This business sits at the intersection of two massive tailwinds: the structural shift to ad-supported streaming and the maturation of ML-powered ad decisioning. The product is already gaining traction with marquee customers, but the go-to-market engine doesn't exist yet. This plan lays out how to build it: the narrative, the enablement infrastructure, the demand engine, and the automation tooling to operate as a team of one.
The market is no longer "emerging." It's consolidating around scaled players, with a long tail of regional streamers, FAST channels, and niche OTT providers who need ad infrastructure but can't build it themselves.
An AI-native ad stack for large streaming providers. Enables platforms to build outcomes-based advertising businesses on their first-party data, without ceding control to walled gardens.
The CTV ad infrastructure market is crowded but fragmented across different layers of the stack. Understanding where the product fits is the first strategic task for PMM.
The brand voice is technical-aspirational: confident about ML, missionary about democratizing access, grounded in measurable outcomes. For Streaming Monetization, the PMM function organizes everything around a single thesis:
Streaming is shifting from subscriber-growth economics to advertising-revenue economics. The platforms that build their own ML-optimized ad stacks will own the economics of this transition. Those that outsource to walled gardens will not. That's what this product makes possible.
Four phases across 12 months. Each builds on the last. The founding PMM operates solo for the first two quarters, with headcount expansion in Q3-Q4.
The founding PMM role is inherently cross-functional. Success depends on building strong working relationships with each of these groups.
Each category tracks leading indicators (are we doing the right work?) flowing into lagging outcomes (is it working?).
A founding PMM doesn't have the luxury of a large team. AI-native tooling closes much of that gap. Six buildable agent workflows let a solo PMM operate with the throughput of a small team.
Monitors competitor websites, press releases, job postings, reviews, and social media. Summarizes weekly into a digest and flags high-priority moves like product launches, pricing changes, and key hires.
Pulls from the CI monitor and CRM win/loss data to suggest updates to battle cards. Drafts new objection-handling responses when new competitive claims emerge. Flags when a battle card is stale.
Transcribes recorded win/loss interviews, extracts key themes (decision drivers, competitive mentions, objections, feature requests), and aggregates findings across deals into trend reports.
Given a topic and target persona, generates a structured content brief: key messages, competitive angle, SEO recommendations, suggested structure, and relevant proof points from the case study library.
Ingests RFP documents, maps questions to existing content (product specs, case studies, security docs), and drafts responses. Flags gaps where new content is needed.
Aggregates CTV industry data (ad spend trends, FAST launches, subscriber growth, regulatory moves) into a live dashboard with AI-generated commentary on what matters for the business.
Twelve months in, a well-executed PMM function for Streaming Monetization should have delivered:
The company has the ML. It has the product. It has the customers proving it works. What it doesn't have is the story that makes the market care.
I build that story. Let's talk.