Why data quality is the cornerstone of agentic AI success

Agentic AI represents the next frontier in artificial intelligence, distinguished by its ability to make autonomous, context-driven decisions without human oversight. Unlike traditional AI, which relies on predefined rules or close monitoring, agentic AI requires advanced levels of adaptability, precision, and integrity. However, it stands on a fragile yet foundational element: data quality. Without high-quality data, agentic AI falters, leading to operational inefficiencies, reputational risks, and unreliable outcomes.

In this article, we’ll explain the critical role of data quality in enabling agentic AI to succeed. We’ll explore what constitutes quality data, the risks posed by deficiencies, how data quality drives agentic AI performance, and what steps you can take to ensure your AI initiatives are built on a solid foundation.

The role of data quality in agentic AI

The success of agentic AI hinges on its ability to interpret, process, and utilise data autonomously. This can only happen if the underlying data meets strict quality standards. Data quality is typically defined by five key dimensions:

• Accuracy: Ensuring that data reflects the true value of the variables it represents.
• Completeness: Avoiding gaps or missing elements that may compromise processes or outcomes.
• Consistency: Maintaining uniformity across sources, formats, and systems to eliminate discrepancies.
• Timeliness: Using the most current data to match the speed of decision-making for agentic AI.
• Relevance: Selecting only the data directly applicable to the AI’s intended task or environment.

Agentic AI is only as effective as the information it uses. When the data fails to align with these dimensions, the AI’s ability to operate autonomously and accurately is compromised. For example, incomplete or outdated datasets can derail adaptive performance, leading to decision-making that lacks context or validity.

The risks of poor data quality in agentic AI

Data deficiencies pose significant risks to agentic AI, ranging from operational inefficiencies to ethical concerns. Here are the core risks, backed by real-world examples:

Biased decision-making

When data lacks diversity or amplifies systemic biases, AI models produce skewed results. This is not a hypothetical concern but a persistent issue. IBM Watson Health’s diagnostics platform faltered because its training data, sourced from several different hospitals, was inconsistent. The use of different terminology, formatting, and recording methods in the data led to unreliable recommendations, putting the Watson Health program’s reputation at risk.

Bias creeps in when training datasets are not representative of the populations they are meant to serve. For instance, if a self-driving car is trained only on urban data, it may fail to operate safely in rural environments. For agentic AI, this problem grows exponentially as it operates in varied and dynamic contexts.

Operational inefficiencies

Poor data quality has a tangible impact on operations, often leading to financial losses. Walmart’s early attempts at leveraging AI for inventory management initially struggled to make accurate inventory predictions due to incomplete and inconsistent historical sales data across stores. This data disparity resulted in overstocking some items while understocking others, costing the company millions in excess inventory and lost sales.

For agentic AI, inefficiencies snowball. A clean but incomplete dataset may prompt the AI to make half-informed calculations, introducing errors and redundancies that compound with scale.

Reputational damage

AI-driven errors, whether minor or catastrophic, erode trust among stakeholders. When AI outcomes are inconsistent or discriminatory, clients, customers, and regulators grow wary. Imagine an agentic AI handling financial lending, denying loans to qualified applicants because of historical biases encoded in the data. Such incidents not only harm trust but also expose organisations to reputational and financial liability.

How data quality drives agentic AI performance

High-quality data transforms agentic AI into a strategic asset, capable of intelligent, reliable performance across scenarios.

When accurate and complete data serve as the foundation, agentic AI can produce predictions and decisions that inspire confidence. For example, an AI system monitoring supply chains becomes vastly more effective when it receives live, comprehensive data on shipping times, inventory levels, and economic conditions. It can make proactive adjustments, such as rerouting shipments in the event of delays.

Agentic AI must adapt to new information in real time. High-quality data, particularly data that is timely and relevant, enables this adaptability. An AI managing urban traffic dynamics, for instance, relies on up-to-date traffic patterns and weather conditions to dynamically reroute vehicles, even as conditions rapidly change.

Organisations deploying agentic AI must also secure trust among users, regulators, and other stakeholders. High-quality data helps achieve this by reducing the likelihood of erroneous or unexplained outputs. Furthermore, diversity in data ensures that the AI understands and considers a wide range of scenarios, minimising instances of bias or exclusion. This is especially important in sectors like finance or healthcare, where decisions carry significant implications.

Finally, bias continues to be a major issue for AI models. Using diverse, well-curated data is a must to ensure your AI system avoids making decisions based on skewed assumptions. For example, a recruitment tool trained on diverse and anonymised datasets can offer fairer applicant assessments, aligning with modern diversity goals. This not only strengthens fairness but also improves business outcomes by widening the talent pool.

Best practices for ensuring data quality in agentic AI

Given these stakes, CIOs and CTOs must prioritise data quality from the outset. Below are actionable strategies to build a robust data foundation for agentic AI systems:

1. Implement data governance frameworks

Establish an organisation-wide framework that defines data ownership, quality standards, and compliance procedures. This ensures that all departments work towards a unified goal of maintaining consistent, clean data.

For instance, appoint data stewards to oversee processes such as standardising data formats and reconciling discrepancies. This approach, as endorsed by Harvard Business Review, equips organisations to eliminate silos that often hinder AI effectiveness.

2. Leverage AI tools for data validation

Advanced AI tools can automate the identification of anomalies, duplicates, or missing entries in large datasets. Automated auditing systems also flag inconsistencies in real time, allowing for rapid correction. This reduces the labour intensity of data preparation, a task that currently consumes 80% of an average data scientist’s time, according to Pragmatic Institute.

3. Foster cross-functional collaboration

Engaging multiple teams in data management ensures consistency across departments, preventing the propagation of conflicting or fragmented data. By aligning business leaders, data scientists, and IT on common standards, organisations can better identify and address gaps.

4. Regularly audit and update data

One of the most overlooked aspects of data quality is its dynamic nature. CIOs must allocate resources to continuously update datasets to match evolving business conditions. For instance, customer preference data may become irrelevant within months in fast-paced markets, stressing the importance of timeliness.

5. Promote a culture of data quality

Beyond technical efforts, fostering a culture where data accuracy is valued across all levels of the organisation ensures a lasting impact. Training employees, from entry-level staff to executives, on the significance of data quality encourages vigilance and adherence to best practices.

This framework of best practices ensures organisations consistently generate, collect, and process high-integrity data, regardless of evolving market demands.