The key to preventing AI from inventing data.
In recent months, it seems that Artificial Intelligence is all anyone talks about. However, many organizations encounter an invisible wall: their AI models are brilliant at writing general texts, but they fail when asked about real inventory, a specific customer’s history, or the company’s internal processes.
The key for AI to provide real value and not just “invent” data (what we technically call hallucinations) is to connect it securely with your operational data. In this article, we will explore how you can unlock the full potential of Gen AI by using your databases as the main engine.
Why does Gen AI need your operational data?
The success of any company today depends on how it uses its information to extract business value. You already use operational data—such as inventory levels, financial transactions, or customer profiles—to improve your processes. The arrival of Gen AI has elevated this potential exponentially.
Imagine a customer asks a chatbot about a product’s availability. If the AI only has access to general knowledge, it will give a generic answer. But if it is integrated with your operational database, it can offer a personalized and accurate response in real-time. In fact, 86% of organizations acknowledge that offering contextual user experiences through AI-integrated databases has a substantial positive impact.
The end of legacy databases
One of the biggest obstacles to digital transformation is the persistence of old or “legacy” databases. These structures are often rigid, slow, and not prepared for the demands of modern AI. Only 14% of organizations feel satisfied with the support their current databases provide for AI.
Modernization is urgent because the most popular tools for working with AI models and vectors work optimally in the cloud. Modernizing your databases is not just a technical change; it is enabling the ability to offer better experiences, increase productivity, and improve information availability.
RAG: The bridge between models and your business
For AI to understand your company’s context, we use a technique called RAG (Retrieval-Augmented Generation).
How does it work? Instead of relying solely on what the AI model “learned” during its training (which is usually general information), the RAG system first searches your operational database for the fresh and specific information it needs to answer a question.
A practical example: The shopping assistant
Let’s think of a toy store that uses a standard AI model. The chatbot can answer basic questions about return policies. However, with RAG, that same chatbot can access real-time inventory and tell the customer: “That toy for children under five is available at the store closest to your location.” This type of contextual response is what really closes a sale.
The power of Google Cloud for data AI
Google Cloud offers a complete ecosystem of databases designed for the AI era, eliminating the need to have specialized databases just for vectors. Some of the key tools include:
- AlloyDB AI: Optimized for applications that need fast and accurate responses, allowing you to generate and search vector embeddings up to 10 times faster than standard PostgreSQL.
- Spanner: A cloud-native database with practically unlimited scale and 99.999% availability, capable of processing billions of queries per second.
- BigQuery: Your AI-ready analytics platform, designed to maximize data value in multicloud environments.
- Cloud SQL: A fully managed service for popular engines like MySQL and PostgreSQL, which now includes built-in support for vector search.
AI for experts: Empowering technical teams
AI not only helps your customers but also the people who maintain your systems. Database management is complex and often error-prone work.
Thanks to tools like Gemini Cloud Assist, developers and administrators can use natural language to build applications, optimize database fleet performance, and accelerate resident code migrations. In addition, tools like Database Center allow proactive management of security and regulatory compliance (such as ISO-27001 or PCI-DSS) through intelligent AI-driven dashboards.
How to start your transformation journey?
At Luce IT and Google Cloud, we recommend four initial steps to start without friction:
- Explore the possibilities: See what your competition is doing and get inspired by successful use cases.
- Align your team: Get decision-makers on the same page and lean on assistive technologies to ease the workload.
- Start small: Identify simple scenarios, such as cleaning up support queues or detecting duplicate tickets.
- Seek continuous improvement: Automate repetitive tasks and personalize the customer experience step by step.
Generative AI-driven transformation is not a destination, but a path of constant innovation. With the right data and the right cloud tools, your organization will not only be ready for the future, it will be leading it.
At Luce IT, we help you maximize the value of your generative Artificial Intelligence applications by integrating them directly with your operational databases through our experience with the Data Platform and LIA (Luce Intelligent Assistant). To dive deeper into this strategy and discover how to prevent your AI from “inventing” data, we invite you to download the full Google Cloud report on our dedicated landing page:
👉 Download eBook: Accelerating generative AI-driven transformation with databases
Frequently Asked Questions about Operational Data
What is RAG and why is it important for my company?
RAG (Retrieval-Augmented Generation) is a technique that connects AI models with reliable external data sources, such as your operational databases. It is vital because it allows AI to offer responses based on real and updated facts about your business, reducing errors and providing specific context.
Why are old databases a problem for AI?
Legacy databases often lack the speed and compatibility needed to handle vector searches and large volumes of data in real-time. This limits the AI’s ability to offer instant and accurate responses, holding back digital transformation.
How does Google Cloud AI help database administrators?
Tools like Gemini Cloud Assist allow administrators to use natural language to generate SQL code, diagnose performance issues, and receive intelligent recommendations to optimize the security and compliance of the entire data infrastructure.
What are the benefits of integrating generative AI with real-time inventory?
It allows for the creation of much more effective shopping assistants that not only recommend products but also provide exact availability, current prices, and pickup locations, significantly improving the customer experience and conversion rate.
