At Quantion, we have developed a Data Agent for a maritime logistics operator that can turn business questions into queries across complex data structures, interpret the results, and generate recommendations aligned with the organization’s objectives. The solution addresses a common challenge for companies managing large volumes of information: turning available data into actionable insights that support better decision-making.

This project illustrates one of the five key opportunities we identified in our article Artificial Intelligence for Business: 5 Areas Where AI Can Deliver Real Impact. In that article, we explore how AI can transform decision-making, operations, customer experience, knowledge management, and software development.

Here, we take a closer look at the first of these areas through a real-world AI application developed by Quantion.

The use case: connecting business questions with diverse data

In industries such as maritime logistics, information is often spread across multiple systems, databases, and operational processes. Accessing that information is not always straightforward. Answering a business question may require understanding how data is structured, identifying the right sources, and building queries that connect different data points.

This complexity can create a gap between the professionals who need the information and the systems where it is stored.To address this challenge, Quantion has developed a Data Agent, an artificial intelligence agent capable of retrieving and interpreting all kinds of data structures to answer business questions.

The solution brings together three core capabilities:

  • Generating database queries from natural language questions. The agent interprets a business question and generates the queries needed to retrieve the relevant information.
  • Semantic analysis of results. Once the data has been retrieved, the agent analyzes its meaning to provide an interpretation that takes the business context into account.
  • Generating recommendations. Based on its analysis, the agent suggests potential actions aligned with the organization’s objectives.

Together, these capabilities take AI beyond simple information retrieval and enable it to support the interpretation of business data.

From data access to actionable recommendations

One of the most important aspects of this project is the scope of the agent’s capabilities. Many analytics tools allow organizations to track KPIs, visualize trends, and generate reports. These functions remain essential, but they do not always bridge the entire gap between asking a question and making a decision. The Data Agent developed by Quantion adds another layer of interpretation.

Understanding the business question

The starting point is a question expressed in business terms, rather than in the technical language of a database. The agent needs to connect that question to the appropriate data sources and structures. This capability draws on an approach known as Text-to-SQL, which translates natural-language questions into database queries. However, the challenge goes beyond generating a technically valid query. The agent must also correctly interpret business concepts and the relationships between different data elements.

Interpreting results in context

Retrieving information is only part of the process. The agent also performs semantic analysis of the results. This helps explain what the retrieved information means in relation to the original business question. This distinction matters. A data point can be accurate without necessarily being useful for making a decision.

Context is what turns a technical answer into a meaningful business insight.

Generating recommendations aligned with business objectives

The third capability is generating actionable recommendations. The agent uses the results of its analysis to suggest potential actions that take the organization’s objectives into account. As a result, the system does more than present information. It can also support the evaluation of possible next steps.

This does not mean automatically delegating decisions to AI. Recommendations should be reviewed and assessed by professionals with the relevant business expertise. The value lies in enabling better-informed decisions, not replacing human judgment.

How is a Data Agent different from a Business Intelligence tool?

Business Intelligence (BI) tools have helped organizations analyze information and monitor performance indicators for years. Data Agents do not necessarily replace these tools. Instead, they can complement existing BI capabilities by introducing a new way to access and interact with data. The main difference lies in the user experience and the types of analysis they can support. While many traditional solutions rely on predefined reports and dashboards, a Data Agent can interpret natural-language questions and generate queries tailored to specific information needs.

When combined with semantic analysis and recommendation capabilities, it can also help connect the results to the broader business context. This evolution is already taking shape in the market. Solutions such as Microsoft Fabric Data Agents allow users to query enterprise data sources using natural language. However, their effectiveness depends on data quality and the correct interpretation of business questions. An AI-generated query does not, by itself, guarantee a reliable answer.

What should businesses consider before implementing Data Agents?

The maritime logistics use case highlights an opportunity that extends well beyond a single industry. Many organizations hold valuable information but struggle to make it easily accessible to the people responsible for making decisions. Data Agents can help bridge this gap. However, successful enterprise implementation requires attention to several key factors.

  • Reliable data foundations. The agent needs access to high-quality, well-organized information. AI cannot automatically fix underlying data quality issues.
  • Business knowledge and context. Organizations need clear definitions of their metrics, an understanding of how they relate to one another, and rules for interpreting them correctly.
  • Security and control. Data access must comply with the organization’s permissions and security policies. It is also important to be able to review queries and validate results.
  • Human oversight of recommendations. When AI suggests actions, organizations need to establish how those recommendations are evaluated and who has final decision-making authority.

These elements are essential for moving beyond a technology demonstration and building a solution that can be used reliably in a business environment.

The value of Data Agents: bringing data closer to decision-makers

The project developed by Quantion for a maritime logistics operator is a practical example of Artificial Intelligence for business. Its value goes beyond translating questions into database queries, but in the ability to cover the entire lifecycle from data to business insight.  This approach opens up new opportunities for organizations looking to make better use of their data.

As we explore in From Data to Decisions: How AI Enables Data-Driven Organizations, a strong data platform is the starting point. The next step is making that information easier to turn into knowledge that supports the business. Data Agents can help bridge two worlds: the heterogeneous reality of enterprise data and the needs of the people making business decisions. That is where Artificial Intelligence begins to deliver real business value.