Are AI Agents the Future of Data Intelligence?

The way we understand and use data is on the brink of transformation. In fact, AI agents are poised to redefine data – and data intelligence – as we know it. 

Data is a critical asset, yet many struggle to harness its full value effectively. Data intelligence practices offer a fast way to overcome many hurdles, especially as data intelligence efforts are accelerated and automated with AI-powered solutions such as AI agents. AI advancements are arriving at a breakneck pace. AI agents are one of them, but they have also become an area of immense investment and interest due to their potential value—in data intelligence and elsewhere.

This post explores these innovative concepts, explains how AI and automation work together to improve data intelligence, and shows how effective data intelligence empowers organizations with improved decision-making, efficiency, and business impact.

Key takeaways

• AI agents are more than automation—they combine intelligence with execution. While automation follows predefined rules, AI agents use reasoning and adaptability to complete complex data tasks, reducing human effort, lowering costs, and improving productivity.

• Data intelligence unlocks data value by capturing crucial information about data to enable faster data discovery, better data quality, improved data governance, and more accurate data-driven decision-making.

• AI agents supercharge data intelligence by automating data curation, metadata enrichment, and other data governance tasks, making data more accessible and generating greater value from data assets.

This is the age of data. If an organization isn’t fully leveraging its available data effectively, it will fall behind. 

Most organizations know this. IDC found that 90% of corporations say data is a critical enterprise asset. Why? The research firm also found that organizations that use data effectively “achieve 2.5x better business outcomes across the board.”

However, as the world continues to generate more and more data, organizations are challenged to find, manage, and utilize it effectively. Luckily, new technologies and approaches are here to help. The explosion in AI-powered solutions has ushered in innovative ways to improve data-driven decision-making and data cultures. 

Two concepts are gaining increased traction in helping organizations put data to work: AI agents and data intelligence. Combined with modern frameworks for managing and understanding data, realizing the full potential of organizational data just became much more manageable.

What are AI agents?

The evolution of AI in business has followed a clear trajectory—from process optimization to workflow automation to AI-driven agents—and now, even agents managing other agents. Each stage has progressively reduced human intervention, increased efficiency, and expanded the scope of what machines can accomplish.

Artificial intelligence has been shaping industries for decades. The term AI was first coined in 1956 at a Dartmouth College conference on “thinking machines.” Over the following decades, AI systems became better at processing structured data and automating predefined workflows—think of early rule-based systems in finance and manufacturing. The next major leap came with machine learning, enabling computers to detect patterns, make predictions, and enhance decision-making.

In just the past few years, generative AI has accelerated AI’s role in business, pulling concepts from science fiction into daily workflows. Tasks like writing a blog post, interacting with customers in real time, and summarizing vast datasets are now handled by AI much faster than humans could manage. This shift has laid the foundation for AI agents—systems that combine generative AI and automation to complete tasks with minimal human intervention.

Unlike simple automation, which follows predefined rules, AI agents can reason, take action, and adapt to changing conditions. They can operate autonomously to manage repetitive processes or be triggered by a user or system prompt to take on a specific task. What makes AI agents valuable is their goal-oriented nature, adaptability, and reusability.

For example, a generative AI model designed for IT support might simply retrieve relevant help articles and generate a response based on past cases. But an AI agent built for IT support would go further—it could walk users through troubleshooting steps, reset passwords, log users out of active sessions, and create a support ticket to document the issue.

As AI continues to evolve, we're now seeing the rise of meta-agents—AI agents that manage other AI agents, orchestrating complex, multi-step workflows. This next stage in AI’s evolution is redefining business processes and expanding the possibilities of automation and intelligence.

Benefits of using AI agents

AI agents aren’t just automation tools; they continuously adapt and improve, learning from interactions within predefined constraints. This ability allows them to manage complex tasks more effectively over time while freeing up human workers to focus on higher-value activities that require ingenuity, strategic thinking, and nuanced decision-making.

But AI agents do more than just give humans more free time—they actively enhance business operations by optimizing processes, improving efficiency, and enabling smarter decision-making.

Here are some key benefits of AI agents:

• Enhanced business results: AI agents save time and money by increasing productivity, improving revenue with faster, better customer experiences, and providing quick access to business insights for more organizational agility.

• Reduced costs: AI agents take on tasks that human workers no longer need to manage, reducing labor costs, improving consistency and accuracy, and reducing rework and poor decision-making.

• Around-the-clock availability: AI agents work automatically and autonomously without human intervention or breaks. 

• Scalability: AI agents can scale and do more as a business grows and without impacting the quality of the agent’s work.

• Increased productivity: AI agents can take tasks off a human worker’s plate, letting those workers accomplish more tasks or goals that drive value.

Examples of AI agent use cases

A few examples of AI agent use cases are mentioned above, but the applications for these autonomous helpers are virtually limitless. 

Here are some examples of how AI agents are being applied to various use cases:

• Healthcare organizations use AI agents to analyze medical images and offer suggestions to physicians, saving time and reducing the likelihood of a missed diagnosis. 

• Human resources teams use AI agents to assist employees with onboarding, answering questions about benefits and company policies, and managing initial training tasks.

• Media streaming organizations use AI agents to capture user behaviors and use that intelligence to generate personalized content suggestions in real time.

• Students and workers use AI agents to record and transcribe spoken meetings or lectures, summarize the content, and offer additional resources on key topics.

• Manufacturing organizations use AI agents to continuously monitor equipment attributes, predict service intervals, and generate a field service request ticket if an anomaly is detected.

What is data intelligence?

Data intelligence is the information about an organization’s data, along with the tools and processes used to gather that information. It’s data about data and how it’s used, analysis of that data, and meaningful insights into how data can be better utilized to achieve organizational goals.

Data intelligence answers questions like who is using data, where the data resides, if the data should be retained, how the data was processed, and more. It’s used to help workers find data they need, support data governance efforts, manage data privacy, risk, and compliance, and more. 

Data intelligence is sometimes confused with business intelligence and data analytics. However, just as business intelligence is information about the business, data intelligence is information about data. 

Data intelligence is also distinct from data management, where the latter orchestrates data from creation through utilization and eventual remediation. Data management focuses on how data is stored, collected, combined, and accessed.

Benefits of data intelligence

Data intelligence enables organizations to understand, govern, and optimize their data, ensuring it supports business objectives while maintaining compliance, security, and quality. By providing deep visibility into data assets, data intelligence reduces risk and maximizes the value of data-driven decision-making.

Additional benefits of data intelligence include the following:

• Access the right data at the right time: Instead of simply speeding up searches, data intelligence ensures workers find the most relevant, high-quality data when they need it. By cataloging, governing, and enriching metadata, it eliminates guesswork and inefficiencies in data discovery. 

• Increase analytics accuracy: Data intelligence provides insights on data so workers can find the right data for their needs or understand who to consult with questions, thus increasing the accuracy of data-driven analytics.

• Improve data quality: Data intelligence can automatically flag inconsistencies, incomplete, or outdated data so workers have full confidence in the data being used and can take action to improve data quality.

• Enhance data governance: Data intelligence tools provide a repository to store data governance rules and policies, provide a platform for data governance workflows, and create guardrails for workers using data.

How AI agents improve data intelligence

AI agents without data intelligence are doomed to fail. Without a foundation of trusted, well-governed, and contextualized data, AI agents risk amplifying errors, reinforcing bias, and making decisions based on incomplete or inaccurate information. To be effective, AI agents must be built on a strong data intelligence framework that ensures they are leveraging relevant, high-quality, and well-documented data.

Organizations are already drowning in data, making data intelligence, governance, and management more challenging as volumes continue to grow. AI is essential for addressing this complexity, offering capabilities like intelligent data curation, automated discovery, and AI-assisted governance.

For example, AI can monitor data usage patterns and suggest stewardship assignments based on how teams interact with specific datasets. It can also autogenerate missing descriptions for data assets, ensuring that workers can find and understand the right data more easily. These AI-powered capabilities are already common in modern data catalogs, making them a critical foundation for AI agents.

AI agents take this a step further by adding automation, adaptability, and autonomy to data intelligence efforts. They enhance how organizations govern and manage data by handling tasks such as continuous data curation, metadata enrichment, and workflow orchestration.

Consider an AI agent that monitors cloud and on-premises data repositories, continuously identifying new datasets. Instead of requiring manual intervention, the AI agent can:

• Autonomously capture metadata and update the data catalog.

• Work in coordination with other AI agents to orchestrate governance workflows.

• Assign a data steward to ensure the dataset is properly managed.

• Generate missing metadata to improve discoverability and usability.

• Alert the governance team and summarize the dataset’s details and the actions taken.

By embedding AI agents within a data intelligence ecosystem, organizations can ensure that AI-driven automation is not just fast, but also accurate, trustworthy, and aligned with business objectives.

The future of AI agents and data intelligence

AI advancements are arriving at a breakneck pace, and AI agents are at the forefront of this transformation. The surge in AI-driven automation has led to a new gold rush, where tooling for AI agents is rapidly evolving—expanding far beyond traditional no-code solutions. Developers now have access to bot builders, observability tools, and agent quality tools, enabling them to create increasingly sophisticated AI agents. The question isn’t whether AI agents will redefine how businesses operate—it’s how quickly organizations can adapt to harness their potential.

The impact of AI agents is particularly profound in data intelligence. AI agents and data intelligence are better together—agents depend on high-quality, well-governed metadata to function effectively, while AI-powered automation accelerates data intelligence efforts. Without data intelligence, AI agents risk failure due to poor data quality, governance issues, and a lack of contextual understanding.

Alation is at the forefront of this revolution. This is an opportunity for the ecosystem: how many Alation-powered AI agent apps can global organizations build to revolutionize data management, so data moves at the speed of business?

Curious to learn how Alation can help you build successful AI agents? Book a demo to learn more.