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The European Union’s AI Act (The EU AI Act) is set to bring significant changes across organizations, impacting not only data leaders but everyone involved in integrating AI tools into business processes. Compliance with this new legislation will require a collective effort to ensure that AI systems meet stringent governance, transparency, and oversight standards.

From AI governance leaders to business executives, product owners, and data chiefs, each role plays a crucial part in aligning their strategies with the Act’s requirements. The following breakdown offers key insights for these roles and how they can contribute to maintaining compliance while driving organizational value.





AI governance program leaders


Be realistic & delivery-focused


AI governance programs must strike a balance between ambition and practicality. Being overly ambitious or designing overly complex governance frameworks can lead to inefficiency and unnecessary delays. Leaders need to focus on the specific requirements of the EU AI Act and prioritize achieving key outcomes, such as ensuring high-risk AI systems comply with transparency, human oversight, and data governance rules, rather than aiming for perfection from the outset.

The key is to break down the process into manageable steps, set clear, realistic milestones, and focus on delivering value at each stage. This delivery-focused mindset means implementing the most critical governance mechanisms first, refining processes as the organization's AI capabilities grow, and responding to regulatory changes.

Understand your organization’s AI & data culture


Before implementing AI governance, it is crucial to assess your organization’s current AI culture and maturity. This involves understanding how AI is perceived and utilized across the company, the decision-making processes surrounding AI initiatives, and the current level of expertise. Are AI initiatives centralized or decentralized? How mature are the teams in understanding AI's risks, benefits, and ethical implications? Are decisions made by technical experts, business leaders, or a combination of both?

Keep it real – What actually works for you?


Governance frameworks need to be designed in a way that genuinely works within the context of your organization. Leaders must avoid a one-size-fits-all approach and instead focus on creating governance policies and practices that are aligned with the company’s unique needs and objectives.

This might mean adjusting compliance measures to the type of AI systems being deployed, the level of risk involved, or the specific regulatory environment in which the company operates. By focusing on what’s practical and effective for the business, leaders can ensure governance frameworks are not only compliant but also operationally efficient, minimizing bureaucracy and maximizing impact.

Leverage what you already have


Implementing AI governance is not just a technical challenge; It’s a form of change management. To integrate AI governance efficiently, it’s essential to leverage existing governance mechanisms, risk management frameworks, and compliance structures. For example, many organizations already have robust data governance policies in place. These can be expanded to include AI-specific concerns like bias detection, fairness, and transparency.

Be ready to evolve & adapt


AI governance is not static- It requires ongoing adaptation to keep pace with technological advancements and evolving regulatory landscapes. This means regularly reviewing AI systems and governance frameworks to ensure ongoing compliance, refining policies to address new risks, and staying ahead of regulatory updates. By adopting a mindset of continuous evolution, organizations can remain compliant while staying competitive in the rapidly changing AI landscape.

Business executives and product owners


Understand your role in the AI value chain


The AI value chain includes various stages, such as data collection, algorithm development, deployment, and end-user interaction. Knowing your exact place in this chain is essential for meeting the Act’s requirements and for mitigating risks. Business leaders must stay informed of industry developments, technological advancements, and regulatory updates to understand how changes in AI governance might affect their role. Regularly engaging with AI and regulatory experts, attending industry conferences, and keeping up with reports on AI governance are practical ways to stay informed.

Be an active stakeholder in your organization’s AI governance program


Executives and product owners do not need to be technical experts in AI to effectively contribute to governance. However, they should be informed and actively engaged in the organization's AI governance programs. This means participating in high-level governance discussions, ensuring that AI strategies align with both business goals and regulatory requirements, and advocating for responsible AI practices at every stage of development.

Evolve your business model, strategy, and product roadmap to stay aligned


As AI technology and regulations evolve, so must business models, strategies, and product roadmaps. The EU AI Act introduces new compliance requirements, especially for high-risk AI systems, which may require changes in product features, data usage, and customer engagement. Executives and product owners must be ready to adapt their business models to account for these new requirements.

Evolving the business model also includes being open to new opportunities that arise from responsible AI use. Companies that prioritize AI compliance can enhance their reputation as ethical leaders, creating a competitive advantage. Strategic shifts, such as focusing on transparency or AI-driven sustainability, can attract customers and investors who prioritize responsible technology.

CDOs & CDAOs


Understand your data


Under the EU AI Act, CDOs and CDAOs have a core responsibility to have a deep understanding of the data ecosystem they oversee. This includes knowing where data comes from, how it’s processed, and how it’s used to fuel AI systems.

Data leaders should work to demonstrate a comprehensive understanding of the data lifecycle and ensure data governance frameworks are robust enough to manage not only the volume of data but also its quality, origin, and purpose. This means focusing on both inputs (the raw data being used to train and develop AI models) and outputs (the decisions, recommendations, or predictions generated by AI systems). Effective data governance around these elements ensures that the AI systems are reliable and that potential biases or errors can be detected and corrected early.

Prioritize your use cases to maximize the chance for success upfront


One of the most important steps CDOs and CDAOs can take is to ensure that AI use cases are aligned with the organization’s business strategy and the requirements of the EU AI Act from the very beginning. To do this, they must prioritize use cases that deliver business value while also being compliant. This involves carefully selecting AI projects that align with long-term goals and ensuring they contribute to strategic priorities such as customer experience, operational efficiency, or new product development.

However, CDOs must also recognize that traditional data tools and processes may need updating to manage the increasing complexity of AI systems. AI use cases, particularly those classified as high-risk under the EU AI Act, often require more advanced data management capabilities, including real-time data monitoring, transparency in decision-making processes, and explainability. Traditional data governance tools, which may focus on static reports or retrospective analysis, might not be sufficient for these dynamic and complex AI environments.

Be proactive


The EU AI Act presents CDOs and CDAOs with a unique opportunity to improve the organization’s overall data governance framework and enhance its data and AI culture. One of the ways to do this is by increasing data literacy across the organization. By educating teams on the importance of data quality, transparency, and ethical AI use, data leaders can create a culture where responsible AI becomes second nature.

Conclusion


The EU AI Act can also act as a catalyst for evolving the organization’s overall data strategy. Data leaders should seize this moment to refine their data governance policies, ensuring that they are not only compliant but also aligned with broader business goals. This means developing a forward-looking data strategy that incorporates not just compliance needs but also scalability, innovation, and sustainability. The ability to use data effectively and responsibly will become a competitive advantage in the coming years, especially as AI becomes more integrated into business operations.

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