Yen Anderson
Product and GTM advisor. AI strategist.

Enterprise AI products fail at the last mile. The technology works. What breaks is the organization, because people do not change how they work just because a tool arrives. Microsoft's Copilot for M365 was technically ready. The hard part was human adoption: getting 17,000+ employees across a global enterprise to fold AI into their daily workflows in a way that showed up as measurable business value.

I owned the full product adoption strategy: the rollout roadmap, the KPIs, and the 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 turned AI capability into role-specific workflows instead of generic training; (3) Built feedback loops from user research straight into the adoption roadmap, so onboarding flows and messaging could iterate fast; (4) Created a measurement model that tied product usage to business outcomes and made the ROI case visible to leadership.

Adoption is a product problem. Teams keep solving it like a training problem. The moment we treated onboarding as a product surface, with its own user stories, friction points, and iteration cycles, the engagement numbers changed. The frameworks I built for it are now standard across multiple Microsoft business units.

Enterprise AI advisory products are usually too generic to close at scale. They pitch capability with no clear wedge into a buyer's existing workflow. Vortexa AI needed a product strategy that could win mid-market and Fortune 500 accounts without a large sales team, and a GTM motion it could sharpen 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 opened mid-market entry while preserving enterprise ASP; (3) Designed onboarding as a product surface rather than a services motion, cutting time-to-value and lifting early retention signals; (4) Created a B2B content and enablement system, 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 steer roadmap decisions.

In an AI-native B2B product, the AI is not the differentiator. Every competitor reaches the same models. The edge is workflow integration and the story you tell around human judgment. Buyers are not paying for AI. They are paying for relief from a specific decision burden, and the product that names that burden most precisely wins the room.

Data science teams build the models. Shipping them as features falls to the product team. The space between the two is where AI value gets lost. At Neustar/TransUnion, enterprise AI fraud models were getting built and never 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 turning model capability into product requirements. Key decisions: (1) Partnered with data scientists to define product success metrics for each model, the business outcomes the customer cared about rather than raw technical accuracy; (2) Rebuilt the platform delivery architecture to decouple build and deploy cycles, cutting build time by 75%; (3) Led a 40% infrastructure cost reduction through systematic consolidation that became a repeatable framework; (4) Created a shared model lifecycle process that gave engineering and product one view of the same pipeline, closing the translation loss that had stalled time-to-value.

The hardest AI product problem is not building the model. It is defining what success looks like in the terms the customer uses to measure their own job. A model can be 94% accurate and still be a science project, if it never maps to the decision the customer is making. A product has to earn its place in the workflow.