WBG Enterprise AI Knowledge Portal.
Case study

This case presents tThe AI Knowledge Portal serves as the Enable layer of the World Bank Group’s AI ecosystem, following the Access layer represented by AI Chat and AI Search.


While the Access layer gives employees direct access to AI capabilities, the Enable layer addresses what comes next: helping employees understand AI, learn how to use it, discover available capabilities, and adopt AI responsibly in their work.


The Portal provides the bridge between accessing AI and developing the knowledge and confidence needed to apply, experiment with, and eventually build AI solutions.

PRODUCT STRATEGY
Product Context
The AI Knowledge Portal serves as the Enable layer of the World Bank Group’s AI Platform, following the Access layer with AI Chat and AI Search.
Its role is to help employees navigate the growing AI ecosystem, from discovering available products and resources to learning, engaging with the AI community, using AI responsibly, and eventually building AI solutions.
AI Knowledge Portal brings these experiences together across five interconnected areas: AI Visibility, AI Education, AI Community, AI Governance, and AI Execution.
Business Problem
As AI capabilities expanded across the organisation, employees faced a fragmented experience around AI. Learning resources, use cases, governance guidance, and communities existed across different channels, making it difficult for employees to understand:
  • what AI capabilities were available;
  • how to learn and apply them;
  • where to find relevant guidance and expertise;
  • how to use AI responsibly;
  • where to go when ready to experiment or build.
The challenge was to create a coherent way for employees to navigate AI and progress from awareness and initial use toward confident, responsible adoption.
Product Opportunity
Instead of treating discovery, education, community, governance, and AI development as separate experiences, the Knowledge Portal could connect them around the employee's progression through AI maturity: Engage, Learn, Use Responsibly, Build.
The opportunity was to make AI adoption a supported product journey rather than a collection of disconnected resources.
Product Vision
Create a central AI knowledge and enablement destination that helps employees move from discovering AI to learning, applying, and ultimately building with it.
The Portal connects AI products, education, community, governance, and development resources into a coherent experience that supports employees at different stages of AI maturity.
AI Use Case Discovery
The product scope was shaped around different levels of AI maturity and the needs employees had as they progressed through AI adoption. User research, personas, and journey mapping helped identify the key capabilities required to support AI adoption journey.
These were translated into five core product areas:
  • Discover. Find internal AI initiatives and approved external AI capabilities.
  • Learn. Build AI knowledge and practical skills, learn from AI Champions.
  • Connect. Connect with AI communities, events, and organisational knowledge.
  • Use Responsibly. Access governance, policies, and responsible AI guidance.
  • Build. Access AI Factory and approved APIs to safely experiment with AI capabilities and evaluate their potential for new internal AI initiatives.
PRODUCT FOUNDATION

User-Centered Product Discovery

Supporting AI adoption across the organisation required understanding how different employee groups approached AI, based on their roles, objectives, responsibilities, and levels of AI maturity. User archetypes were developed to capture these differences and identify role-specific needs. These insights informed the Portal’s information architecture, content strategy, and prioritisation of core product capabilities.

Key Employee Segments

Supporting AI adoption across the organisation required understanding how different employee groups approached AI, based on their roles, objectives, responsibilities, and levels of AI maturity. User archetypes were developed to capture these differences and identify role-specific needs. These insights informed the Portal’s information architecture, content strategy, and prioritisation of core product capabilities.

Key User Journeys

The identified needs were translated into role-specific AI adoption journeys, from responsible AI usage and exploration to upskilling, community engagement, reuse, operational application, and AI development.

Each journey connected a specific user need with the relevant Portal capabilities and next step — creating multiple pathways through the same product rather than a one-size-fits-all experience.

  • 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

Product Architecture Diagram

The product vision was translated into a structured navigation layer for the World Bank’s AI Platform, bringing together the capabilities employees need across different stages of AI adoption.

The architecture organised the Portal around five core areas: Discover, Learn, Connect, Use Responsibly, and Build, connecting AI products, learning, community, governance, and development capabilities within a single experience.

Product Architecture Diagram
Beta Release
An initial beta version provided an early product foundation for validating the Portal’s structure, content model, and user experience before further evolution.
Что именно вошло в первую версию и какую минимальную проблему она решала.
LEARNING AND EVOLUTION

Product Diagram. Updated After Beta

Product Architecture Diagram

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.

SCALING AND IMPACT

The impact of AI-enhanced Search was assessed through usage patterns, employee feedback, and observed changes in knowledge discovery workflows. As an internal enterprise product, its value was reflected primarily in adoption, accessibility, efficiency, and the way employees interacted with institutional knowledge.

Enterprise Al Scaling
Scale:
  • Organization-wide rollout
  • 20,000+ internal users
  • Employees across 189 member countries

Organizational Impact:
Faster Access to Institutional Knowledge
AI-generated summaries reduced the effort required to identify and understand relevant information.

Reduced Knowledge Discovery Friction
Employees could move from a question to a synthesized answer without manually reviewing multiple result sets.

AI Adoption Through Familiar Workflows
AI capabilities were introduced through an established enterprise Search experience rather than requiring employees to adopt a separate tool.

Foundation for Broader Enterprise AI Adoption
Search provided a familiar entry point for introducing AI-powered knowledge discovery across the organization.
Supporting Evidence
Repeated AI Usage
~60% of users transitioned from initial exploration to repeated usage, indicating movement from experimentation toward regular AI use.

Workflow Efficiency
~50% faster task completion was observed for document-based workflows, demonstrating the potential of AI to improve knowledge-intensive work.

Reduced Reliance on External AI
~80% reduction in reliance on external AI tools indicated growing use of the internal AI environment for sensitive and organization-specific work.
Enterprise Strategic Impact
Enterprise AI Capability
AI Search introduced AI-powered knowledge discovery into an existing organization-wide workflow, expanding access to AI beyond standalone AI tools.

Knowledge Access
The product made institutional knowledge easier to find and understand, reducing friction across distributed knowledge sources.

Trusted AI Adoption
Grounded responses, visible sources, existing access controls, and a familiar Search experience helped introduce AI within a trusted enterprise environment.

Platform Impact
Search connected established knowledge discovery with conversational AI, creating a natural entry point into the broader World Bank AI platform.
ROLE AND DELIVERY
My Role
My official role at The World Bank was Senior Consultant. Working within the organisation’s AI-focused technology department, I contributed to the transformation of Enterprise Search into an AI-powered knowledge discovery experience, working across product strategy, discovery, prototyping, delivery, rollout, and continuous improvement.

My work focused on connecting existing Search capabilities and workflows with emerging AI capabilities, balancing user needs, organisational priorities, technical constraints, and enterprise requirements to shape the product’s evolution.

Product Strategy and Evolution: shaped the evolution of Enterprise Search from keyword-based retrieval toward AI-powered knowledge discovery, defining product priorities based on user needs, organisational goals, and AI opportunities.

AI Use Case Discovery: identified high-value enterprise Search use cases and explored how AI could improve knowledge discovery through hands-on prototyping and iterative validation.

Cross-Functional Collaboration: worked with engineering, data architecture, and business stakeholders to define AI-powered workflows, response logic, requirements, and priorities.

Delivery and Rollout: contributed to beta validation, product refinement, full rollout, and subsequent improvements, using user feedback, usage patterns, and adoption insights to guide product evolution
Delivery Approach
The product was delivered through an iterative Agile approach, combining structured planning, cross-functional collaboration, and continuous feedback.

Plan. Product priorities and roadmap were translated into epics, user stories, acceptance criteria, and tasks in Azure DevOps.
Explore. Product, engineering, data, and architecture teams worked together to explore solutions, clarify requirements, and align on technical feasibility.
Deliver. Work progressed through Agile/Scrum cycles, including planning, backlog refinement, daily coordination, and sprint reviews.
Learn and Iterate. User feedback, testing, and adoption insights informed product decisions and subsequent iterations.
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