Agentic AI will flop without these 5 data consolidation rules

03. 04. 2025.

Agentic AI will transform industries, but with one important exception — without consolidated data, even the most sophisticated AI systems will fail.

Consolidated data is not just a technical requirement; it is the foundation of an AI agent’s capability to think, learn, and adapt in the best possible manner.

In this text, we’ll explain why poor and broken data are catastrophic for agentic AI and introduce five essential data consolidation rules that every company must adhere to in order to make their AI platforms thrive.

Table of Contents

What is agentic AI?

According to Gartner, agentic AI ranks among the top 10 strategic technology trends for 2025. It’s the newest artificial intelligence advancement that is not only designed to perform pre-programmed tasks but also to:

Make decisions,

Learn from experiences, and

Adjust to changing contexts independently.

By 2028, Gartner predicts that 33% of enterprise software applications will feature agentic AI — an impressive leap from less than 1% in 2024 — enabling autonomous decision-making for 15% of daily workplace tasks.

In contrast to traditional AI systems founded on static code, agentic AI mimics human-like intelligence through the processing of intricate information and making informed choices in real time. Its planning, self-optimization, and ongoing learning capabilities make it ready to revolutionize businesses looking to drive efficiency and data-driven personalization.

These abilities are entirely dependent, however, on the precision, organization, and integration of the data that it processes. With low-quality, unstructured, and fractured data, even the most sophisticated AI agent will be unable to realize its full potential, making data the most significant factor in determining its success.

Let’s explain this in greater detail.

The role of data in agentic AI

Data is the lifeblood of agentic AI — the foundation upon which its performance and reliability are built. Everything an AI agent does — adaptation, prediction, decisions — derives from the data it processes.

Agentic AI can only function properly if the data it processes is accurate, complete, and consistent.

Here’s why:

Accurate information guarantees that facts are used to make decisions, rather than faulty data.

Full information presents a holistic overview that enables agentic AI to make balanced decisions.

Properly organized information also avoids wastage and makes sure that the AI receives appropriate information at the appropriate time.

Without these features, even the most advanced agentic AI won’t be in a position to provide useful and accurate results.

Challenges of poor data quality

Poor data quality is one of the greatest threats to the success of agentic AI. It has severe implications such as

Decision errors,

Inefficiency, and

Missed opportunities.

If the data is inaccurate or incomplete, AI agents will be forced to make decisions based on that data, which may generate faulty recommendations, biased predictions, and unhappy customers.

For instance, an AI-driven e-commerce website premised on outdated or fragmented customer data will recommend irrelevant products, repelling customers rather than boosting sales.

Even in logistics, fragmented information can create inefficient route planning, which translates to increased delivery times and operational expenses. The two instances underscore the absolute necessity of information being current, consistent, and integrated in order to be in a position to harness the entire potential of Agentic AI.

Why data consolidation matters for agentic AI

Data consolidation is a key to effective agentic AI, addressing one of the oldest data management challenges: data silos.

Fragmented data — spread across different systems or stored in incompatible formats — creates inefficiencies, slows down decision-making, and increases the likelihood of errors.

For example, when customer data is spread across marketing, sales, and support systems, AI agents will not be able to form an integrated view of their needs, which leads to poor recommendations or missed opportunities.

Conversely, agentic AI can access and process unified, integrated data. At the same time, it will leverage unstructured data to enhance decision-making and adaptability. This single system can enhance:

Decision-making with a holistic view,

Accuracy by eliminating conflicting inputs, and

Efficiency by avoiding duplicated efforts.

Data consolidation or unification, in short, converts raw, disparate data into an empowering resource, allowing AI agents to function at their optimum.

The 5 essential data consolidation rules for agentic AI

To unlock the full potential of agentic AI, efficient data consolidation must become a top concern for companies. However, not all consolidations are alike. Read on as we explain 5 core data consolidation rules for a more effective use of agentic AI.

Rule 1: Consistency across data sources

For agentic AI to operate effectively, information coming from diverse sources must be compatible and consistent. AI agents would not be expected to interpret data accurately if datasets are in conflict — due to differences in format, classification, or age.

For instance, consider an e-commerce website where sales personnel record product categories differently than the marketing department. If the AI agent is trained on different categories of products, it may offer customers irrelevant suggestions, resulting in lost sales and poor customer experience.

Uniformity across all data sources prevents such discrepancies, giving the AI company a basis for making informed decisions.

Rule 2: Timeliness and regular updates

Information is worthless unless it’s current. Regular refreshing of data sets helps agentic AI run on accurate, relevant, and current data. Outdated data leads to poor decisions and missed opportunities, and this undermines the effectiveness of AI systems.

For instance, consider the scenario of an AI-based retail business that sends personalized offers. If customer preference data has not been refreshed in a couple of months, AI could end up recommending winter wear to a customer who has already moved to a warmer area. Not only is this uninteresting to the customer but can also create a poor brand impression. Fresh and updated data enables AI agents to generate relevant, context-based outputs.

Rule 3: Data integrity and accuracy

There has to be high-level data integrity in order for agentic AI to generate strong and trustworthy decisions. Data inaccuracies or errors — regardless of whether they result from human input error, system breakdown, or stale data — can mislead AI agents and lead to inferior results.

For example, let’s take a look at one banking example — the case of a bank using agentic AI to screen loan candidates. When income details of a client are input or copied improperly, the AI may approve a loan to an ineligible client or reject a suitable applicant. While leading to faulty decisions, such errors also compromise customer trust and business outcomes. Ensuring data integrity through rigorous validation and error-checking procedures allows AI agents to operate reliably and precisely.

Rule 4: Scalability of data systems

When businesses expand, so do the dataset volumes they create. Scalability is necessary in data systems for agentic AI to be able to deal with growing datasets efficiently without a decline in performance. Scalable systems can scale to meet increasing demands, process and consume large datasets easily.

For instance, an online webshop with heightened Black Friday sales may have much more purchasing and customer activity than normal. If the data system is not scalable, then the AI will not be able to keep up with the demand for more data, potentially resulting in lagging or failing recommendations. Scalable systems guarantee that regardless of the volume of the data, the AI will operate at its best capacity, generating recommendations on time and correctly.

Rule 5: Robust data governance

Strong data governance is imperative for security, accuracy, and compliance of information that is leveraged in agentic AI. With sound processes and policies, companies can preserve data integrity and protect sensitive information and comply with legislation simultaneously.

For instance, in industries such as telco and healthcare, in which AI agents handle vast amounts of sensitive customer data — ranging from call records and billing details to confidential patient information — robust governance is imperative. In the absence of strong access controls and compliance, sensitive information can become compromised, leading to breaches of privacy, hefty fines, and a loss of trust.

Data governance not only keeps information secure but also ensures ethical and responsible use of AI, and with it, instills confidence in customers and stakeholders.

From unconsolidated data to high-performing agentic AI: How Things Solver can help

Agentic AI is only as good as its source information. Without proper data consolidation, even the most advanced AI agents will stumble with inaccuracies, inefficiencies, and poor decision-making.

By following the five most important rules of data consolidation we’ve outlined in this blog post, you can extract the full value of the latest AI technology.

Thanks to Things Solver’s cutting-edge approach and AI technology, your information will consistently preserve its integrity, freshness, accuracy, scalability, and security, providing a sound platform for AI-powered decision-making.

Smooth integration – Unify disparate sources of information into one, organized platform

Real-time analysis – Keep AI agents working with current, most relevant information

Scalable architecture – Handle growing volumes of information with no performance degradation

Secure governance – Protect sensitive information and maintain compliance

The lesson is simple: without high-quality, consolidated data, agentic AI will flop. But with proper tools, it can make smarter decisions and help your business perform much better.

Ready to start consolidating your data and try Get started transforming your AI model?

Reach out to us at ai@thingsolver.com or book a free demo to get an evaluation of your current information processes and see how Things Solver can enable a sounder AI base for your business operations.