use case
AI-Knowledge Portal

Enterprise Knowledge Hub for Responsible AI Adoption

This case presents centralized internal knowledge platform created for the entire World Bank Group to support AI adoption across the organization by unifying access to learning materials, internal AI products, governance guidelines, APIs, and community-driven knowledge in one place.

By bringing together previously fragmented resources, the platform improves visibility, accessibility, and consistency of AI knowledge, helping employees move from awareness of AI to understanding and practical application in their daily workflows.

Role and Recommendation:

I played a key role in shaping and driving the product evolution from early planning and MVP exploration through implementation, organization-wide rollout, and multiple iterative releases. Worked cross-functionally with engineering, data architecture, and business stakeholders to define workflows and feature logic, align priorities, and translate user and organizational needs into actionable product improvements. Continuous feedback loops, pilot testing, and adoption insights guided ongoing iterations and expansion of the platform capabilities.

AI-Knowledge Portal position at the World Bank's AI Ecosystem

Problem Statement

  • No centralized AI communication layer
    There was no single channel to communicate AI initiatives, updates, and success stories across the organization, resulting in limited visibility into ongoing AI activities.
  • Low discoverability of internal AI products
    Internal AI tools and products were difficult to find and often remained unknown outside their originating teams, limiting reuse, cross-team awareness, and overall adoption.
  • Fragmented AI learning resources
    AI learning materials and training resources were scattered across multiple internal platforms and sources, making it difficult for employees to follow structured, end-to-end learning paths.
  • Lack of centralized AI governance
    There was no unified, easily accessible source of guidance on responsible AI usage, policies, and guardrails. This led to inconsistent understanding and application of AI across teams.

Product Vision

  • Unified AI communication layer
    Establish a centralized channel for AI initiatives, updates, and success stories to improve visibility and alignment across the organization.
  • Centralized discovery of internal AI products
    Enable employees to easily find, understand, and reuse internal AI tools and solutions, increasing cross-team awareness and adoption.
  • Structured AI learning experience
    Consolidate fragmented learning materials into a coherent, end-to-end learning journey that supports continuous AI upskilling.
  • Centralized AI governance and guidance
    Provide a single, accessible source of truth for responsible AI usage, policies, and guardrails to ensure consistent and safe adoption across teams.

AI Adoption Architecture through Knowledge Portal

The product vision was translated into a layered architecture that unifies fragmented AI capabilities into a single structure, enabling consistent AI adoption across the organization. The AI Knowledge Portal acts as a central entry point, consolidating distributed AI resources, knowledge, and guidance into one navigational layer. Instead of working with isolated tools, employees interact with a structured system that supports discovery, learning, governance, collaboration, and execution in one flow.

This design creates a continuous adoption experience, making AI more accessible, consistent, and scalable across the organization.

Key User Archetypes

As the portal was designed to support AI adoption across the entire organization, understanding the needs of different employee groups was a critical part of product discovery. A broad range of employee profiles with varying levels of AI experience, responsibilities, and objectives was analyzed to capture diverse adoption needs across the organization.

The user archetypes represent a selection of the key employee groups included in this analysis. Through empathy mapping, their goals, challenges, expectations, and attitudes toward AI were explored to surface common themes and role-specific needs. These insights informed the portal’s information architecture, content strategy, and prioritization of core capabilities, ensuring a unified experience that supports users throughout their AI adoption journey.

Key User Journeys

  • Govern
    Responsible AI Journey
    User: Any WBG Employee
    Need safe AI usage
    → Governance guidelines
    → Assess risk
    → Apply mitigation rules
    → Proceed safely
  • Discover
    AI Exploration Journey
    User: Any WBG Employee
    Need AI visibility org-wide
    → AI Knowledge Portal entry
    → Browse internal AI
    → Explore tools, use cases
    → Understand AI landscape
    → Identify entry points
  • Learn
    AI Upskilling Journey
    User: Any WBG Employee
    Need AI upskilling
    → Access Learn hub
    → Follow learning path
    → Complete OLC modules
    → Apply AI in daily work
  • Create
    AI Builder
    Journey
    User: Developer / IT
    Need to build AI solution
    → Access AI Factory
    → Explore APIs
    → Test in playground
    → Build and deploy solution
  • Align
    Executive AI Journey
    User: Senior Executive
    Need strategic AI overview
    → Review AI initiatives
    → Explore adoption insights
    → Understand maturity level
    → Support scaling decisions
  • Connect
    AI Community Journey
    User: Any WBG Employee
    Need engagement & knowledge sharing
    → Receive updates/events
    → Join community
    → Participate in discussions
    → Share experience
  • Scale
    AI Reuse
    Journey
    User: Cross-functional team
    Need existing AI solution
    → Discover internal product
    → Review documentation
    → Contact owning team
    → Adapt and reuse
  • Apply
    Ops Efficiency
    AI Journey
    User: Operations Manager
    Need process optimization
    → Identify inefficiency
    → Search AI for automation
    → Find relevant solution
    → Integrate into operations

From Architecture to Experience

Home Page. Entry Point

Curated homepage highlights combining learning modules, new AI products, and recent updates, providing a dynamic overview of the most relevant and up-to-date content across the portal.

Homepage acts as a preview into a deeper area of the portal, enabling users to seamlessly transition from high-level discovery to more detailed exploration. Whether it is training content, newly launched AI products, or organizational updates, the homepage is designed to surface the most relevant and timely information in a consolidated and accessible format.

The navigation system complements this structure through a highly organized and intuitive menu architecture. Each top-level category expands into a rich set of sub-sections, allowing users to quickly access specific areas of interest while maintaining clarity and consistency across the overall portal experience.

Internal AI Products Section. Discovery Layer

A structured catalogue of internal AI products designed to support easy discovery and reuse across the organization.

Products section serves as a centralized discovery layer, providing a structured view of internal AI solutions developed across the organization. It enables employees to navigate the growing AI portfolio through search, filtering, and categorization, supporting efficient identification and reuse of existing capabilities.

Each product is represented with a dedicated detail view that includes key functionality, ownership, and recent updates, ensuring transparency and clarity around available solutions. By consolidating access to internal AI products in one place, the page supports broader goals of AI adoption, cross-team visibility, and reduction of duplicated efforts across the organization.

AI Factory Section. Gateway to AI Development

A bridge between AI learning and AI implementation, providing access to development resources, approved API models, documentation and guided onboarding paths for users with different levels of experience.

The AI Factory section serves as a gateway to the organization's AI development, helping employees move from AI exploration to practical implementation. It provides structured access to development resources, approved models, APIs, documentation, and guided onboarding paths tailored to different levels of technical expertise.

By bringing together development guidance and access to AI building capabilities in a single entry point, the section lowers barriers to adoption and enables employees to more effectively explore, prototype, and develop AI-powered solutions. It also strengthens connectivity across the broader internal AI ecosystem by increasing visibility of available development tools and platforms.

Innovate Section. Central Channel for AI Ideas

This layer serves as a structured intake mechanism within the AI Knowledge Portal, enabling organization-wide capture of AI ideas for improving workflows.



Innovate section introduces a governed submission flow where ideas are first checked against existing internal AI products and prior submissions to reduce duplication and ensure alignment with the existing AI portfolio.

Validated ideas are then submitted through a standardized form that collects key details for evaluation by the AI team. This creates a controlled pipeline connecting employee-driven ideas with centralized AI governance and prioritization.

Delivery Process

AI Knowledge Portal was developed over a 2-year period using an iterative, Agile-based approach, with continuous validation and close cross-functional collaboration.

1. Problem Framing & Product Vision

The process began with identifying key organizational challenges around fragmented access to AI knowledge, limited visibility of internal AI capabilities, and inconsistent ways of discovering and using AI tools across the organization. These insights shaped a clear product vision for a unified AI Knowledge Portal, designed to provide secure, centralized access to AI learning resources, internal products, and development capabilities for employees.

2. Roadmap Definition

and Alignment

Based on the product vision, a clear and transparent roadmap was established, outlining key milestones, priorities, and expected evolution of the product.

The roadmap was accessible across teams, enabling shared understanding of:

  • Product direction
  • Scope and priorities
  • Team involvement and timelines.

3. Cross-functional Discovery

Early-stage exploration involved close collaboration across product, design, engineering, and architecture.

Work was driven through:

  • Collaborative workshops (whiteboards, workshop sessions)
  • Alignment discussions with technical architects
  • Iterative exploration of possible solutions
  • This helped translate complex requirements into feasible product directions.

4. Agile Delivery

via Azure DevOps

The delivery process was structured using MS Azure DevOps, with work broken down into:

  • Epics
  • User stories
  • Tasks

This ensured clear traceability from high-level goals to implementation.

5. Sprint Execution

and Scrum

Development followed an Agile sprint cycle, including:

  • Sprint planning
  • Backlog grooming
  • Daily syncs
  • Sprint reviews
  • Regular Scrum ceremonies ensured alignment, prioritization, and continuous progress across teams.

6. Iterative Development

The platform was developed through an iterative delivery approach, incorporating continuous improvements based on user feedback and cross-functional input. Each iteration focused on refining usability, information architecture, and navigation across the AI ecosystem to ensure a clear, consistent, and scalable portal experience.

7. User Feedback and Validation

Continuous feedback loops were embedded throughout the process via:

  • Usability testing sessions
  • Internal feedback channels
  • Direct user observation
  • Insights were used to refine features, improve usability, and guide prioritization.

8. Continuous Alignment & Delivery

Ongoing collaboration across teams ensured alignment between:

  • User needs
  • Technical constraints
  • Business goals
  • This enabled consistent delivery of a complex product while maintaining clarity, usability, and strategic direction.
Impact & Metrics
Impact was measured through a combination of product analytics, cross-module interaction tracking, task efficiency signals, adoption behavior changes, and qualitative user feedback.
  • 65%
    Alignment with Central
    AI Governance

    Share of users consistently referencing centralized AI guidelines and responsible AI policies during AI-related activities.

  • 90%
    Improved AI Tools
    Discovery Efficiency

    Time reduction in finding relevant AI tools, knowledge, and resources through centralized access vs. fragmented systems.

  • 90%
    Increased Internal AI Products Reuse

    Internal AI solutions that became discoverable and reusable across teams beyond their original development context.

  • 70%
    Adoption of Structured
    AI Learning Paths

    Share of users engaging with consolidated learning journeys instead of scattered, unstructured learning materials.

This is the essence of the AI-Knowledge Portal Use Case.

Available for further discussion on LinkedIn.

🖂 hi@foxalice.com / LinkedIn
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