTM support up and down the funnel as their AI product strategies come to market. Things like:

• Pricing and packaging for usage-based and hybrid SaaS/agent models — agentic software represents a genuinely hard problem that most legacy pricing structures weren’t designed for.
• Naming, messaging, and positioning for new AI agent frameworks and agent libraries being introduced across large SaaS portfolios. Many SaaS companies with portfolios are exploring single agentic architectures that deliver AI capabilities across their products in a consistent way. That becomes a product in and of itself—a product that needs to be named, have its value cleanly articulated, receive promotion for its key features, and perhaps even have its own release marketing cadence.
• Launch strategies for standalone AI applications as established players take a “two irons in the fire” approach — building AI-native solutions while modernizing their existing products in parallel.
• Enablement on the company’s AI narrative. Field teams are hungry for talking points as they hear about competitor AI offerings in customer conversations.

Walking the line between “here’s what’s new” and “here’s why you still need what you have” is one of the hardest positioning challenges in enterprise software right now. That challenge is one I’m sure plenty of PMM teams are working through.

How to take advantage

If you’re a PMM at an enterprise company, you don’t have to wait for permission. Look for where AI is creating internal pressure and volunteer to help.

Ask to work on the positioning and messaging for new your company’s new AI capabilities. Take a call with the team exploring AI-first pricing and packaging. Partner with the product teams building AI use cases within your portfolio.

Try to find yourself a seat at the table as this work gets done. Odds are the team would welcome the support.

If you’re looking to get into AI from outside an established company, pay attention to the founding PMM roles being created at early-stage AI startups.

Those fast-moving, emerging companies need versatile PMMs who can build their enablement and launch standards from scratch. And they need experience navigating selling into enterprise.

Plenty of those companies are looking for people who’ve learned GTM rigor at larger companies.

A few examples of new AI PMM roles that exemplify the trend on both sides:

• Anthropic: Product Marketing Lead, Agents
• Databricks: Product Marketing Director, AI
• Intap: Product Marketing Director — Agentic AI platform
• Norm Ai: Senior Product Marketing Manager
• Palo Alto Networks: AI Product Marketing Manager
• Stripe: Product Marketing Manager, Agentic Commerce
• Synthesia: Senior Product Marketing Manager, Content & Events

Final thoughts

The AI boom is a product story. And it’s a story businesses need product marketers to help tell.

The companies that navigate this moment well will be the ones that figured out who they’re for, who they are, and how to communicate it clearly in a market that’s moving faster than most GTM programs were designed to handle.

That’s the work of product marketers. And right now, that work has never mattered more.