According to McKinsey’s State of AI survey, cited in the Stanford University AI Index Report 2025, 71% of organizations now use generative AI in at least one business function — a number that has more than doubled over the last 12 months.
However, the fact remains that AI is only as good as the data that fuels it. Without accurate, consistent, and contextual data, even the most advanced AI models will produce unreliable outcomes. But while companies know the importance of good data, many continue to struggle with it — only 12% of organisations report that their data is of sufficient quality and accessibility for effective AI implementation.
Data integrity cannot be treated as an afterthought. Making it a strategic imperative for any AI-focused initiative will allow organisations to create a more resilient foundation that can weather virtually any future technological shakeup. However, to achieve this, organisations must address the following key data challenges.
Unifying Critical Data Across Diverse Systems
Large organisations usually rely on several, and often disjointed, environments to host critical data relating to customers, prospects, vendors, inventory, employees and more. In industries like financial services, mainframes remain a cornerstone for storing sensitive data due to their security and dependability. However, integrating this complex data into modern cloud-based AI workflows can be a challenging task.
To drive trustworthy, accurate AI outputs, organisations must prioritise the integration of vital datasets — spanning cloud, on-premises, and hybrid infrastructure, as well as departmental silos. Doing so ensures a unified view of the organisation’s information landscape, enabling insights that span customer segments, regional operations, and beyond.
The best part? Removing these data silos won’t just make your AI models better — it will help the entire organisation utilise the company’s data to its fullest potential.
Ensuring Robust Data Governance and Quality
As you leverage data for your AI model, it’s important to remember that you must be a good steward of that data to maintain trust with clients, users, and the broader public. Regulations like the EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) provide legal frameworks to ensure data usage remains transparent and user privacy is maintained. Additionally, with regulations like the EU AI Act, businesses must establish strong governance frameworks to ensure AI models are built on trusted, traceable, and compliant data.
However, the data governance processes required to meet these regulations remain a major hurdle for proper AI adoption — 62% of organisations cite it as their most important data challenge.
Data governance helps align technology, people, and processes, enabling organisations to have a wider understanding of its data. This creates enhanced visibility, which strengthens the accountability and quality of an organisation’s data assets and allows it to be correctly monitored to ensure compliance with privacy and security regulations.
A comprehensive approach to building and maintaining data quality should also be applied — leveraging a framework that incorporates core business rules, automated validation processes, and proactive anomaly detection. With these capabilities, businesses can stay ahead of potential issues, identifying and resolving data quality challenges quickly and efficiently. This proactive stance ensures that AI models are powered by trustworthy data, ultimately leading to more accurate predictions, better business decisions, and improved outcomes across the board.
Addressing AI Bias with Data Enrichment
Even when data is accurate and complete, AI models may still fall short if they lack context. Without understanding the broader picture, AI models are more likely to misinterpret anomalies or generate biased outputs.This erodes trust, as 67% of organisations do not trust the data used for decision-making. And if users don’t trust the results, they won’t fully embrace AI initiatives.
Enriching data with reliable third-party sources and geospatial insights can significantly improve its diversity and uncover patterns that may otherwise go unnoticed. This can include points of interest data, demographics data, detailed address information, and more to provide the contextual intelligence AI needs to make informed predictions. Insurers, for example, can leverage risk data relating to natural disasters to greatly improve the speed and accuracy of quotes and optimise claims experiences for their customers.
As AI evolves at record speed, it’s no longer about chasing the next model; it’s about scaling responsible AI with the right data, infrastructure, and cross-functional culture behind it.Those who embrace innovation, adapt quickly, and develop robust data strategies based on accurate, consistent, and contextual data will be the ones who succeed in unlocking the true value of their AI investments.
