[AI]Han Heloir, MongoDB: The role of scalable databases in AI-powered apps

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As data management grows more complex and modern applications extend the capabilities of traditional approaches, AI is revolutionising application scaling.

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Han Heloir, EMEA gen AI senior solutions architect, MongoDB.

In addition to freeing operators from outdated, inefficient methods that require careful supervision and extra resources, AI enables real-time, adaptive optimisation of application scaling. Ultimately, these benefits combine to enhance efficiency and reduce costs for targeted applications.

With its predictive capabilities, AI ensures that applications scale efficiently, improving performance and resource allocation—marking a major advance over conventional methods.

Ahead of

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, Han Heloir, EMEA gen AI senior solutions architect at

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, discusses the future of AI-powered applications and the role of scalable databases in supporting generative AI and enhancing business processes.

AI News: As AI-powered applications continue to grow in complexity and scale, what do you see as the most significant trends shaping the future of database technology?

Heloir: While enterprises are keen to leverage the transformational power of generative AI technologies, the reality is that building a robust, scalable technology foundation involves more than just choosing the right technologies. It’s about creating systems that can grow and adapt to the evolving demands of generative AI, demands that are changing quickly, some of which traditional IT infrastructure may not be able to support. That is the uncomfortable truth about the current situation.

Today’s IT architectures are being overwhelmed by unprecedented data volumes generated from increasingly interconnected data sets. Traditional systems, designed for less intensive data exchanges, are currently unable to handle the massive, continuous data streams required for real-time AI responsiveness. They are also unprepared to manage the variety of data being generated.

The generative AI ecosystem often comprises a complex set of technologies. Each layer of technology—from data sourcing to model deployment—increases functional depth and operational costs. Simplifying these technology stacks isn’t just about improving operational efficiency; it’s also a financial necessity.

AI News: What are some key considerations for businesses when selecting a scalable database for AI-powered applications, especially those involving generative AI?

Heloir: Businesses should prioritise flexibility, performance and future scalability. Here are a few key reasons:

• The variety and volume of data will continue to grow, requiring the database to handle diverse data types—structured, unstructured, and semi-structured—at scale. Selecting a database that can manage such variety without complex ETL processes is important.
• AI models often need access to real-time data for training and inference, so the database must offer low latency to enable real-time decision-making and responsiveness.
• As AI models grow and data volumes expand, databases must scale horizontally, to allow organisations to add capacity without significant downtime or performance degradation.
• Seamless integration with data science and machine learning tools is crucial, and native support for AI workflows—such as managing model data, training sets and inference data—can enhance operational efficiency.

AI News: What are the common challenges organisations face when integrating AI into their operations, and how can scalable databases help address these issues?

Heloir: There are a variety of challenges that organisations can run into when adopting AI. These include the massive amounts of data from a wide variety of sources that are required to build AI applications. Scaling these initiatives can also put strain on the existing IT infrastructure and once the models are built, they require continuous iteration and improvement.

To make this easier, a database that scales can help simplify the management, storage and retrieval of diverse datasets. It offers elasticity, allowing businesses to handle fluctuating demands while sustaining performance and efficiency. Additionally, they accelerate time-to-market for AI-driven innovations by enabling rapid data ingestion and retrieval, facilitating faster experimentation.

AI News: Could you provide examples of how collaborations between database providers and AI-focused companies have driven innovation in AI solutions?

Heloir: Many businesses struggle to build generative AI applications because the technology evolves so quickly. Limited expertise and the increased complexity of integrating diverse components further complicate the process, slowing innovation and hindering the development of AI-driven solutions.

One way we address these challenges is through our MongoDB AI Applications Program (

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), which provides customers with resources to assist them in putting AI applications into production. This includes reference architectures and an end-to-end technology stack that integrates with leading technology providers, professional services and a unified support system.

MAAP categorises customers into four groups, ranging from those seeking advice and prototyping to those developing mission-critical AI applications and overcoming technical challenges. MongoDB’s MAAP enables faster, seamless development of generative AI applications, fostering creativity and reducing complexity.

AI News: How does MongoDB approach the challenges of supporting AI-powered applications, particularly in industries that are rapidly adopting AI?

Heloir: Ensuring you have the underlying infrastructure to build what you need is always one of the biggest challenges organisations face.

To build AI-powered applications, the underlying database must be capable of running queries against rich, flexible data structures. With AI, data structures can become very complex. This is one of the biggest challenges organisations face when building AI-powered applications, and it’s precisely what MongoDB is designed to handle. We unify source data, metadata, operational data, vector data and generated data—all in one platform.

AI News: What future developments in database technology do you anticipate, and how is MongoDB preparing to support the next generation of AI applications?

Heloir: Our key values are the same today as they were when MongoDB initially launched: we want to make developers’ lives easier and help them drive business ROI. This ******** unchanged in the age of artificial intelligence. We will continue to listen to our customers, assist them in overcoming their biggest difficulties, and ensure that MongoDB has the features they require to develop the next [generation of] great applications.

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