Design an experimentation dashboard for Firefly at global scale

Problem Statement Description

Product context: Adobe is a creative, document, and marketing software company; its products include Creative Cloud, Photoshop, Illustrator, Acrobat, Adobe Express, Firefly, and Experience Cloud.

Adobe Firefly supports generative AI workflows across creative tools, document workflows, and marketing use cases. Imagine Firefly teams are running many experiments globally: model updates, question UX changes, template recommendations, credit/pricing experiences, collaboration flows, and integrations into Adobe products. The audience for the dashboard includes product managers, data scientists, engineering leads, design, trust/safety, and regional business stakeholders.

Your task is to define what an experimentation dashboard should measure and expose so teams can make reliable launch, rollback, or iteration decisions. The dashboard must work across regions, languages, customer segments, platforms, and workflows, with particular attention to small businesses using Firefly for practical creative and marketing output.

The problem is not just to list metrics. You should clarify how experiments are interpreted at scale: what the core success metrics mean, how denominators are defined, how cohorts are segmented, how instrumentation quality is verified, and how guardrails protect user trust, creative quality, legal/commercial safety, latency, cost, and retention.

The experience should consider:

- Primary experiment outcomes for Firefly usage, activation, generation success, creative workflow completion, and business value.

- Clear metric definitions, including numerator, denominator, event source, attribution window, and eligibility criteria.

- Cohorts such as new vs. existing users, small-business customers, geography, language, subscription tier, Adobe app entry point, and use case.

- Instrumentation requirements for questions, generations, edits, downloads, shares, content safety events, latency, credits, and downstream workflow actions.

- Guardrail metrics for model quality, harmful or policy-violating outputs, user dissatisfaction, cost per generation, performance, fairness, and support escalations.

- Experiment-readout needs such as statistical confidence, sample size, exposure integrity, novelty effects, seasonality, and regional variance.

- Decision usefulness for product leaders: when to ship, hold, investigate, localize, rollback, or run follow-up experiments.

- Dashboard usability for multiple stakeholders, including drilldowns, alerts, annotations, and comparison across concurrent experiments.

The goal is to frame a metrics dashboard that enables Adobe to run trustworthy, globally scalable experimentation for Firefly while balancing growth, creative value, small-business adoption, operational efficiency, and responsible AI standards.