Why your databases could be the AI advantage you’re missing

Over the last year, we’ve heard it time and again: “We need AI, but our data is holding us back.” As AI experimentation transitions to real-world applications, organizations are realizing that while they are ready to embrace AI, their existing data infrastructure isn’t. 

AI thrives on reliable, diverse, and readily-available data. To learn and generalize effectively, AI models benefit from substantial training data, both structured and unstructured. And for organizations to use these models, data must also be accessible precisely when and where it is needed. Additionally, AI introduces specific requirements, such as grounding gen AI models in enterprise data to ensure accurate, relevant, and complete responses.

The bottom line is that AI demands modern databases that can keep pace in the AI era — and data leaders will need to evolve their existing data platforms to deliver.

Redefining databases in the AI era

We are seeing a historic inflection point in the role of databases in the application stack. For multiple reasons, this shift may be the single most fundamental change in the database landscape in decades.

First, a new application stack is emerging where core capabilities can now be delivered directly through underlying technologies using a combination of AI and data. Already, these new applications are being utilized alongside traditional applications. For instance, integrating a gen AI agent with a system of record could enable you to generate reports on sales, inventory levels, or operational metrics by chatting directly with the database in natural language. This direct interaction represents a powerful paradigm for delivering business value, potentially reducing the reliance on conventional business logic, although the full extent of how these agents will augment existing applications is still evolving. 

This development necessitates databases that handle more complex and diverse data. Business operations today involve diverse data formats, including unstructured data like text, images, and videos. Therefore, modern databases must be equipped to efficiently manage and integrate a broad range of information alongside  structured data to support both existing and emerging AI-driven applications.

As a result, traditional databases will evolve to offer better integration with AI tools and models. These innovations include natural language interfaces, advanced search capabilities that combine structured and multimodal data with advanced AI techniques to deliver higher performance and accuracy when searching and querying data.

In addition, many legacy databases may become overloaded due to the high volume and speed required to enable AI. These systems can face scale and performance limits because AI-driven workloads can substantially increase the volume of queries. Research also indicates that roughly 90% of enterprise data is collected and stored but never used in any meaningful way. This, so-called “dark data,” often locked away or forgotten in various systems, represents a valuable opportunity for businesses to gain a better understanding of their customers, improve decision-making, and drive innovation to build a competitive edge. However, to do so, organizations will need databases that can integrate and deliver access to this less frequently used data.

All together, these factors are fundamentally reshaping databases, transforming how we view and utilize them. Modern databases are expected to do much more than store data; they are increasingly required to incorporate metadata, capture the semantics of data, including meaning, context, and relationships, and to ensure the data can all be easily used and interpreted by AI systems. 

As a result, databases are evolving into collaborative participants in the AI ecosystem that help unlock the full potential of your data and fuel AI initiatives.

The new engines of growth

Traditional ways of moving and processing data often struggle to keep pace with AI applications, increasing the need for a more dynamic and integrated approach — moving the AI to the data, rather than the data to the AI. Organizations need databases that can adapt quickly, handle diverse types of data, and, most importantly, help bridge the gap between data, AI technologies, and real-world understanding. 

So, what does an AI-ready database look like in action?

We are seeing new capabilities emerging to enable better data access, allow for more intuitive interaction, and bring intelligence closer to data. 

Gen AI, along with natural language interfaces, are empowering both technical and business users to explore, analyze, and make informed decisions with ease. Many companies want to build agentic AI applications, such as building AI agents to simplify data analysis, to make it easier to access data, understand business context, and deliver valuable insights. Imagine querying your database with a simple phrase like, “Show me all the customers who purchased last month and opened a support ticket,” or “Show me the top sales trends for last quarter,” and receiving an immediate, accurate response.

We’re also seeing a growing trend towards multi-model databases, which store and process multiple data models (formats) at the same time. This development moves databases beyond purely technical schemas to become semantically richer, including capabilities like full-text search for indexing textual data, vector search for semantic similarity, and graph processing for deeper contextual understanding of the relationships within data. 

Graph processing facilitates querying and managing highly interconnected data, which is ideal for use cases like social networks (finding connections between users), fraud detection (identifying suspicious patterns), and recommendation engines (suggesting related products or content). In addition, multi-model databases reduce the need for separate, specialized databases, simplifying data management and allowing organizations to build robust, intelligent applications using their existing operational data.

Vector search, in particular, is increasingly widely adopted for semantic search, and is key for techniques like retrieval augmented generation (RAG) workflows. These allow gen AI models to access reliable systems of record, allowing them to ground responses in fresh, factual, and enterprise operational data. RAG becomes even more powerful as GraphRAG, which combines RAG with knowledge graphs, to  enhance the context and depth of information that models retrieve. While traditional RAG supplies foundational models with document excerpts, GraphRAG incorporates the connections between data points, enabling deeper comprehension and more accurate inferences.

In addition, AI is now being integrated directly into the databases themselves, allowing database queries to make direct calls to foundation model APIs. This enables the incorporation of real world and qualitative context into queries. For example, “Show me all of the budget hotels that are family friendly” is a query in which concepts like “budget” and “family friendly” may not be recorded in the database, but may be a filter or condition that an AI model can match to a given hotel’s characteristics and attributes. AI models can also be used to simplify queries, helping to perform tasks like reranking or filtering of search results. 

In the context of managed database services, AI is helping to optimize fleet management, recommend database configurations, and optimize queries while unlocking more effective and efficient ways to classify, tag, migrate, and integrate data.

Ultimately, data management is on a path of becoming innately smarter — and organizations will increasingly organize, access, and effectively use smarter databases to realize tangible returns on their AI investments.

Unlock your data’s potential with Google AI-centric databases

At Google Cloud, we’re leading the charge in redefining what databases can do in the AI era, empowering businesses to: 

1. Put their trusted data at the heart of enterprise gen AI apps 

The more you use your databases in your AI applications, the more powerful they will be. 

We deliver world-class vector capabilities across our entire database portfolio and capabilities, such as graph processing, enabling developers to develop advanced gen AI applications. In particular, we have already announced many new AI innovations in AlloyDB, including the ability to access data with natural language, make direct API calls to AI models, process foundation models in PostgreSQL queries, support for multiple specialized AI models, and integrate and optimize vector search.

In addition, we offer tight integration with our broader ecosystem of AI tools, including our AI development platform Vertex AI, direct integration between Google Agentspace and AlloyDB, and the open-source MCP Toolbox for Databases, which enables AI agents and other agentic protocols to easily access a variety of databases. These integrations allow you to build accurate, relevant, and contextually-relevant gen AI apps and experiences. 

2. Modernize legacy applications and databases 

There are many good reasons for modernizing databases, including cost efficiency, licensing issues, performance, operational excellence, and feature availability. In addition to all of these historical drivers for database modernization, the new role of databases in the emerging application stack is a major driver in accelerating database modernization. The ability to use your data in AI agents and systems will depend on how AI-enabled your database systems are.

Modernizing applications and databases shouldn’t disrupt your operations — or change the way you want to work. We are committed to being the most open data platform with unmatched choice, flexibility, and portability, so data teams can create modern, data-driven applications wherever their workloads are. 

We not only contribute to open-source database technologies, but we’re also actively working to expand these capabilities with the best of Google. For instance, AlloyDB for PostgreSQL, is PostgreSQL compatible, but compared to standard open-source PostgreSQL it is 4x faster for transactional workloads,  AlloyDB’s ScaNN index offers up to 10x faster filtered vector search queries, and AlloyDB’s column store technology delivers analytical queries that are up to 100x faster. 

Google Cloud databases are a great step towards modernizing your existing applications and cloud-first databases such as Spanner, Firestore and Bigtable, offer capabilities that are designed for cloud infrastructure. They are designed to scale-out and scale-in elastically, be fault tolerant, have zero-downtime maintenance models, and support geo-distributed operation. They are also cost efficient for less demanding workloads and can scale effortlessly to any level of data volume, user concurrency, or query throughput that you might need. Our databases also provide straightforward data sharing with BigQuery, Google Cloud’s fully managed and completely serverless enterprise data warehouse.

3. Supercharge database development and management with AI 

Operational databases are more critical than ever in the AI era, but keeping up with the technology is a constant challenge for database professionals. Ill-fitting tools, complex scripts, and error-prone workflows all stand in the way of ensuring data can flow in and out smoothly, hampering development quality and productivity. 

Gemini in databases provides an AI-powered assistant to help with every aspect of database operations across development, performance optimization, fleet management, governance, and migrations. Teams can now work faster, better, and easier than ever before, giving them more time to focus on what matters most — putting data and AI to work in your organization. 

This next shift in the data landscape is pivotal, and we want to make it easy for every organization to connect all their data to AI. The AI era of data is here, and we’re not just building AI-enabled databases — we’re building the future of data-driven applications.