A few years after the introduction of consumer AI, companies are rushing to create governance structures, appoint AI officers, write policies and formalize oversight processes. The goal is to ensure that AI adoption delivers measurable value while minimizing operational, legal and reputational risks. But on the road to innovation, organizations are overlooking something important: the state of their data.
Many companies have been collecting transactional, operational and customer data for decades. In the AI era, how that existing data is managed will determine whether AI systems deliver meaningful returns or amplify existing weaknesses.
Data bases for AI management
The public debate about the use of AI data has focused mainly on model developers scraping the open internet, including social media, books again journalismto train productive AI systems. These processes have caused a backlash on privacy and copyright grounds, revealing unresolved questions about what constitutes fair use in the digital age.
Little attention has been paid to how the businesses themselves use the data. Harvard’s 2025 AI Index report found that 88 percent of organizations use AI in some way. These companies feed internal data into AI models to guide operations and generate insights. But much of that data was not collected with AI deployment in mind. From a management perspective, business data is often incomplete, inconsistently labeled, mislabeled or does not adequately protect personal and sensitive data.
This creates a structural gap. While companies are investing in AI management, many are ignoring the data bases those systems depend on. Based on our experience enriching organizations with responsible AI and data management systems, the conclusion is clear: AI governance starts with data management.
The dangers of poorly managed data
If AI systems are built on a weak data base, risk is inevitable. Start with honesty. AI systems fed with incomplete or unsupervised data will produce erroneous results. Starbucks delivery of An AI-powered innovation tool shows the point: it is designed to do automatic stock calculation and replenishment, the system was given wrong data. The result was stockpiling and product shortages, which resulted in reduced sales. Instead of driving well, the system introduces new costs.
Bias presents a second, more complex danger. AI models trained on datasets that favor certain groups will produce biased results. A 2025 environmental study of large-scale linguistic models trained on emergency department data found that tools are more likely to do so they recommend more invasive treatments for Black, LBGTQ+ and homeless patients than for other groups, replicating the biases embedded in the training data. Similar concerns arise in all applications for hiring, lending, insurance and law enforcement, where biased data can directly impact access to jobs, credit and public service. For businesses, adopting AI tools produces biased results with legal, financial and reputational consequences that are difficult, and expensive, to reverse.
Poor data management also undermines transparency and accountability. When training data, validation procedures and model performance are poorly documented, organizations accumulate “documentation debt.” This liability limits their ability to explain how decisions are made, with negative consequences for compliance, incident investigations and audits.
The dangers continue even further. Re-purposing data without a clear legal basis may violate data protection laws. Weak data use controls increase the likelihood of using protected intellectual property. Biased or incomplete data sets can create negative human rights impacts, especially when automated systems influence employment, health care, access to finance or housing.
These risks are not isolated compliance failures. Rather, they are structural consequences of treating data management as secondary to AI implementation.
Making your data AI-ready
Unlike model development, which is often dependent on external vendors, data management remains firmly within the organization’s control. Companies that want to extract value from AI should start there.
The first step is to create a complete database. Organizations need a clear record of what data they hold, where it comes from and the legal basis for using it. This includes identifying what additional tests—including privacy, regulatory or risk-related—are needed before data can be repurposed for AI. A well-designed inventory not only supports compliance but enables rapid and confident deployment of AI programs by reducing data quality uncertainty and risk exposure.
Second, organizations should establish a data privacy policy. Data assets should be classified according to their sensitivity, value and regulatory obligations. The aim is to protect the privacy, integrity and availability of data used in AI applications while ensuring that they meet both legal requirements and operational standards. Developing such a policy requires answering a few deceptively simple but often overlooked questions: What data do we hold? How sensitive is it? What laws govern its use?
Third, roles and responsibilities must be clearly defined. Effective data governance depends on accountability. Data owners must be responsible for accuracy and classification, data custodians for storage and safe handling and data users for appropriate applications. Establishing these roles allows organizations to create safeguards when transferring their data to AI systems.
Existing standards and regulations provide practical guidance. I EU AI legislation sets the basic requirements for data quality and management in AI systems. International standards like ISO 42001 establish data-related guidelines for AI applications, while ISO 27001 again ISO 38500 set comprehensive data management requirements. Even in less regulated markets, these frameworks provide an effective starting point for building internal governance maturity.
Data readiness is AI readiness
Business leaders shouldn’t just ask if their organizations are ready for AI They should also consider whether their data is ready. AI systems cannot compensate for weak data bases. Without unified, well-governed data, organizations are at risk of investing in tools that increase inefficiencies, introduce new liabilities and fail to deliver returns.
Policymakers, experts and businesses are still debating where responsibility for AI governance should lie. But in the question of internal data quality, there is no ambiguity: accountability belongs to the organization. Businesses that treat data management as a priority are the ones in the best position to turn AI investments into a competitive advantage, and protect their decisions when scrutiny comes.
Amelia Williams is a Senior Research Impact Officer at Trilateral Research with expertise in science communication at the intersection of emerging technologies, environmental issues, ethics and policy. At Trilateral, he supports the development and implementation of policy-related research projects, media and industry collaboration.
