Yen Anderson
Product leader. AI strategist.

Enterprise AI products fail at the last mile — not because the technology doesn't work, but because organizations don't change how they work. Microsoft's Copilot for M365 was technically ready. The challenge was human adoption: getting 17,000+ employees across a global enterprise to integrate AI into their daily workflows in a way that translated to measurable business value.

I owned the end-to-end product adoption strategy — defining the rollout roadmap, KPIs, and user segmentation model. Key decisions: (1) Prioritized a cohort-based rollout over a big-bang launch to build internal champions first; (2) Designed executive coaching frameworks that translated AI capability into role-specific workflows, rather than generic training; (3) Built feedback loops from user research directly into the adoption roadmap, enabling rapid iteration on onboarding flows and messaging; (4) Created a measurement model that tied product usage metrics to business outcomes, making the ROI case visible to leadership.

Adoption is a product problem, not a training problem. The moment we reframed onboarding as a product surface — with its own user stories, friction points, and iteration cycles — the engagement numbers changed. The frameworks I built to solve this are now standardized across multiple Microsoft business units.

Most enterprise AI advisory products are too generic to close at scale — they pitch capability without a clear wedge into a buyer's existing workflow. Vortexa AI needed a product strategy that could close mid-market and Fortune 500 accounts without a large sales team, and a GTM motion that could be refined quickly from early signals.

I own the product from roadmap to revenue. Key decisions: (1) Defined the ICP through structured customer discovery interviews with C-suite buyers, surfacing the exact job-to-be-done that justified budget; (2) Built a tiered pricing model that allowed mid-market entry while preserving enterprise ASP; (3) Designed the onboarding flow as a product surface — not a services motion — to reduce time-to-value and improve early retention signals; (4) Created a B2B content and enablement system including ROI calculators, competitive battle cards, and case studies that gave the sales team a repeatable close motion; (5) Established a product health dashboard tracking DAU/MAU, activation rate, NPS, and expansion MRR — reviewed with stakeholders monthly to drive roadmap decisions.

In an AI-native B2B product, the real differentiator isn't the AI — every competitor has access to the same models. It's the workflow integration and the story you tell around human judgment. Buyers don't buy AI; they buy relief from a specific decision burden. The product that names that burden most precisely wins the room.

Data science teams build models. Product teams ship features. The gap between them is where AI value dies. At Neustar/TransUnion, enterprise AI fraud analytics models were being built but not operationalized — the path from model output to product feature to customer value was undefined, slow, and fragile.

I sat at the intersection of systems engineering and product, responsible for translating model capability into product requirements. Key decisions: (1) Partnered with data scientists to define product success metrics for each model — not technical accuracy metrics, but business outcome metrics the customer actually cared about; (2) Rebuilt the platform delivery architecture to decouple build and deploy cycles, reducing build time by 75%; (3) Drove a 40% infrastructure cost reduction through systematic consolidation that became a repeatable framework; (4) Created a shared model lifecycle process that gave both engineering and product visibility into the same pipeline — eliminating the translation loss that had been killing time-to-value.

The hardest AI product problem isn't building the model — it's defining what success looks like in terms the customer uses to measure their own job. A model that's 94% accurate but doesn't map to a customer's decision workflow is a science project, not a product.