Product Case Study

IBM Watson Media — Product leadership on live‑streaming SaaS

Product Manager for enterprise live‑streaming platforms; partnered with design and engineering to deliver releases and integrate AI/ML and IoT features.
Role: Senior Product Manager
Timeline: 2016-2017
Platform: Media / Streaming SaaS

Executive Brief

  • Mandate: Own roadmap and cross‑functional delivery for live‑streaming SaaS offerings.
  • Scope: Partnered with Design & Engineering across releases; coordinated with internal/external stakeholders.
  • Levers: Discovery→roadmap prioritization, Partner orchestration, Release planning & analytics
  • Results: Shipped live‑streaming releases; integrated AI/ML and IoT features into the product portfolio.
Shipped releases
AI/ML + IoT integration
Partner coordination

Mandate & Constraints

High‑visibility media programs with complex technical dependencies and partner expectations. Needed to balance roadmap ambition with platform stability and performance requirements.

Decision Framework

Customer & Partner Value
Prioritize capabilities that unlock audience value and meet partner commitments.
Technical Risk First
Surface streaming, latency, and scale constraints early; timebox spikes and proof‑of‑concepts.
Evidence‑Driven Roadmaps
Use telemetry, user feedback, and operational metrics to plan releases and measure impact.

Plays I Ran

Discovery → Roadmap
Structured discovery with design & engineering; converted insights and constraints into sequenced epics and releases.
Partner Orchestration
Aligned stakeholders on scope and timelines; coordinated integrations with internal teams and third‑party partners.
Release Planning
Defined release criteria, tracked readiness, and communicated status to leadership and partners.
Telemetry & Feedback Loops
Instrumented key flows; reviewed usage and performance to inform subsequent roadmap decisions.

Outcomes & Signals

  • Released features that strengthened the live‑streaming portfolio and partner use cases.
  • Integrated AI/ML and IoT components into product experiences with measurable operational guardrails.
  • Improved predictability of launches via clearer sequencing and readiness criteria.

Evidence

I don't have any artifacts at the moment for this case study.

What I'd Do Next

  • Define outcome metrics for audience impact and partner value.
  • Scale a streaming quality dashboard (latency, error rates, QoE) to inform prioritization.

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