How technology and human expertise come together to improve the quality of corporate data.

In recent years, Artificial Intelligence (AI) has become a game-changer for companies across all sectors. No topic sparks as much curiosity and expectation as its potential to transform processes, generate efficiency, and redefine roles within organizations. However, between enthusiasm and practical application, there is a sensitive point that differentiates sustainable results from fleeting promises: data quality .

Master Data Management is the core of corporate information integrity. It's where the materials, suppliers, customers, and services that feed the entire supply chain, finance, and operations are concentrated. And it is precisely in this ecosystem that AI is causing a silent revolution—one that demands both technological innovation and human discernment.

This article, based on a webinar held by CH | Astrein , explores how Artificial Intelligence in Master Data Management is reshaping the future of data cleansing and what challenges and opportunities accompany this transformation.

What is Artificial Intelligence and why does it matter?

Although it gained popularity with the arrival of generative tools in 2022, AI is not a recent invention. Since Alan Turing's studies in the 1940s, researchers have been seeking to teach machines to reason.

The current leap forward comes from the evolution of Large Language Models (LLMs) — systems capable of understanding natural language and generating contextual responses, such as Chat GPT, Copilot, Gemini, and others.

These models, however, do not "think." They reproduce probabilistic patterns based on vast databases. This means that their accuracy depends entirely on the quality, consistency, and comprehensiveness of the information with which they were trained. And here arises the direct intersection with the universe of master data : the more reliable the source data, the more accurate the result that AI can generate.

In practice, AI has the potential to accelerate analytical processes, reduce manual errors, and support decision-making. However, without reliable data, it becomes merely an amplified reflection of existing flaws. Therefore, in CH | Astrein 's view , artificial intelligence does not replace data management—it depends on it.

Why Master Data Management is the foundation of AI

Master data is the "lifeblood of the company" : it flows through all systems and departments, connecting what the company buys, produces, sells, and controls. Every error, duplicate, or gap in a master data entry can generate chain reactions—from inflated inventories to production stoppages.

CH | Astrein , a pioneer and leader in master data management in Latin America, has already processed more than 40 million cleansed items across more than 250 companies , consolidating a continuously growing data community. This experience shows that data cleansing goes far beyond technical standardization: it is a process that requires methodology, specialists, and reliable content .

AI can contribute significantly to this process—but within well-defined limits. It can process large volumes of information, suggest patterns, translate descriptions, and speed up queries. What it cannot yet do is replace human discernment in validating, interpreting, and contextualizing data.

The role of AI in data cleansing and enrichment.

The application of Artificial Intelligence in Master Data Management occurs at specific stages of the process. In CH | Astrein's practice, the data cleansing workflow involves everything from validating legacy information to standardization and final integration into the client's ERP system. Along this path, AI acts as a co-intelligence , assisting and expanding human capabilities.

Where AI adds value

• Searching and cross-referencing catalogs: AI is capable of comparing thousands of sources in seconds, identifying similarities and suggesting references.

• Translation and standardization: with well-structured prompts, generative models achieve a high level of accuracy in multilingual descriptions.

• Classification and categorization: algorithms can propose initial taxonomies and identify recurring attributes in families of materials.

• Supporting productivity: AI-based automation reduces rework and frees up analysts for more complex tasks.

  
  

Where AI has not yet replaced humans.

• Physical and technical validation: much of this information can only be obtained directly from warehouses, industrial plants, or the manufacturer.

• Contacting suppliers: not all of them provide complete information; in many cases, direct contact is necessary.

• Critical analysis: AI does not understand context. It does not recognize when information "makes sense"—only when it is statistically probable.

  
  

In short, AI increases productivity, but it doesn't eliminate the need for expert supervision . At CH | Astrein, this philosophy is applied through the HIT concept — Human in the Loop — ensuring that every technological insight undergoes human validation before becoming master data.

Challenges and vulnerabilities of Artificial Intelligence

The efficiency of AI is directly proportional to the quality of the data that feeds it. When this data is inconsistent, the risk of "hallucinations" —incorrect answers, but written with apparent confidence—grows exponentially. These errors are not trivial: in an industrial context, a wrong measurement can disrupt an entire production line.

During internal tests conducted by CH | Astrein, 23% of automated responses showed technical errors in classification and enrichment tasks. This number reinforces the need for critical analysis and control. AI, by nature, doesn't know how to say "I don't know." Faced with a lack of data, it invents—and it is at this point that data governance becomes indispensable.

Furthermore, there are practical barriers:

• Access limitations: some industrial information is not available on the internet, such as internal catalogs and technical drawings.

• Outdated data: public databases do not always reflect recent versions of products and standards.

• Biases and inconsistencies: models trained on open datasets may reproduce distortions and generalizations.

  
  

These factors do not invalidate AI—they merely demonstrate that governance is a prerequisite for automation . Technology enhances performance, but trust remains human.

AI as a strategic ally, not a replacement.

When analyzing the impact of AI on registration services, CH | Astrein adopted a pragmatic approach: investigating where the technology enhances and where it does not replace human work. This perspective resulted in important findings.

First, AI does not threaten the data cleansing business—on the contrary, it strengthens it. In a market where the speed of updates is increasing and the demand for accuracy is paramount, AI becomes an essential tool for productivity gains. But the essence of the work remains human: understanding the context, ensuring 100% accuracy, and translating technical complexity into applicable information.

Validation of this thesis comes from the market itself. Salesforce's recent acquisition of Informatica for US$8 billion reinforces that leading AI companies are seeking not algorithms, but reliable data . Without it, machine learning is impossible. Intelligence depends on the foundation.

In other words: AI is the new fire; master data is the fuel. Whoever masters both will master the future of business efficiency.

The data community as a competitive advantage.

The data community created by CH | Astrein is the prime example of how human collaboration enhances the use of AI. Within it, each client contributes to and benefits from a collective repository where standardized descriptions, tax classifications, and technical information are constantly improved.

When an item is remediated—for example, a fuse—it receives a Golden Code CH , with all specifications validated. If another client requests the same item, they already have access to a reliable description, ready for integration. If an improvement is made, all participants automatically receive the update. This is the concept of collective intelligence applied to data governance .

AI, integrated into this ecosystem, amplifies the capacity for search, translation, and suggestion. But it is the human factor—the accumulated knowledge of experts—that guarantees accuracy and traceability . Technology accelerates the valuable community.

The future of Master Data Management with AI

The advancement of AI is inevitable. But the competitive advantage for companies will lie in how they use it. Superficially—merely as a showcase—or as part of a solid governance strategy.

The concept of co-intelligence synthesizes this vision: AI is not an autonomous agent, but rather a digital collaborator that enhances human capabilities. The master data management of the future will be hybrid—uniting intelligent algorithms, mature methodologies, and professionals skilled in critical analysis.

According to the experts at CH | Astrein, the path is clear:

1. Invest in reliable sources.

2. Empowering teams to interpret AI results.

3. Apply automation responsibly and with supervision.

4. Maintain constant updates to standards, norms, and taxonomies.

With these pillars, AI ceases to be a fad and becomes infrastructure — invisible, but essential.

Conclusion: Governing data is governing the future.

Artificial Intelligence in Master Data Management is more than a technological trend; it's a natural evolution of corporate governance. The speed of automation only makes sense when the fundamentals are solid. Reliable data, tested methodologies, and skilled experts form the tripod that supports any innovation.

CH | Astrein continues to invest in solutions that combine technology and human expertise , applying AI responsibly and strategically. Because governing data is, above all, governing the future of business decisions.

Want to know how to apply AI safely and productively to your operation? Contact our team of experts and discover how to transform data into a competitive advantage.

Bonus tip

Start small, but start right. Implement AI in controlled processes where results can be measured and validated. This accelerates organizational learning and reduces the risk of large-scale errors.

FAQ — Frequently Asked Questions

1. Can AI replace human work in master data management? No. It is a support tool. Validation, context, and critical judgment remain human.

2. What is the main contribution of AI in this process? Agility. AI allows scanning databases and catalogs in seconds, reducing search time and enriching information.

3. What are the risks of relying solely on AI? Misinterpretations, "hallucinations," and the use of outdated data. Without human oversight, reliability drops.

4. How does CH | Astrein apply AI in its projects? With the Human in the Loop concept : automation assisted by experts, ensuring quality and precision.

5. What sets CH | Astrein apart in its use of AI for master data? The unique combination of methodology, data community, and technical expertise built over more than 30 years of experience.

CH | Astrein — A pioneer in Master Data Management in Latin America. Leadership that combines experience and innovation to transform data into reliable decisions.