In most industries, artificial intelligence is becoming a standard part of operations and decision-making. Even if a company does not “formally” develop or use AI, it is likely that certain forms of AI are already in use, through ChatGPT, automated decision-making, predictive tools, or related technologies. As AI becomes more deeply integrated into processes, risks grow proportionally: primarily in relation to data protection, algorithmic bias, and regulatory compliance. Without a clear framework for managing AI, an organization exposes itself to risks that undermine trust, create legal liabilities, and may disrupt critical operations. Developing an AI company policy is therefore a key mechanism for risk mitigation: it establishes the foundations for responsible use, ensures accountability within the organization, and positions the company as a thoughtful actor in the market. If you need to prepare an AI policy but are not sure where to start, the following provides a practical framework tailored to business needs.
Understanding the Concept of an AI Policy
What is an AI policy?
The term “AI policy” has a different meaning depending on context: it can refer to (i) an ethical policy (guidelines for responsible development and use), (ii) a governance policy (legal and regulatory frameworks), (iii) AI-based decision-making policy (ML strategies), or (iv) an internal organizational AI policy (company rules for managing AI systems). This text addresses the last category, which regulates how AI systems are developed, used, and monitored within a company (hereinafter: “AI policy”).
An AI policy functions as a “roadmap” for aligning practices with legal and ethical standards and it guides employee behavior in line with defined values and strategy. As a formal commitment to responsible AI adoption, it allows the benefits of AI to be maximized while minimizing risks (bias, security incidents, legal complications). Since goals, structure, and risks are specific to each organization, the policy must be tailored to the particular context and responsibilities associated with AI management. The AI policy is an overarching document, setting “house rules” by defining objectives, principles, and safeguards. It is therefore the starting point for building strong AI governance. In practice, there are two approaches: (a) supplement existing policies (when AI is banned or tightly restricted), or (b) adopt a dedicated AI policy (when AI is permitted and actively applied, e.g., in decision-making, data processing, or client interactions).
Strategic Advantages of an AI Policy and Risks of its Absence
Before adopting an AI policy, it is important to consider the benefits it brings and the risks of not having one. We highlight the strategic value of a well-drafted AI policy and the importance of informed, responsible decision-making in an environment where AI drives processes.
Reasons to adopt an AI policy:
- Regulatory compliance. An AI policy ensures alignment with the law and reduces the risk of non-compliance with regimes such as GDPR, HIPAA, and new AI regulations (e.g., the EU AI Act).
- Data protection and privacy. Since generative AI processes large amounts of data, without clear measures employees may inadvertently share confidential or personal information; the policy defines protocols for data collection, storage, sharing, and processing.
- Bias detection and elimination. Establishes periodic evaluations and reviews of outputs to detect discrimination (gender, race, age, etc.).
- Ethical and responsible use. Sets the implementation framework, protects reputation, and clarifies the role of AI in decision-making.
Risks when AI policy is absent:
- Unreliable or unchecked results. Generative AI provides answers based on statistical probability (which may be incorrect, partially correct, or biased); without an AI policy, there is no adequate mechanism for timely error detection.
- Unintentional security or data breach incident. For example, entering confidential client data into publicly available AI tools. An AI policy defines permitted tools, acceptable use, and limits in data handling.
- Compliance risks. Difficulty tracking regulatory changes increases legal exposure and the likelihood of sanctions.
Key Questions Before Drafting an AI Policy
An effective AI policy is not a mere administrative formality. It requires preparation that includes assessing the environment, tools, values, and risks, defining goals, forming competent teams, and establishing accountability. Before beginning to draft, consider:
a) Definition of AI in a business context. Is there an internal, agreed definition of what counts as AI, which systems and tools fall under that definition, and where they are already integrated?
b) Working group and management training. Has a multi-functional team been formed to lead the policy development process? Has the board/management received adequate training on key risks (bias, privacy, workforce impact)?
c) Purpose of the policy. What are the objectives and expectations of the AI policy? Does it primarily enable, restrict, or safeguard certain uses, tools, and behaviors?
d) Motivation for adoption. Growing use of AI tools, increased government oversight, internal concerns, or reputational risks?
e) Ethical principles and regulatory framework. What values does the organization want to build and which legal obligations must it meet (data protection, privacy, sectoral guidelines)?
f) Use cases and risks. Who uses AI, for what purposes, and in which contexts? What risks should be anticipated (bias, vulnerabilities, adverse outcomes) and which safeguards must be preemptively established?
g) Roles and responsibilities. How are responsibilities distributed across the AI system lifecycle and what training is needed to build adequate competencies?
h) Continuous monitoring and evaluation. The AI policy must evolve over time, requiring mechanisms for user feedback, periodic reviews, and performance monitoring.
i) Presentation of the policy. How will the policy be presented to employees? Is it clearly written, accessible, and explained in understandable language via internal channels (emails, trainings, platforms)?
These questions form the foundation for a structured and sustainable AI policy that reflects specific needs, regulatory obligations, and principles of responsible technology use.
Core Elements of an AI Policy
- Purpose and scope. Clearly state the reasons and objectives, aligned with the company’s strategy. Precisely define which AI systems and activities the policy applies to, and to which individuals, so implementation is unambiguous and verifiable.
- Fundamental ethical principles. The policy reflects values (e.g., OECD, UNESCO, HLEG frameworks) through governance rules to strengthen trust and reliable AI systems: responsible use; compliance with international, national, and sectoral regulations (data protection, privacy, consumer rights, IP, etc.); transparency and accountability (duty of transparency, accountability for results, ability to explain and justify outcomes, clearly defined supervisory responsibilities); centralized AI governance and compliance system (visibility of planned and active AI activities); data privacy and security (rules for personal and sensitive data, including anonymization, secure storage, and compliant processing); safety, security, and resilience (protection from attacks and functioning in adverse conditions); fairness and equality (active monitoring and elimination of discriminatory outcomes); human–AI collaboration (limits on reliance on AI recommendations); standards for third-party services (ethical and legal requirements for external AI services).
- Governance bodies and roles. The policy defines executive roles and responsibilities in the development, deployment, and monitoring of AI systems. If necessary, a high-level body within the company hierarchy is formed with a clear mandate and authority.
- Risk classification and management. All AI systems are categorized by purpose and risk class, distinguishing permitted, restricted, and prohibited systems. The policy describes identification, monitoring, and risk mitigation, including accuracy/reliability testing and appropriate measures for reducing or eliminating risks.
- Acceptable and prohibited use. The document lists permissible uses and strictly prohibited activities (e.g., political lobbying, categorization by protected characteristics, entering sensitive data into AI systems), with clearly defined boundaries.
- Continuous monitoring and audits. Periodic reviews of performance, accuracy, and fairness are foreseen, with controls and measures adjusted based on findings to ensure ongoing compliance and effectiveness.
- Training and awareness. Establishes a training and continuous education program, with employees obliged to understand risks, responsibilities, and ethical implications, and to comply with the AI policy.
- Incident reporting. Channels for reporting are established, with confidentiality measures and protection from retaliation, as well as procedures for handling AI incidents, from initial assessment to corrective and preventive measures.
- Policy violations. Consequences are prescribed for breaches of the AI policy (disciplinary measures, potential contract termination, and other legal consequences), in line with internal acts and applicable regulations.
- Review and updates. A schedule for regular reviews is defined and an individual or body is appointed responsible for updating the document to remain aligned with practice and regulatory changes.
Implementing the AI Policy
Successful implementation requires a structured, multi-phase approach ensuring both compliance and practical integration across the company.
Recommended steps:
- Step 1: Drafting with team-wide input. Prepare a draft according to the above guidelines, involving multiple team members from different departments, preferably through an AI Governance Committee, and conduct a mandatory legal review of the document.
- Step 2: Distribution and acknowledgment. Once adopted, the policy is communicated to all employees; each formally acknowledges being informed and obligated to comply.
- Step 3: Operational integration and alignment of procedures. Managers and employees define workflows and control mechanisms that align daily AI use with policy requirements, including team-specific procedures and decision-making protocols.
- Step 4: Continuous evaluation and improvement. The AI policy is treated as a dynamic document; regular alignment is based on audit findings, risk assessments, and operational feedback to fine-tune implementation.
A “Living” Framework, Not a One-Off Document
AI is a disruptive and dynamic technology that no organization, regardless of sector or size, can ignore. Instead of waiting for the “market to stabilize,” a proactive approach is recommended, which includes:
- identifying areas of greatest business value,
- comprehensive risk assessment,
- adopting a systematic and holistic AI policy,
- establishing a competent body to govern the use and development of AI systems.
Even when a standalone AI policy exists, it is usually necessary to revise existing internal policies, especially in data protection, information security, and acceptable technology use, to address the specific challenges AI brings. AI policy is therefore not isolated but part of a broader legal system and organizational ecosystem. It must remain dynamic, adaptable, and continuously applied to maintain relevance and effectiveness.
