Building Hope: Accelerating Response for a Global Human Aid Organization

Humanitarian aid operations often face significant challenges like delays and resource constraints, making it harder to respond quickly and effectively during crises. Traditional methods can be slow due to fragmented data that requires greater manual effort, and are also prone to human error, limiting the scale and impact of relief efforts. Moreover, a lack of humanitarian funding often exacerbates these challenges.

In today’s interconnected world, the complexity and frequency of humanitarian crises require a new approach—one that is agile, data-driven, and scalable. This is where innovative technologies like Gen AI can make a profound difference. By leveraging tools such as AI-powered chat interfaces, automated data processing, and predictive analytics, humanitarian organizations can streamline their operations, accelerate decision-making, and improve service delivery to those in need.

When response time needs to improve, real-time insights, streamlined communication, and intelligent automation becomes more critical than ever. By embracing Gen AI, humanitarian organizations can enhance their operational efficiency, future-proof their efforts, and, most importantly, help more people in need, with greater speed and precision.

Our client faced significant challenges in delivering timely and accurate information to its large field force, which constituted more than 90% of its employee base. The lag in policy information, interpretation, and decision-making impacted millions of lives. This delay was primarily due to multiple revisions in policy documentation over the years and references to myriad sections and annexures. Some of the challenges included:

• Historical context dependency: Preventing timely updates to documents. 
• Large document sets: Covering global policies with complex interconnections. 
• Major SME dependency: Extracting the correct context from extensive documentation. 
• Siloed information: Information was fragmented across multiple documents and annexures. 
• Specific terminology and acronyms: Making it difficult to navigate and interpret documents.
• Separate document libraries: Storing use case-specific documents that were difficult to search and navigate.

As a chat platform, the solution is multipurpose and has been adopted by various other functions like the HR, finance, external relations, innovation, and IT teams to get an in-depth understanding of spend patterns anomalies and basic policy Q&A, respectively. The beauty of the solution is that it has been built with a customer-first mindset with emphasis on having high quality data in place which is key for success of any chat data platform.

To successfully transition the five Generative AI proof of concepts (POCs) into a production environment, the team adopted an Agile delivery model structured around 32 sprints, each spanning two weeks. This iterative approach enabled continuous development, testing, and refinement of features, ensuring alignment with evolving business requirements and technical feasibility. 

Throughout the 64-week timeline, cross-functional collaboration between application architects, program managers, data scientists, Gen AI engineers, infra developers, and DevOps teams facilitated seamless integration of Gen AI capabilities into the enterprise ecosystem. 

Regular sprint reviews and retrospectives ensured transparency, adaptability, and incremental value delivery, ultimately culminating in a robust, scalable, and production-ready Gen AI solutions for each BU.

The solution integrated Azure Cognitive Search, Azure OpenAI LLM, and responsible AI practices to ensure a secure, scalable, and user-friendly experience. It provided accurate, reliable, and efficient document interaction capabilities, setting a new benchmark for AI-driven productivity tools in the enterprise landscape.

“The business ’s Gen AI impementation is a paradigm shift for their organization and the greater humanitarian sector. The solution makes use of Azure OpenAI and other AI services to open new possibilities for improving efficiency, effectiveness, and innovation for similar organizations operating in the humanitarian aid sector.”

Global AI Strategy Lead

“Our data and integration team is doing great! The second chatbot is out, running on Azure. Great reception and utility across the organization, enabling staff to chat with HR and finance policies, job aids and other documents. It’s been an interesting journey but with a great team and business partners, all so worth it! Kudos to the entire LTIMindtree team!”

Chief BRM Officer, Data and Integration Collaboration and Communications Solutions