Debug a spike in complaints from document workers on Frame.io 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’s Frame.io team has seen a sudden global spike in complaints from “document workers” — users who rely on Frame.io to upload, review, annotate, approve, and share documents such as PDFs, presentations, campaign assets, briefs, and client-facing creative files. These users often work in cross-functional review workflows involving marketing, legal, creative operations, agencies, and enterprise stakeholders, where version control, comments, permissions, and timely approvals are critical.

You are asked to lead the root-cause analysis for this spike. The issue is occurring at global scale, so the investigation should distinguish between a true product regression, changes in user behavior, regional or infrastructure issues, workflow-specific friction, integration failures, support-channel noise, or expectation gaps introduced by recent product, AI, collaboration, or enterprise workflow changes.

Your response should focus on how you would structure the investigation, validate the anomaly, segment the data, generate and prioritize hypotheses, identify evidence, coordinate mitigation, and prevent recurrence. Do not assume the cause upfront; show how you would narrow the problem in a high-stakes Adobe product environment where professional trust, collaboration reliability, and enterprise customer impact matter.

The experience should consider:

- How you would define and validate the complaint spike, including baseline period, complaint rate denominator, severity, duplicate reports, and channel mix.

- Segmentation by geography, enterprise account, document type, file size, browser/device, workspace role, plan tier, language, integration path, and workflow stage.

- Instrumentation checks across uploads, previews, comments, annotations, notifications, permissions, sharing links, approvals, search, and version history.

- Hypotheses spanning product bugs, latency, rendering issues, access-control problems, localization, recent releases, infrastructure degradation, AI-assisted features, and third-party integrations.

- Evidence sources such as telemetry, logs, support tickets, session traces, customer success notes, release timelines, incident dashboards, and qualitative user reports.

- Mitigation options, including hotfixes, feature flags, rollback paths, customer communications, support playbooks, and enterprise account escalation handling.

- Guardrails around privacy, security, document confidentiality, compliance expectations, and trust for professional document workflows.

- Prevention mechanisms such as monitoring, alerting, QA coverage, staged rollouts, regression tests, feedback loops, and clearer ownership across product, engineering, support, and customer success.

The goal is to demonstrate how you would run a rigorous RCA for a complex, global Frame.io issue affecting document-centric collaboration workflows, balancing speed of mitigation with confidence in the diagnosis and long-term product reliability.