Case Study 1: AI-Powered Chat
This case presents the evolution of an enterprise AI product at The World Bank, from a focused conversational AI MVP to a secure, governed workspace connecting employees with institutional knowledge and AI-powered workflows. The product evolved through user testing, continuous feedback, and the introduction of new AI capabilities and internal knowledge sources.
STRATEGY
Product Context, Business Problem
As AI rapidly changed the way knowledge-intensive work could be performed, The World Bank faced a broader organizational challenge: how to make AI a trusted and useful part of everyday work while preserving the security, integrity, and governance of institutional knowledge.
The organization holds a vast body of specialized knowledge across projects, reports, documents, and other internal sources. Employees spend significant time searching, interpreting, synthesizing, and transforming this information as part of their daily workflows.
Generative AI created an opportunity to fundamentally improve how employees interact with this knowledge, from finding relevant information to analysing documents, generating content, and supporting complex knowledge workflows.
However, adopting AI at enterprise scale required more than providing access to a general-purpose model. The organization needed an AI environment designed around its own data, users, workflows, security requirements, and governance principles.
The organization holds a vast body of specialized knowledge across projects, reports, documents, and other internal sources. Employees spend significant time searching, interpreting, synthesizing, and transforming this information as part of their daily workflows.
Generative AI created an opportunity to fundamentally improve how employees interact with this knowledge, from finding relevant information to analysing documents, generating content, and supporting complex knowledge workflows.
However, adopting AI at enterprise scale required more than providing access to a general-purpose model. The organization needed an AI environment designed around its own data, users, workflows, security requirements, and governance principles.
Product Vision
The initial product vision was to create a trusted conversational layer over World Bank knowledge, allowing employees to interact with institutional information through natural language rather than navigating fragmented systems.
The product was designed to become a foundation for enterprise AI adoption, connecting employees with institutional knowledge, AI capabilities, and reusable AI applications within a secure and governed environment.
The vision evolved around 4 main principles:
1. AI Enabled Knowledge Access. Transform how employees discover and interact with institutional knowledge by providing natural-language access to relevant information across trusted internal sources.
2. AI Assisted Workflows. Move beyond information retrieval toward supporting real workflows, analysing documents, synthesizing information, drafting content, translating, and generating new outputs.
3. Trusted, Governed AI. Create a trusted environment for employees to use AI within the organisation security and governance boundaries, with controlled data access, traceability, transparency, and responsible AI principles.
4. AI Platform. Use the product not only as an application, but as a foundation for broader AI adoption and capability building across the organisation, connecting new data sources, models, workflows, and reusable AI applications.
The product was designed to become a foundation for enterprise AI adoption, connecting employees with institutional knowledge, AI capabilities, and reusable AI applications within a secure and governed environment.
The vision evolved around 4 main principles:
1. AI Enabled Knowledge Access. Transform how employees discover and interact with institutional knowledge by providing natural-language access to relevant information across trusted internal sources.
2. AI Assisted Workflows. Move beyond information retrieval toward supporting real workflows, analysing documents, synthesizing information, drafting content, translating, and generating new outputs.
3. Trusted, Governed AI. Create a trusted environment for employees to use AI within the organisation security and governance boundaries, with controlled data access, traceability, transparency, and responsible AI principles.
4. AI Platform. Use the product not only as an application, but as a foundation for broader AI adoption and capability building across the organisation, connecting new data sources, models, workflows, and reusable AI applications.
AI Use Case Discovery
The discovery process focused on finding the intersection between generative AI capabilities and existing employee needs and workflows. Several areas showed strong potential for AI assistance:
- finding information across institutional knowledge;
- understanding and summarizing reports and documents;
- extracting insights from large amounts of content;
- drafting and transforming work-related content;
- translation and multilingual work;
- working with multiple documents and sources.
Enterprise Readiness
AI adoption is not only a technology implementation. It requires the organisation, its data, technology, and people to be ready.
Data: Data scientists and technical teams assessed internal repositories and selected the most relevant sources with appropriate metadata, tagging, and access controls for the MVP. Other sources were prepared for later integration.
Employees: AI Knowledge Hub supported adoption by providing learning resources, AI guidance, information on approved tools, risks, and responsible AI.
Enterprise Requirements: Security, data, technology, and governance teams worked with the product team to ensure the AI solution met organisational requirements and policies.
Data: Data scientists and technical teams assessed internal repositories and selected the most relevant sources with appropriate metadata, tagging, and access controls for the MVP. Other sources were prepared for later integration.
Employees: AI Knowledge Hub supported adoption by providing learning resources, AI guidance, information on approved tools, risks, and responsible AI.
Enterprise Requirements: Security, data, technology, and governance teams worked with the product team to ensure the AI solution met organisational requirements and policies.
PRODUCT FOUNDATION
High-Level AI Product Architecture
The MVP provides the starting point for the product, combining conversational AI with selected internal data sources and RAG-based retrieval. As the product evolved, the architecture was extended to support additional data sources and AI capabilities.
AI Architecture, RAG
The product used a Retrieval-Augmented Generation (RAG) approach to connect the LLM with relevant World Bank knowledge.
The system:
received the user's query,
retrieved relevant information from selected internal World Bank sources,
passed the retrieved context to the LLM,
generated a response grounded in that context,
returned the answer together with source references.
This approach allowed the product to work with organization-specific knowledge while keeping responses connected to the underlying sources.
The system:
received the user's query,
retrieved relevant information from selected internal World Bank sources,
passed the retrieved context to the LLM,
generated a response grounded in that context,
returned the answer together with source references.
This approach allowed the product to work with organization-specific knowledge while keeping responses connected to the underlying sources.
AI Governance, Responsible AI
Key elements:
Controlled access: access to the product and internal information is linked to authenticated World Bank employee identities and access permissions.
Data protection: internal data is handled within the organisation controlled environment and through approved external service integrations.
Grounded responses: AI responses is connected to retrieved source information to improve transparency and reduce unsupported answers.
Source traceability: users can access source references to understand where information came from.
Controlled access: access to the product and internal information is linked to authenticated World Bank employee identities and access permissions.
Data protection: internal data is handled within the organisation controlled environment and through approved external service integrations.
Grounded responses: AI responses is connected to retrieved source information to improve transparency and reduce unsupported answers.
Source traceability: users can access source references to understand where information came from.
AI Validation & Testing
AI products require validation beyond traditional usability testing. The product needed to be evaluated not only on whether the interface worked, but also on whether the AI produced relevant, grounded, and trustworthy responses.
Response quality: relevance and usefulness of generated answers.
Retrieval quality: whether the system surfaced appropriate internal information.
Source references: whether responses could be traced back to the retrieved sources.
User feedback: understanding where AI responses or interactions didn't meet user expectations.
Security: ensuring users could only retrieve information available to them.
Validation is treated as an ongoing part of product development rather than a one-time activity.
Response quality: relevance and usefulness of generated answers.
Retrieval quality: whether the system surfaced appropriate internal information.
Source references: whether responses could be traced back to the retrieved sources.
User feedback: understanding where AI responses or interactions didn't meet user expectations.
Security: ensuring users could only retrieve information available to them.
Validation is treated as an ongoing part of product development rather than a one-time activity.
MVP launch
The MVP launch helped validate workflows, observe user behaviour, identify issues, and gather feedback before expanding the product's capabilities.
Main focus on:
Main focus on:
- A conversational AI interface
- A limited set of internal knowledge sources
- RAG-based retrieval
- Source-grounded responses
- Secure employee access
- Controlled rollout to selected internal group
MVP Evaluation
Feedback on the MVP was collected through MS Viva Engage, with UX researchers reaching out to users directly. In parallel, moderated usability sessions were conducted. Users were given simple tasks, and their first interactions with the product were observed. This helped capture both user feedback and real user behaviour, providing the basis for key learnings and the next product iteration.
LEARNING AND EVOLUTION
MVP Key Findings
MVP feedback revealed that employees expected AI to support much more than simple question answering. They wanted to use it across broader knowledge workflows, finding information, working with documents, generating content, translating, and continuing work across conversations. This pointed to an opportunity to evolve the product from a conversational assistant into a broader AI productivity layer.
MVP Key Learnings
MVP feedback revealed several areas for further exploration. Users needed more guidance with structuring prompts, expected to upload and work with files and images. They also wanted more control over the knowledge used in responses, access to different AI models, faster way to provide feedback, and support for multilingual translation.
These findings shaped Product Iteration 1, which introduced improvements across prompting and onboarding, file input, conversation history panel, knowledge filtering, in-app feedback, sources panel, model selection, and translation.
These findings shaped Product Iteration 1, which introduced improvements across prompting and onboarding, file input, conversation history panel, knowledge filtering, in-app feedback, sources panel, model selection, and translation.
Product Iteration 1
From Conversational AI to AI-assisted Workflow
Key Product Changes
Better AI interaction:
- Guided onboarding
- Prompt Library
- Improved input and feedback patterns
- File and image upload
- Knowledge / dataset filtering
- In-chat translation
- History panel functionality updates
- LLM options
- Web Search
Key Learnings
As usage of the product grew, feedback revealed new needs that went beyond the single-conversation experience. Users needed to work with multiple documents and organize information around ongoing tasks, access relevant internal documents without uploading them manually, and discover reusable AI tools built for specific needs. They also expected more flexible ways to interact with the product, including mobile access and voice input, while the growing number of capabilities required a simpler and clearer interface.
These findings shaped the 2nd Product Iteration, which introduced Spaces for multi-document work, in-chat document search, an AI Agents Library, mobile access and further improvements.
These findings shaped the 2nd Product Iteration, which introduced Spaces for multi-document work, in-chat document search, an AI Agents Library, mobile access and further improvements.
Product Iteration 2
From AI-assisted Workflow to an Enterprise AI Workspace
Key Changes
AI Spaces, Multi-Document Work
- Grouped documents, conversations, and context
- In-chat search across internal documents
- Outputs based on multiple sources
- AI Agents / AI Apps Library
- Domain-specific AI tools
- Improved discoverability and filtering
- Voice input and transcription
- Mobile experience
- Simplified entry points and quick actions
Outcome
The 2nd Product Iteration expanded the product from a single-conversation experience into a structured AI workspace. Users could organise multiple documents and conversations into dedicated Spaces, search internal documents directly from chat, and work with information from multiple sources. In parallel, a curated AI Agents / Apps library surfaced reusable internal AI solutions across departments, creating a unified place to discover and use domain-specific internal AI capabilities.
A connected employee identity layer added a more personalized experience based on the user organisational context.
A connected employee identity layer added a more personalized experience based on the user organisational context.
SCALING AND IMPACT
Impact was assessed across both quantitative and qualitative dimensions, reflecting the nature of an internal enterprise product. Rather than generating direct revenue, the product value was measured through improvements in employee efficiency, adoption, knowledge access, and usage behaviour.
Enterprise Al Scaling
The product evolved from a conversational AI MVP into a broader enterprise AI workspace supporting knowledge access, document-based space, internal database search, reusable internal AI applications, and multiple AI capabilities.
Scale
Scale
- Organization-wide rollout
- 20,000+ internal users
- Employees across 189 member countries
- More internal knowledge sources
- Multiple AI models and search capabilities
- Multi-document workflows
- Reusable AI applications
- Mobile access
- More accessible institutional knowledge
- AI integrated into everyday knowledge workflows
- Foundation for broader enterprise AI adoption
Hard KPIs. Workflow value
Organizational Capability Shift
~60% of users transitioned from initial exploration to repeated usage, reflecting a shift from AI experimentation toward integration into daily workflows.
AI-Assisted Workflow Efficiency
~70% faster task completion for document-based workflows, highlighting the potential of AI to improve productivity and support knowledge-intensive work.
Trusted AI Environment
~80% reduction in reliance on external AI tools, indicating a shift toward using the internal AI environment for sensitive and organization-specific work.
~60% of users transitioned from initial exploration to repeated usage, reflecting a shift from AI experimentation toward integration into daily workflows.
AI-Assisted Workflow Efficiency
~70% faster task completion for document-based workflows, highlighting the potential of AI to improve productivity and support knowledge-intensive work.
Trusted AI Environment
~80% reduction in reliance on external AI tools, indicating a shift toward using the internal AI environment for sensitive and organization-specific work.
Soft KPIs. Adoption, usage
Improved access to institutional knowledge: employees could find and work with information through natural language rather than navigating multiple systems.
Reduced friction in knowledge-intensive work: AI supported searching, summarizing, translating, drafting, and working across documents.
Higher user confidence and trust: source references and controlled access helped users understand and verify AI-generated information.
Broader AI adoption: the product moved from experimentation with chat to integration of AI into everyday workflows.
Greater discoverability of AI capabilities: reusable AI Apps/Agents made specialized AI capabilities easier to find and use.
Better continuity of work. Spaces and conversation history supported longer-running tasks rather than isolated questions.
Reduced friction in knowledge-intensive work: AI supported searching, summarizing, translating, drafting, and working across documents.
Higher user confidence and trust: source references and controlled access helped users understand and verify AI-generated information.
Broader AI adoption: the product moved from experimentation with chat to integration of AI into everyday workflows.
Greater discoverability of AI capabilities: reusable AI Apps/Agents made specialized AI capabilities easier to find and use.
Better continuity of work. Spaces and conversation history supported longer-running tasks rather than isolated questions.
Enterprise Strategic Impact
The product became more than an internal chatbot. It evolved into a reusable enterprise AI capability that connected employees with institutional knowledge, AI models, search, documents, and reusable AI applications within a trusted environment.
Enterprise AI capability
Created a shared foundation for delivering AI capabilities across the organization.
Knowledge access
Made institutional knowledge easier to discover, understand, and use in everyday work.
Platform potential
Created a foundation that could be extended with new data sources, models, AI applications, and workflows.
Enterprise AI capability
Created a shared foundation for delivering AI capabilities across the organization.
Knowledge access
Made institutional knowledge easier to discover, understand, and use in everyday work.
Platform potential
Created a foundation that could be extended with new data sources, models, AI applications, and workflows.
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 evolution of enterprise AI products across the product lifecycle, from early exploration and MVP definition through delivery, rollout, and continuous improvement.
My work combined product strategy, AI use case discovery, hands-on prototyping, and cross-functional collaboration. I connected user and organizational needs with AI capabilities, technical constraints, and delivery requirements to shape product direction and priorities.
Product Direction and Prioritization: shaped product direction and priorities based on user needs, organizational goals, AI capabilities, and technical feasibility.
Discovery and Prototyping: explored AI use cases and product concepts through hands-on prototyping, user feedback, and iterative validation.
Cross-Functional Collaboration: worked with engineering, data architecture, and business stakeholders to define workflows, feature logic, requirements, and priorities.
Delivery and Evolution: contributed across MVP definition, delivery, rollout, and subsequent iterations, using feedback and adoption insights to guide product improvements.
My work combined product strategy, AI use case discovery, hands-on prototyping, and cross-functional collaboration. I connected user and organizational needs with AI capabilities, technical constraints, and delivery requirements to shape product direction and priorities.
Product Direction and Prioritization: shaped product direction and priorities based on user needs, organizational goals, AI capabilities, and technical feasibility.
Discovery and Prototyping: explored AI use cases and product concepts through hands-on prototyping, user feedback, and iterative validation.
Cross-Functional Collaboration: worked with engineering, data architecture, and business stakeholders to define workflows, feature logic, requirements, and priorities.
Delivery and Evolution: contributed across MVP definition, delivery, rollout, and subsequent iterations, using feedback and adoption insights to guide product improvements.
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.
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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