How AI Literacy Transforms Risks into Competitive Advantages
What does AI literacy mean in practice, and how does an organisation meet the EU AI Act’s requirements? In this article, we examine four key components and four concrete actions.
Martti Asikainen 17.6.2026 | Photo is created with AI
In the previous article, I examined how shadow AI threatens companies from Samsung to Amazon, and why using AI without understanding can lead to data breaches, erroneous decisions and even breaking the law (Carlini et al. 2021; Huang et al. 2023; Mehrabi et al. 2021).
Banning AI use isn’t the solution. That’s like refusing to use the internet and burying your head in the sand whilst competitors sweep past you. What matters is building organisational AI literacy. AI literacy is the collective ability to understand AI’s fundamental principles, use it responsibly and identify the associated risks (Long & Magerko 2020; Ng et al. 2021).
Since 2025, this is no longer merely good practice, it’s a regulatory obligation for companies operating in the European Union, as stipulated in the EU AI Regulation (AI Act, EU 2024/1689). Article 4 obliges all AI system providers and users to ensure their personnel have an adequate level of AI literacy.
AI Literacy as a Regulatory Obligation
The AI literacy obligation applies to all AI systems, including general-purpose tools that can affect a company’s operations or decision-making (EU 2024/1689; NIST 2023).
This includes commonly used generative AI language models such as ChatGPT, Claude, Copilot and Grok. According to the regulation, companies must assess the risks associated with AI use and tailor training to staff roles and AI usage.
Simply distributing user manuals won’t cut it. Employees need proper training and guidance to understand AI’s potential opportunities, risks and harms. Not only to staff and the company itself, but also to customers and other stakeholders (e.g. Benlian et al. 2025; Long & Magerko 2020).
The EU AI Act and its enforcement come fully into force on 2 August 2026, when most of its provisions become subject to supervision. Penalties for violations are determined by national laws.
The regulation also applies to companies outside the EU if their AI systems impact EU markets or citizens. In other words, if your company serves European customers or operates in the EU in any capacity, this applies to you.
Four Components of Adequate AI Literacy
AI literacy isn’t merely technical expertise or a superpower—it’s a broader workplace competency and the company’s collective ability to operate safely with AI (Ng et al. 2021).
It builds on four complementary elements: ensuring understanding, identifying roles, assessing risks, and developing AI literacy.
1.
Ensuring Understanding
Employees don’t need to programme neural networks to grasp AI’s fundamental principles. Large language models like Claude produce text by predicting probable words or characters based on previous data—they’re not databases in the traditional sense (Brown et al. 2020; Bender et al. 2021). Understanding these fundamentals prevents unrealistic expectations and reduces the risk of misuse.
It’s useful for employees to understand how AI learns from data, where that data comes from, why it makes mistakes, and why it isn’t magic but fundamentally a statistical tool. When staff understand that AI produces probabilities rather than truths, it helps prevent erroneous assumptions and misuse (Bender et al. 2021).
2.
Identifying Roles
The company must know its own role. Are you an AI system provider or user? Are your employees developing AI or using it in their work? Your role determines responsibilities and obligations under the EU AI Regulation (EU 2024/1689). In employees’ daily work, this also means understanding how to use AI effectively in their specific role.
A marketing specialist knows how to use AI for content drafts but verifies facts and legality before publication. A programmer utilises coding assistance but understands when human assessment is essential. That’s when AI literacy transforms from a cost item into concrete benefits: improved productivity, more innovative working methods and better information security (e.g. Brynjolfsson et al. 2023)
3.
Assessing Risks
AI systems involve multi-layered risks: hallucinations, algorithmic bias, privacy challenges and erroneous conclusions (Huang et al. 2023; Mehrabi et al. 2021; Carlini et al. 2021). Risks are typically company-specific.
A law firm’s risks may relate to confidentiality, incorrect conclusions and hallucinations. In a marketing agency, risks might involve copyright issues or bias in marketing analyses. A recruitment company’s risks concern algorithmic bias, discrimination and poor hiring decisions. A healthcare organisation must consider patient safety and everything related to health technology.
When discussing AI literacy, risk assessment also means awareness of AI’s societal and moral dimensions. It covers factors such as transparency, accountability and non-discrimination (Ng et al. 2021). This protects the company from legal problems, reputational damage and loss of stakeholder trust.
4.
Developing AI Literacy
AI literacy isn’t a one-off achievement. It’s an ongoing process. AI develops rapidly, even faster than legislation, and tools that were safe last year may be risky today. In the AI era, the companies that succeed are those that don’t train their staff just once but invest in continuous learning (Benlian et al. 2025; NIST 2023).
These four dimensions aren’t separate; they reinforce each other. Technical understanding of how AI learns from data helps explain why bias emerges. Identifying roles without risk assessment leaves the company vulnerable. Developing AI literacy without fundamental understanding would be like building a house without foundations.
Four Steps to Implementation
Haven’t mastered AI literacy yet? Perhaps you don’t know where to start. Don’t wait around. If you’re unsure where to begin, follow these four steps:
Create a Clear AI Policy for Your Company
Companies need governance frameworks that guide AI use, risk management and compliance (NIST 2023; ISO 2023). They define which AI tools may be used, for what purposes, and with what limitations.
At minimum, they should cover information security (what data may be entered), accuracy (mandatory human verification of AI outputs), intellectual property rights, ethical questions and compliance. A company’s AI policy isn’t a static document—it’s a living framework that’s updated regularly as technology and regulation evolve.
Tailor Training by Role
AI literacy builds on role-specific competencies and context-dependent use (Long & Magerko 2020; Benlian et al. 2025). Not everyone needs the same training. The development team needs deeper understanding of coding assistants and their limitations. Marketing needs expertise in content production, copyright and brand protection.
Leadership needs strategic insight into AI’s business potential and risks. HR needs understanding of non-discrimination and recruitment algorithm bias. An effective training programme recognises these differences and offers targeted training.
Build a Culture of Continuous Learning
AI develops rapidly. Regular updates, internal discussions and swift responses to new threats are essential. Companies that succeed in the AI era make AI literacy a continuous process, not a single training event.
In practice, this means every team member must be prepared to question old working methods and adopt new skills as part of daily work. The company’s ability to learn and adapt to technological development is central to managing and exploiting opportunities (NIST 2023).
Document and Monitor
Demonstrating compliance requires systematic risk management and documentation of governance practices, particularly for high-risk AI systems (EU 2024/1689; ISO 2023). The EU AI Act requires that companies can demonstrate they meet literacy requirements.
Keep records of who has received what training, when, and how competence is assessed. This isn’t just a regulatory obligation—it’s also a management tool to ensure that investments in AI literacy actually produce results. Systematic documentation also helps identify competence gaps in time and target future training resources where they’re truly needed.
AI Literacy as a Competitive Advantage
AI isn’t a future tool—it’s today’s reality. Your company’s employees are already using it, whether you like it or not. The question isn’t whether AI should be adopted, but how to manage its use in a way that maximises benefits and minimises risks.
AI literacy isn’t a technical luxury—it’s an operational necessity. It’s as critical as cybersecurity or financial competence, an area where ignorance isn’t innocence but vulnerability. Companies that invest in their staff’s AI literacy don’t merely fulfil regulatory obligations. They build competitive advantage, safeguard their reputation and ensure that AI is a resource rather than a risk.
The next time you hear your employee has asked ChatGPT something, ask yourself: did they truly understand what they asked, how to interpret the answer, and what risks they took by asking? If you’re uncertain about your answer, your company has a serious vulnerability. Consider carefully whether it’s one your company can afford. With AI, it’s not worth learning the hard way.
References
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the ACM Conference on Fairness, Accountability, and Transparency.
Benlian, A., Kettinger, W. J., Sunyaev, A., & Winkler, T. J. (2025). The AI literacy development canvas: A conceptual framework for workforce enablement. Business Horizons.
Brown, T. B., Mann, B., Ryder, N., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems.
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at work. National Bureau of Economic Research Working Paper. https://doi.org/10.3386/w31161
Carlini, N., Tramer, F., Wallace, E., et al. (2021). Extracting training data from large language models. USENIX Security Symposium.
Huang, J., et al. (2023). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint.
European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union.
ISO. (2023). ISO/IEC 42001: Artificial intelligence management systems.
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the CHI Conference on Human Factors in Computing Systems.
Mehrabi, N., Morstatter, F., Saxena, N., et al. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys.
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence.
NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National Institute of Standards and Technology.
Author
Martti Asikainen
Communications Lead
Finnish AI Region
+358 44 920 7374
martti.asikainen@haaga-helia.fi
