One of the biggest obstacles is the lack of trust in the data used to power AI models: only 12% of organizations now believe they have data of sufficient quality and accessibility to effectively support their AI initiatives.
According to a recent report from Bain, the market for artificial intelligence (AI) products and services is expected to reach between $780 billion and $990 billion by 2027, with annual growth of between 40% and 55% over the next three years. However, not all companies manage to make these investments a reality. One of the biggest obstacles is the lack of trust in the data used to power AI models: only 12% of organizations now believe they have data of sufficient quality and accessibility to effectively support their AI initiatives.
With the meteoric rise of generative AI (GenAI) over the last two years, companies are realizing the need to fundamentally rethink their data strategies in order to derive real value from their investments. As the market moves from predictive models to agentic AI systems, capable of reasoning, planning and acting autonomously to achieve a goal, data becomes the very environment in which these agents operate. According to our latest Data Integrity Trends and Insights report, 60% of companies now cite AI as a driving factor in their data programs, up from just 46% in 2023.
Data integrity, the foundation of AI performance
Data integrity is an essential prerequisite for the effective use of AI. Reliable, consistent and contextualized data helps power effective AI initiatives, produce relevant analyzes and generate actionable results. To achieve this, organizations must integrate critical data sets across the enterprise, establish robust governance and quality processes, and enrich their internal data with third-party sources to maximize contextual value.
Break down data silos
To fully leverage AI, businesses must have accessible and reliable data, including within complex legacy systems. Without it, AI projects risk being riddled with bias, errors, and other issues that undermine their impact on the business. For example, relying on data limited to a single geographic area or a narrow demographic segment can skew insights and create bias in analytics.
A robust data integration strategy helps consolidate heterogeneous sources into actionable formats, ensuring complete, accurate and consistent data for analytical uses and AI models. By defining clear goals and assessing their existing data landscape, businesses can identify critical sources, assess data quality, and make it easily accessible to analytics and AI tools. The objective is clear: eliminate silos, improve data quality and produce reliable, bias-free insights.
For example, agentic AI relies on continuous feedback loops and dynamic exploration of multiple data domains. Fresh, interoperable and easily accessible data are essential to allow agents to observe their environment and learn from it.
Organizations that prioritize data integration will gain an edge over their competitors by quickly adding new data sources and targets to improve AI outcomes. By developing AI models from integrated, recent and varied data sources, they provide their teams with a complete and reliable view of business data, which is essential for effective AI implementation.
Implement robust data governance
According to our research, a lack of data governance is the biggest barrier to AI initiatives for 62% of organizations. This situation is explained by the central role of governance in the management of data uses: location, traceability, access rights, presence of personal data (PII), etc. All critical elements to ensure truly AI-ready data.
Thus, strong governance builds trust in the organization’s data, ensuring that AI models have the necessary information and that it is used ethically and responsibly. In this framework, data governance naturally becomes the foundation of AI governance.
Improve data quality
The effectiveness of AI directly depends on the quality of the data used to train and power its models. Accurate, consistent and complete data allows AI to identify patterns, make predictions and produce relevant insights. Conversely, poor quality data can lead to bias, hallucinations, or unreliable results.
Successful AI applications rely on continuous assessment of data quality. To act effectively, AI must rely on accurate and constantly monitored data. This is where data observability comes in, which becomes a fundamental pillar. Companies must favor automated monitoring, alerting and diagnostic mechanisms in order to detect anomalies, pattern deviations or volume variations, and quickly trace the source of problems or trigger corrective actions. Data quality should no longer be a one-time check, but a dynamic, ongoing capability.
Without rigorous quality management processes, AI initiatives, and especially autonomous agents, risk relying on incomplete, outdated, or faulty data, leading to inaccurate and potentially costly strategic decisions.
Leverage third-party data to enrich context
Complete and reliable data is essential to produce quality or trusted AI results. However, without context, models remain susceptible to bias and may lack the nuance needed to provide reliable results. Data enrichment consists of supplementing the company’s internal data with carefully compiled third-party datasets: geographic data, demographic data, environmental risk factors, etc. This helps increase the diversity of data used to train AI models, highlighting hidden trends that might otherwise go unnoticed, and significantly improves the reliability of the results obtained by the AI.
As organizations adopt generative AI, those that build on robust data foundations (integration, quality, observability, governance and enrichment) will gain a decisive lead. By ensuring that AI systems rely on reliable, contextualized and relevant data, they give themselves the means to attract new customers, accelerate their time to market and reduce the risks of non-compliance. In a future marked by autonomous decision-making, having reliable data will constitute a competitive advantage and a growth lever.
