The Smarter the Data Management, the Smarter the AI Agent

Authors: Edward Crabb , Arife Hussain

AI agents are revolutionising the way we work— from scheduling medical appointments to approving credit cards. These intelligent systems learn, adapt, and operate autonomously, unlocking new levels of efficiency. But there’s a crucial factor that determines their success: data. If the importance of data is neglected, AI agents can make flawed decisions, perform actions incorrectly, or even break down completely.

So, how do we ensure they are efficient but also make the right decisions? It all starts with data management.

What are AI Agents?

An AI agent is an intelligent system that acts in the world with little to no human guidance. Once goals are defined by people, the AI agent independently decides and performs the most effective steps to reach them based on data gathered from its environment.

Here’s an example of how an AI agent goes beyond traditional data & AI systems:

Once a goal has been set, an AI agent independently achieves it by not only analysing information to make decisions but also by acting on them. In our example, the agent doesn’t just recommend options – it makes decisions based on preferences and autonomously books the trip. The ability to act independently is what sets AI agents apart, making them truly autonomous problem-solvers.

The Workforce of the Future

With the cost-savings and productivity increases coming from fully automating many human tasks, it’s no surprise AI agents are a hot topic, with 82% of organisations intending to integrate them within 1–3 years.

If the rapid rise of traditional AI adoption is anything to go off, it won’t be long before AI agents are a key part of the workforce of the future where working patterns are dramatically different from how they are today:

“Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons” Bill Gates

What’s stopping agents being embedded into our working lives? While new challenges in model development and computing power persist, an enduring issue remains as critical as ever: AI agents need the right data to work properly.

Doubling Down on the Importance of Data

Not only do agents need data to be trained prior to deployment like all AI systems, they also continue to learn from real-time data after deployment via workflows, continuously adapting to perform tasks. This double dependency is true for all AI agent use cases:

Whatever the agent is doing, it depends on both types of data to be successful in achieving its goal. If the data has problems, so does the agent.

Data Dilemmas: The Hidden Challenges AI Agents Face

AI agents depend on high-quality, well-organised, and accessible data. But that’s not always what they get. Here are three major challenges they face:

1. Data Quality – Rubbish In, Rubbish Out

• AI learns by analysing massive datasets. But what if that data is incomplete, outdated, or full of errors? If AI agents are trained on bad data, they’ll make bad decisions—like recommending incorrect treatments or making poor financial predictions.
• In real-time operations, the problem gets worse. Unlike training data, real-time data can’t always be cleaned before use. If an AI agent receives poor quality data, it may act on a faulty view of reality—leading to costly mistakes.

2. Siloed Data – When AI Only Sees Part of the Picture

• AI agents are not one-trick ponies, they have to handle multiple different tasks in their workflows. This means they must learn using data from different parts of a business. If this data is unavailable or disconnected (exists in silo’s), the agent will not be able to learn how to handle all the required situations.
• AI agents use real-time data to perform actions which could affect people, processes or products. If this data is disconnected, the agent may perform the action on an incorrect subject.

3. AI Explainability – The ‘Black Box’ Problem

• AI decisions can be difficult to explain, especially when trained on huge, unstructured datasets without clear documentation. This makes it hard to track why AI agents make certain choices—a big issue for compliance and trust.
• Decisions based on real-time data are even more challenging to explain. If an AI agent made a decision based on data which has changed since the decision was made, it can be very difficult for the business to justify it.

From Data Mess to AI Success: The Power of Data Management

The good news? These challenges can be solved with data management strategies, such as:

1. Data Quality Initiatives – The Foundation of Smarter AI

• Cleaning, validating, and updating data before AI agents use it prevents bad decisions. High-quality training data improves learning, while real-time DQ monitoring ensures agents adapt based on accurate and up-to-date information.
• Example: With high-quality up-to-date data, an AI logistics agents can see blocked roads and traffic data in real-time ensuring drivers take the fastest and most efficient routes.

2. Master Data Management (MDM) – Breaking Down Data Silos

• MDM creates a single source of truth, integrating data across all departments. This ensures AI agents have access to all the information they need, preventing misalignment and costly errors.
• Example: With MDM, an AI credit agent can pull both financial and risk data, ensuring approvals go to the right people.

3. Data Transparency & Traceability – Making AI Decisions Explainable

• Strong data governance (especially data lineage tracking) helps businesses trace AI decisions back to their data sources. This allows for better accountability, easier audits, and stronger regulatory compliance.
• Example: An AI nurse Agent can use data lineage to track the history and origin of patient data, allowing it to justify why it scheduled an appointment.

The Bottom Line: Data Management is Key to AI Agent Enablement

AI agents are set to transform industries — but only organisations that prioritise data management will be part of that transformation. Invest in making your data clean, connected, and transparent, and you will lay a foundation for AI that shapes the future.

As AI continues to evolve, one thing is clear: Better data management means better AI—and better AI means better business.