AI Governance Without Experts? The Risk of Falling Behind in an AI-Driven Economy
AI Literacy: The New Requirements Under the AI Act
As of February 2, 2025, the European Union’s Artificial Intelligence Act (AI Act) introduces specific obligations for organizations to ensure AI literacy among their employees. This is not just a formal requirement—it reflects the growing need for professionals across industries to understand AI’s capabilities, risks, and ethical implications. As AI continues to integrate into business operations and decision-making, organizations must ensure their workforce is equipped to use it responsibly and effectively.
The AI Act mandates that both AI providers (those developing or marketing AI systems) and deployers (organizations implementing AI solutions in their operations) take steps to enhance AI literacy within their teams. This applies to technology companies building AI-powered products, financial institutions using AI for risk assessment, healthcare organizations relying on AI diagnostics, and even public sector bodies automating services. In essence, any entity that interacts with AI must ensure that its employees understand how AI works, how to manage its risks, and how to comply with regulations.
AI literacy extends far beyond technical knowledge. It includes understanding AI risks, ethical concerns, regulatory compliance, and risk management strategies. Employees should be able to recognize AI risks and limitations, such as bias, security vulnerabilities, and misinformation risks, while also grasping regulatory requirements to ensure compliance with the AI Act. Moreover, they need to make informed decisions when deploying AI tools and develop responsible AI usage policies within their organizations. To foster AI literacy, the AI Act encourages companies to establish internal guidelines, training programs, and interdisciplinary learning initiatives that enable employees to apply AI knowledge in their specific work environments.
While there are no direct fines for failing to provide AI literacy training, non-compliance can have serious consequences. Organizations could face civil liability if AI-related harm occurs due to employee misuse or misunderstanding, and regulatory scrutiny may increase if a lack of AI literacy leads to non-compliance in other areas of the AI Act. Beyond legal risks, poor AI literacy results in operational inefficiencies, where employees misinterpret AI-generated insights, leading to biased decision-making, ethical blind spots, and overall business inefficiencies.
To navigate the AI era successfully, organizations operating in the EU market must proactively develop AI literacy programs. Ensuring employees are well-equipped to understand, evaluate, and interact with AI is not just a compliance measure—it is a strategic necessity for responsible AI adoption, risk mitigation, and long-term success.
Key Topics in AI Literacy
AI literacy is not just about understanding how AI works—it’s about knowing how to use it responsibly, securely, and in compliance with regulations. Organizations must ensure that employees across various functions, from technical teams to policymakers, legal experts, and business leaders, develop a broad understanding of AI’s risks, governance, and applications.
There is no one-size-fits-all approach to AI literacy, as different roles require different levels of expertise. Some employees may need to understand AI ethics and compliance, while others must focus on technical implementation, risk management, or security concerns. Below is a list of recommended topics for AI literacy programs—these serve as guidance but are not exhaustive, as organizations should tailor their training to their specific industry and needs.
| Title | Topic Essence | Target Employees |
| ISO AIMS (ISO/IEC 42001:2023) Usage | Understanding and implementing the AI Management System standard for structured AI governance and compliance. | Management, Compliance, Risk Officers |
| AI Act Compliance | Training on legal obligations, risk classification, and CE marking for AI systems. | Compliance, Legal, Management |
| Introduction to AI | Basic AI concepts, technologies, and applications for all employees working with AI. | General Workforce, Management, Technical Teams |
| AI Ethics | Understanding fairness, accountability, transparency, and mitigating AI bias. | Management, Ethics Officers, Compliance, Technical Teams |
| AI Policy Development | Creating AI governance policies, documenting AI decisions, and ensuring accountability. | Management, Compliance, Legal |
| Model Security | Protecting AI models from cyber threats, adversarial attacks, and unauthorized access. | Cybersecurity, IT, AI Engineers |
| Bias and Unpredictability | Testing AI for biases and improving its reliability in real-world applications. | AI Engineers, Data Scientists, Risk Officers |
| Explainability and Transparency | Techniques to make AI systems more interpretable and transparent for users and regulators. | Technical Teams, Compliance, Management |
| Bias Mitigation and Fairness | Ensuring algorithmic fairness using debiasing techniques and compliance with ethical AI principles. | AI Engineers, Ethics Officers, Compliance |
Each of these topics supports responsible AI adoption and ensures that employees can effectively interact with AI systems in their respective fields. By equipping staff with the right knowledge, organizations can mitigate AI-related risks, ensure compliance with regulations, and enhance decision-making processes. AI literacy is not just about technical understanding—it’s about integrating AI responsibly into everyday business operations, ensuring that employees at all levels can navigate the opportunities and challenges AI presents.
The Role of ISO AIMS in AI Literacy
AI literacy in organizations extends beyond simply understanding how AI works; it involves ensuring that AI systems are governed, monitored, and aligned with ethical and legal standards. This is where ISO/IEC 42001:2023, the Artificial Intelligence Management System (AIMS), plays a crucial role. Designed to provide a structured framework for managing AI, this standard helps organizations establish clear governance structures, risk management strategies, and compliance measures that align with the EU AI Act and other regulatory frameworks.
ISO AIMS defines best practices for AI governance, lifecycle management, security, and risk assessment, ensuring that organizations develop AI responsibly while maintaining transparency and accountability. By following this framework, businesses can create structured processes for AI deployment, minimize risks, and foster a culture of continuous improvement. AI systems require regular monitoring, auditing, and adaptation to ensure they function as intended and comply with evolving regulatory requirements.
A key aspect of ISO AIMS is its focus on AI governance and leadership, ensuring that organizations establish internal accountability structures and clear roles for managing AI systems. Risk management is also a critical component, as the framework provides methods for identifying, assessing, and mitigating AI-related risks, such as bias, security vulnerabilities, and compliance challenges. Additionally, ISO AIMS outlines AI lifecycle management principles, covering systems from development to deployment, monitoring, and decommissioning, promoting fairness, transparency, and ongoing ethical assessments.
Another important pillar of ISO AIMS is data governance and model security, which ensures that AI models are trained on high-quality, unbiased, and privacy-compliant datasets while being protected from adversarial attacks and unauthorized access. Organizations that implement these principles can improve AI transparency, security, and long-term reliability. To further strengthen AI oversight, the standard emphasizes performance monitoring and continuous learning, requiring organizations to evaluate AI models regularly, detect issues such as bias or performance drift, and implement feedback loops for ongoing improvement.
By incorporating ISO/IEC 42001, organizations ensure that AI literacy is not just a theoretical concept but an integral part of daily operations. Employees across different functions become familiar with AI governance best practices, risk management, and compliance requirements, enabling them to deploy AI ethically and securely. Rather than treating AI literacy as a one-time training initiative, ISO AIMS encourages organizations to embed a culture of continuous learning and responsible AI management, ensuring long-term success in an AI-driven world.
Building AI Literacy for Daily Work and Practical Use
AI literacy goes beyond understanding regulations and governance—it involves the ability to apply AI tools effectively in everyday tasks. Employees across various industries must develop the skills to interact with AI systems, assess their reliability, and use them to enhance productivity. Without practical knowledge, AI remains an underutilized tool, and organizations risk inefficient implementation or compliance failures.
To build AI literacy in a way that benefits daily work, organizations should start by ensuring employees have a general understanding of AI. This includes knowledge of how AI models function, their strengths and limitations, and their applications across different industries. Employees should develop realistic expectations, understanding that while AI can generate valuable insights and automate repetitive tasks, it may also produce errors or biased outcomes. This foundation helps prevent the misuse of AI tools and encourages responsible deployment.
A critical skill in AI literacy is prompt engineering, which enables employees to communicate effectively with AI models. By structuring prompts correctly, providing sufficient context, and using logical sequencing, employees can enhance the relevance and accuracy of AI-generated responses. Poorly formulated prompts often result in vague or misleading outputs, whereas well-crafted prompts lead to actionable insights. This skill is particularly important for professionals using AI for data analysis, research, decision-making, and customer interactions.
While AI can assist with decision-making, it should never replace critical human judgment. Employees must be trained to verify AI-generated content, recognize biases, and understand the uncertainty inherent in AI systems. For example, an AI-generated financial recommendation should be cross-checked against industry standards and expert assessments before implementation. Organizations should ensure employees are aware of AI’s limitations, reinforcing the principle that AI augments human expertise rather than replacing it.
Another essential component of AI literacy is ethical AI use and regulatory compliance. Employees must understand AI-related risks, such as data privacy violations, bias, and discriminatory decision-making. Following internal AI governance policies, knowing when to escalate potential risks to leadership, and ensuring AI-driven processes align with GDPR and other data protection regulations are key responsibilities for AI users. Ethical AI literacy is particularly relevant for HR teams, legal departments, compliance officers, and customer-facing roles.
To successfully embed AI literacy into daily operations, organizations must develop structured AI training programs tailored to different roles. Providing clear AI guidelines and best practices ensures consistency across departments, while fostering collaboration between AI specialists and non-technical employees creates a more informed and AI-ready workforce. Given that many organizations are now establishing internal AI compliance departments due to the AI Act’s requirements, training existing employees is a critical first step—especially considering the shortage of AI specialists in the labor market. A well-defined strategy for AI roles, responsibilities, and workflows will be essential to ensuring effective and sustainable AI adoption.
The Changing AI Job Market: Growing Demand, Skill Gaps, and a Shortage of Specialists
As AI continues to reshape industries, organizations are under pressure to build internal AI expertise to manage, regulate, and optimize AI deployment. While AI is creating new opportunities, many companies are struggling to find specialists with the necessary interdisciplinary skills in AI governance, risk management, compliance, and ethical oversight. This skills gap is widening, making it difficult for businesses to comply with emerging regulations, such as the AI Act, and effectively integrate AI into their operations.
Many organizations plan to establish dedicated AI compliance and governance teams, but the labor market is not keeping pace with demand. As a result, companies must rethink their approach to AI workforce development, ensuring that AI literacy becomes an integral part of their operations. Some of the key emerging job roles include:
- AI Officer – Oversees AI governance, compliance, and strategic deployment, ensuring AI aligns with business and regulatory goals.
- AI Risk Manager – Identifies and mitigates risks associated with AI, addressing security vulnerabilities, model reliability, and ethical concerns.
- AI Compliance Officer – Ensures that AI solutions meet AI Act requirements, ISO standards, and other regulatory frameworks.
- AI Policy Analyst – Monitors AI regulations, assesses their impact on business operations, and advises on compliance strategies.
- AI Ethics Officer – Focuses on fairness, bias mitigation, and responsible AI development, ensuring AI systems align with ethical standards.
- Head of AI – Leads AI strategy, integration, and development within an organization, ensuring AI aligns with business goals.
- AI Quality Assurance Manager – Ensures the reliability, accuracy, and security of AI models through ongoing testing and validation.
- AI Privacy Officer – Manages AI-related data privacy risks, ensuring compliance with GDPR and other data protection laws.
- AI User Experience Manager – Optimizes AI interactions for end-users, ensuring transparency, usability, and accessibility.
- AI Training and Development Manager – Leads AI literacy programs and employee upskilling efforts to ensure teams can use AI effectively.
Despite the urgent need for these roles, the market lacks enough specialists to fill them. Universities and training institutions are only beginning to adapt, and many professionals in related fields lack the AI-specific knowledge required to transition into these positions. This places significant pressure on organizations to act quickly, ensuring their workforce is prepared for AI-driven transformation.
To bridge this skills gap, organizations do not have to rely solely on in-house expertise. External AI consultants and compliance specialists can help companies define AI-related job roles, structure AI governance departments, and develop training programs tailored to business needs. These experts provide strategic guidance on AI implementation, risk management, and regulatory compliance, helping organizations navigate the complexities of AI adoption without relying on an already overstretched job market.
Businesses that proactively seek external expertise to structure their AI teams and develop workforce competencies will gain a significant advantage in AI governance and regulatory compliance. In contrast, those that delay will face increased risks, non-compliance issues, and missed opportunities for innovation. AI literacy is no longer a distant priority—it is a business necessity, and companies must act now to build sustainable AI capabilities before the skills gap becomes an even greater barrier to growth.
About author
Dr. Valentas Gružauskas is an expert in AI governance, conformity assessment, and ethical AI applications. As an Associate Professor at Vilnius University and CEO of AI Conformity & Research Consulting, he leads initiatives in AI regulation and ISO standards. He actively participates in AI governance through the Horizon Europe Programme and various standardization committees. Dr. Gružauskas has made significant contributions to fields like machine learning, deep learning, and agent-based modeling, with a focus on AI ethics and data depersonalization. His work is widely published in esteemed journals, reflecting his impact and recognition in the AI community.