Dallas Cullen

About

I sit at the intersection of product, growth, and AI.

Most of my career has been spent on one question: how do you take a complex product and make the market want it? I've done this at Microsoft (leading GTM for a $20B+ AI sales channel), at McKinsey (advising F100 companies on GenAI monetization), and as a founder (building and selling a media company from scratch).

I'm drawn to AI-oriented companies because the GTM playbook is still being written. The winners won't just have the best models — they'll have the clearest story, the sharpest ICP, and the most disciplined path to revenue.

            $20B+
            AI Channel Owned
          

            $1B+
            Revenue Impact
          

            30+
            GTM Engagements
          

            3
            AI Product Launches
          

Perspectives

A few things I've learned from launching AI products and building GTM at scale.

01

What most companies get wrong about AI GTM

The default playbook for AI go-to-market is broken. Three mistakes I see repeatedly:

Selling capability, not outcomes.

"Our model has 100B parameters" means nothing to a buyer. The companies winning deals are the ones who can say: "We'll cut your claims processing time from 14 days to 2." Translate model capability into a business outcome the buyer already cares about.

Undefined or over-broad ICPs.

"Every enterprise is a potential customer" is a GTM death sentence for AI products. The best AI GTM starts narrow — one persona, one workflow, one pain point — and expands from a position of proof, not ambition.

Over-reliance on PLG.

Product-led growth works beautifully for horizontal SaaS. For AI products with complex integration, change management, and trust barriers, PLG alone stalls at the team level. You need a sales-assisted motion to unlock enterprise budgets. The real question is when and how to layer it in.

02

How AI changes product-market fit

Traditional PMF is binary: the market either pulls the product or it doesn't. AI products break this model in two ways:

PMF is continuous, not binary.

Because AI products improve with data and usage, fit strengthens over time within an account. The implication: your GTM needs to measure time-to-value and expansion velocity, not just initial conversion. A customer who churns at month 3 may have retained at month 6 with better onboarding.

The "product" includes the implementation.

For most AI products, the experience isn't just the software — it's the prompt templates, the workflow design, the integration with existing tools. This means GTM teams need to own more of the post-sale experience than they're used to. The line between product marketing, customer success, and solutions engineering is blurring, and the best GTM leaders lean into that.

Selected Work

Two examples of how I approach AI product launches and growth strategy.

Case Study

Launching an AI product in an undefined market

Problem

A Fortune 100 media company had built an AI-powered content tool but had no clear buyer, no pricing model, and no competitive positioning. The product team loved it. Nobody else understood what it was for.

Approach

Led customer discovery across 30+ stakeholders to identify the highest-intent buyer persona. Built a value-based pricing model tied to production hours saved (not seats). Developed positioning that anchored on workflow transformation rather than AI capability.

Insight

The initial target market (enterprise studios) was wrong. Mid-market production houses had more acute pain, faster procurement, and higher willingness to pay. Narrowing the ICP accelerated pipeline 3x.

Outcome

Launched to market with a clear narrative, monetization strategy, and sales enablement package. Product went from internal prototype to revenue-generating product line.

Case Study

Building a GenAI growth strategy for a $100B retailer

Problem

A Fortune 100 retailer knew GenAI mattered but couldn't quantify where to invest. Dozens of teams were running pilots with no central strategy, no prioritization framework, and no path to P&L impact.

Approach

Mapped 60+ GenAI use cases across the value chain, then built a prioritization model scoring each on margin impact, technical feasibility, and time-to-value. Worked shoulder-to-shoulder with product and engineering teams to validate assumptions.

Insight

The highest-value use cases weren't customer-facing (where leadership attention focused). They were in supply chain and merchandising — less visible but with 10x the margin impact and fewer regulatory barriers.

Outcome

Identified $1B+ in addressable margin. Delivered a phased roadmap adopted by the C-suite, with the first three initiatives moving to production within 90 days.

Background

Experience

Microsoft

Director of Product Marketing, AI @ Work

2024 — Present

Own GTM and product marketing for $20B+ AI sales channel. Lead positioning, launch strategy, and monetization for Copilot and cloud AI products.

McKinsey & Company

Engagement Manager

2019 — 2024

Led 30+ growth strategy engagements for F100 tech and consumer companies. Specialized in GenAI monetization, product-led growth, and GTM design.

DCMC Productions

Founder & CEO

2008 — 2014

Founded and sold a media company. Built partnerships with professional sports organizations and managed 100+ client stakeholders.

Google

Advertising Sales

2012

Analyzed customer insights and digital trends to develop advertising strategies for Google's largest retail clients.

Get in Touch

Let's talk.

I'm always happy to chat about opportunities where I can own GTM and drive growth. If that sounds like a fit, I'd love to connect.

dallaspcullen@gmail.com LinkedIn