Organizational Knowledge Platform PoC
An organization is exploring a web-based corporate knowledge platform to capture, structure, validate, and reuse expert knowledge that stays trapped in individual experience, project teams, or informal practice — so that when experienced employees leave or rotate off projects, their nuanced procedures, decision paths, and special-case adaptations don't leave with them. It is meant to go beyond a Q&A forum, document repository, or basic wiki. Delivering a quick but working proof of concept.
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Demonstrate how organizational knowledge can move from scattered, informal expertise to an evolving, structured, AI-supported knowledge asset — a multi-sided platform where contributors capture reusable “scripts” for how work is actually done, users search and adapt them, and validators review and approve preferred methods.
Framing the platform as a multi-sided knowledge co-creation system for three sides — knowledge contributors, knowledge users, and validators or stewards — and differentiating it from wikis, Q&A forums, intranets, document repositories, and generic AI chatbots. Personas, empathy maps, and a journey from contribution through structuring, validation, reuse, and improvement, plus a structured knowledge model covering entry conditions, required inputs, steps, expected outputs, assumptions, risks, and variants. Excludes enterprise deployment, advanced AI model development, production-grade security, integration with internal systems, and full knowledge-base population.
A proof-of-concept web platform — a lightweight web application, mock-ups, or a clickable prototype — limited to the core workflows: a contributor captures a reusable method, workflow, procedure, or prompt sequence; a user searches for, interprets, applies, and adapts it; a validator reviews, approves or rejects it, manages variants, and identifies a preferred version; and knowledge evolves through updates and alternative branches, with AI-supported assistance turning informal descriptions into structured workflows. Representative use cases include a data-science method and a prompt-engineering procedure, presented in a workshop-style executive session.