**Global:** Enterprises face challenges integrating AI due to siloed data and outdated legacy systems. Capgemini and MongoDB’s partnership offers modern data solutions, enabling AI applications such as semantic search and predictive analytics, demonstrated in energy, automotive, and insurance sectors.
Enterprises are undergoing a significant transformation as artificial intelligence (AI) rapidly reshapes operational landscapes. However, a fundamental challenge remains: most legacy applications and data infrastructures were not originally designed to support AI-driven workloads. A recent analysis by Capgemini, alongside MongoDB, highlights how outdated enterprise systems, which primarily focus on transactional processes, struggle to integrate AI capabilities at scale.
The issue largely revolves around data fragmentation and the lack of AI-ready infrastructure. According to a global Workday survey cited by Capgemini, 59 percent of C-suite executives acknowledged that their organisations’ data remains partially or fully siloed. This segmentation impedes the seamless extraction of AI-driven insights because operational and analytical systems often exist separately. Moreover, cutting-edge AI applications such as retrieval-augmented generation (RAG), semantic search, and recommendation engines require advanced vector databases—a feature largely unsupported by traditional data architectures.
Developers face additional complexity due to disconnected systems, which necessitate manual syncing of data and reconciliation of queries across multiple platforms. This increases the operational burden and slows the deployment of AI, requiring organisations to modernise their data ecosystems to remain competitive.
To address these challenges, Capgemini and MongoDB have teamed up to provide an end-to-end solution aimed at modernising data infrastructure and enabling AI-powered applications. MongoDB offers a flexible document model capable of storing and querying structured, semi-structured, and unstructured data essential for AI applications. Its vector search capability supports semantic search, recommendation engines, anomaly detection, and RAG without the need for complex data pipelines, thereby reducing latency and operational overhead. Additionally, MongoDB’s distributed and serverless architecture ensures scalability suited for real-time AI workloads such as chatbots, intelligent search, and predictive analytics.
Capgemini complements this technological backbone by utilising AI-powered automation and migration frameworks to restructure enterprise applications, optimise data workflows, and facilitate transitions to AI-ready systems like MongoDB. Their use of generative AI assists organisations in analysing existing infrastructures, creating data migration scripts, and integrating AI functionalities seamlessly into their operations.
Several real-world use cases demonstrate the effectiveness of this collaboration. For example, a global energy company deployed a conversational AI interface to simplify data input for workers in hazardous environments like oil rigs. Instead of filling out complex 75-field forms manually, over 120,000 field workers now interact with the system using natural language, improving efficiency and safety.
In the automotive sector, AI-powered engine sound analysis has been utilised to identify anomalies and predict mechanical failures in advance. This innovation, powered by vector embeddings, has reduced vehicle breakdowns and optimised maintenance schedules, thereby lowering costs and improving reliability.
Another noteworthy application is GenYoda, an AI-driven solution developed by Capgemini for the insurance industry. By leveraging MongoDB Atlas Vector Search, GenYoda analyses large volumes of customer data—including policy documents, claims histories, and health records—to provide actionable insights. This capability has notably enhanced underwriting efficiency, allowing faster report generation and reducing manual effort. One insurer reported a 15 percent increase in productivity, a 25 percent acceleration in report turnaround, and a 10 percent reduction in the time spent on manual PDF searches.
As AI becomes increasingly integral to real-time operations and mission-critical processes, enterprises are urged to modernise their data infrastructure accordingly. The partnership between Capgemini and MongoDB exemplifies how businesses can overcome legacy limitations, unify fragmented data, and harness the next generation of AI-driven applications.
The Capgemini blog and a TechCrunch Disrupt session featuring Steve Jones, EVP of Data-Driven Business & Generative AI at Capgemini, alongside Will Shulman, former Vice President of Product at MongoDB, provide further insights and examples of AI innovation powered by this collaboration.
Source: Noah Wire Services
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