In an era where artificial intelligence (AI) is revolutionising industries, many organisations embark on ambitious AI projects with the hope of driving efficiency, automation, and competitiveness. However, a significant portion of these projects falter due to foundational data challenges. According to Gartner, by 2025, 30% of generative AI projects will be abandoned due to poor data quality, inadequate risk controls, and unclear business use cases. This statistic underscores a fundamental truth: AI is only as effective as the data that fuels it.
For organisations to harness AI’s potential, robust data governance must be in place. Here at Oakland, like many data management consultancies we’ve been exploring AI-driven transformations, the differences between data governance and AI governance, and how businesses can overcome common challenges to ensure AI success.
The Critical Role of Data in AI Success
AI, at its core, is a data product. Whether in machine learning models, large language models (LLMs), or generative AI applications, the principle of “garbage in, garbage out” remains true. If AI systems are trained on poor-quality data, the outcomes will be flawed, potentially leading to misinformed decision-making, regulatory compliance risks, and reputational damage.
Key data challenges impacting AI initiatives are:
- Data Quality Issues – AI models depend on accurate, complete, and consistent data. Missing values, duplication, and outdated records compromise AI outputs.
- Data Ownership Ambiguities – A lack of clear data ownership makes accountability difficult and slows decision-making.
- Understanding Data Landscapes – Without a well-documented data architecture, it is challenging to trace data lineage, assess data integrity, and ensure compliance.
- Bridging the Gap Between Vision and Execution – Business leaders often have ambitious AI visions, but data professionals struggle to translate these into feasible implementations due to underlying data constraints.
- Unclear Business Value – Lack of alignment to wider business goals and challenges to determine where AI efforts will deliver most value.
Additionally, there is often a disconnect between executives pushing for AI adoption and the data teams responsible for managing the infrastructure. Many assume that AI will work out of the box, failing to recognise the need for strong governance principles that allow AI initiatives to scale successfully. Without this, projects often result in frustration, misalignment, and ultimately abandonment.
Data Governance as the Foundation of AI Success
This is where working with a data management consultancy like Oakland can play a crucial role in addressing these challenges. We provide organisations with structured methodologies to manage data effectively, ensuring data is accurate, reliable, and accessible for AI applications.
Key Elements of Data Governance for AI
- Data Lineage and Provenance – Organisations must track where data originates, how it has been transformed, and who is responsible for it.
- Data Quality Management – Implementing data validation, cleansing, and monitoring processes ensures high-quality input for AI models.
- Ownership and Accountability – Establishing clear roles for data stewardship fosters responsibility and trust.
- Privacy and Security Controls – Protecting sensitive data and ensuring AI models comply with regulations like GDPR and the EU AI Act is critical.
- Feedback Mechanisms – Continuous monitoring and improvement of data assets and AI outcomes create an adaptive governance framework.
Organisations that embrace these principles avoid the common pitfalls of AI projects, such as reliance on poor-quality datasets, uncertainty around data sources, and misalignment between business strategy and AI deployment.
Data Governance vs. AI Governance: What’s the Difference?
While data governance focuses on managing data assets within an organisation, AI governance extends beyond data to include:
- Model Lifecycle Management – Ensuring AI models are built, tested, deployed, and monitored according to best practices.
- Regulatory Compliance – Adhering to AI-specific regulations such as the EU AI Act and industry guidelines.
- Ethical Considerations – Addressing fairness, bias, and transparency in AI decision-making.
- Risk Management – Identifying and mitigating risks associated with AI models, including unintended consequences and security threats.
Ultimately, AI governance builds on data governance. Organisations with a strong data governance framework can more easily scale AI governance practices, ensuring that AI models operate with integrity and compliance.
As organisations navigate this landscape, they must also consider how AI itself can assist in governance efforts. AI-driven tools can help automate data lineage mapping, anomaly detection, and compliance monitoring, making governance more scalable and efficient.
The Business Case for Investing in AI and Data Governance
To secure executive buy-in for AI and data governance initiatives, organisations should:
- Align with Business Objectives – AI initiatives should support core strategic goals, such as improving customer experience, increasing operational efficiency, or enabling new revenue streams.
- Prioritise Use Cases – Focusing on specific, high-impact AI applications rather than boiling the ocean ensures measurable results and quick wins.
- Leverage Existing Capabilities – Organisations with mature data governance practices can extend them to AI governance, optimising resource utilisation.
- Demonstrate ROI – Quantifying cost savings, risk reduction, and performance improvements strengthens the case for investment.
How Data Governance and AI Governance Enhance Business Performance
Improved Decision-Making
With well-governed data, organisations can confidently rely on AI-driven insights to make strategic business decisions, reducing uncertainty and improving outcomes.
Increased Efficiency and Scalability
AI-powered automation can eliminate repetitive tasks, but only if the underlying data is trustworthy. Data governance ensures that automation is built on a solid foundation, allowing businesses to scale AI initiatives seamlessly.
Regulatory Compliance and Risk Mitigation
AI regulations are rapidly evolving, and organisations that fail to comply face fines and reputational damage. Data governance ensures that AI models adhere to legal and ethical standards, reducing regulatory risks.
Competitive Advantage
Organisations that integrate AI governance with strong data management practices differentiate themselves by delivering AI solutions that are accurate, reliable, and compliant, gaining an edge over competitors.
Implementing a Structured Data Governance and AI Governance Framework
A structured, standardised approach to governance is key to AI success. Best practices include:
- Building a Cross-Functional AI Governance Team – Engage data stewards, compliance officers, IT leaders, and business stakeholders.
- Developing a Clear Data Strategy – Define data ownership, quality standards, and governance policies.
- Leveraging AI for Data Governance – Use AI to automate data lineage mapping, quality assessments, and compliance monitoring.
- Establishing Trust and Transparency – Ensure AI decisions are explainable, auditable, and aligned with ethical standards.
- Continuously Refining Governance Practices – AI and data governance should evolve with changing business needs and regulatory landscapes.
Organisations that view AI as a data-driven capability rather than just a technological innovation position themselves for long-term success. Data governance provides the critical foundation needed for AI initiatives to thrive, ensuring high-quality data, clear accountability, and compliance with regulations. By aligning data governance and AI governance, businesses can unlock AI’s full potential while mitigating risks, ultimately driving better decision-making, efficiency, and competitive advantage.
For enterprises looking to embark on or refine their AI journey, investing in data governance is not optional it’s a necessity.
“AI, Generative AI or Agentic AI holds great promise. However, many organisations currently feel held back by the necessity to validate their data thoroughly, unclear understanding of AI risks, and the potential for unforeseen issues, like bias and data security. Either making them hesitant to fully commit, i.e. death by POC, or unrealised sustainable value when deployed.
These are unchartered waters for most. The good news is you can choose the pace you want to go. Navigating AI governance is like steering a ship through a storm; robust data governance is your compass, guiding you to safer, clearer waters”.
Zareene Choudhury – Oakland Data Governance Lead
Want to find out more?
If you’d like to learn more about Oakland’s data governance consulting services, then get in touch by emailing [email protected] If you’d like to learn more about what a suitable data governance framework looks like, click on the link.
