I went straight to use cases: surfacing document summaries, letting users query their Box content via chat, that kind of thing.
Start by clarifying the user problem and business goal for integrating Box with ChatGPT, then outline a phased approach from discovery to launch. Emphasize technical feasibility, user value, and differentiation while acknowledging trade-offs and risks.
Pro tip: Focus on how the integration unlocks Box's content for AI-driven workflows, not just a generic chatbot. Highlight security and compliance as enablers, not blockers, to show you understand Box's enterprise DNA.
Identify the target user (e.g., knowledge workers) and the core job-to-be-done (e.g., quickly finding insights in Box content via natural language). Articulate a clear vision for how ChatGPT + Box creates unique value.
Evaluate Box's APIs (e.g., Content API, Search API) and ChatGPT's capabilities (e.g., plugins, function calling). Consider data privacy, permissions, and latency. Identify what's possible today vs. what needs new development.
Select 1-2 high-impact use cases (e.g., Q&A over documents, summarization) for an MVP. Define success metrics (e.g., task completion rate, time saved) and scope the MVP to validate value quickly.
Ensure the integration respects Box's permissions model and enterprise security requirements. Design for scalability and consider data residency and audit logs.
Outline a launch plan with pilot customers, gather feedback, and iterate. Consider pricing, packaging, and how to drive adoption within Box's existing user base.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.