# Quality Management — legal context bundle > Curated from overview.legal on 2026-08-22. Canonical page: https://overview.legal/topics/quality-management-system-requirements > Sources are cited per item. Verify against the official texts before relying on them. This new topic is needed to specifically address the detailed requirements, implementation procedures, and operational aspects of quality management systems for AI systems as mandated by Article 17 of the AI Act, which is distinct from general quality management concepts. ## Legislation (full text of key provisions) ### Quality management system *Source: AI Act, aiact-art-17-en, 2024-06-12 — https://overview.legal/posts/92256* ### Recital 81 — provider quality management system *Source: AI Act, aiact-rec-81-en, 2024-06-12 — https://overview.legal/posts/93844* The provider should establish a sound quality management system, ensure the accomplishment of the required conformity assessment procedure, draw up the relevant documentation and establish a robust post-market monitoring system. Providers of high-risk AI systems that are subject to obligations regarding quality management systems under relevant sectoral Union law should have the possibility to include the elements of the quality management system provided for in this Regulation as part of the existing quality management system provided for in that other sectoral Union law. The complementarity between this Regulation and existing sectoral Union law should also be taken into account in future standardisation activities or guidance adopted by the Commission. Public authorities which put into service high-risk AI systems for their own use may adopt and implement the rules for the quality management system as part of the quality management system adopted at a national or regional level, as appropriate, taking into account the specificities of the sector and the competences and organisation of the public authority concerned. ### Recital 173 — Commission delegated powers to adapt AI rules *Source: AI Act, aiact-rec-173-en, 2024-06-12 — https://overview.legal/posts/94028* In order to ensure that the regulatory framework can be adapted where necessary, the power to adopt acts in accordance with Article 290 TFEU should be delegated to the Commission to amend the conditions under which an AI system is not to be considered to be high-risk, the list of high-risk AI systems, the provisions regarding technical documentation, the content of the EU declaration of conformity the provisions regarding the conformity assessment procedures, the provisions establishing the high-risk AI systems to which the conformity assessment procedure based on assessment of the quality management system and assessment of the technical documentation should apply, the threshold, benchmarks and indicators, including by supplementing those benchmarks and indicators, in the rules for the classification of general-purpose AI models with systemic risk, the criteria for the designation of general-purpose AI models with systemic risk, the technical documentation for providers of general-purpose AI models and the transparency information for providers of general-purpose AI models. It is of particular importance that the Commission carry out appropriate consultations during its preparatory work, including at expert level, and that those consultations be conducted in accordance with the principles laid down in the Interinstitutional Agreement of 13 April 2016 on Better Law-Making (55). In particular, to ensure equal participation in the preparation of delegated acts, the European Parliament and the Council receive all documents at the same time as Member States’ experts, and their experts systematically have access to meetings of Commission expert groups dealing with the preparation of delegated acts. ### Recital 146 — simplified quality management for microenterprises *Source: AI Act, aiact-rec-146-en, 2024-06-12 — https://overview.legal/posts/93974* Moreover, in light of the very small size of some operators and in order to ensure proportionality regarding costs of innovation, it is appropriate to allow microenterprises to fulfil one of the most costly obligations, namely to establish a quality management system, in a simplified manner which would reduce the administrative burden and the costs for those enterprises without affecting the level of protection and the need for compliance with the requirements for high-risk AI systems. The Commission should develop guidelines to specify the elements of the quality management system to be fulfilled in this simplified manner by microenterprises. ### Recital 158 — financial services authorities for AI oversight *Source: AI Act, aiact-rec-158-en, 2024-06-12 — https://overview.legal/posts/93998* Union financial services law includes internal governance and risk-management rules and requirements which are applicable to regulated financial institutions in the course of provision of those services, including when they make use of AI systems. In order to ensure coherent application and enforcement of the obligations under this Regulation and relevant rules and requirements of the Union financial services legal acts, the competent authorities for the supervision and enforcement of those legal acts, in particular competent authorities as defined in Regulation (EU) No 575/2013 of the European Parliament and of the Council (46) and Directives 2008/48/EC (47), 2009/138/EC (48), 2013/36/EU (49), 2014/17/EU (50) and (EU) 2016/97 (51) of the European Parliament and of the Council, should be designated, within their respective competences, as competent authorities for the purpose of supervising the implementation of this Regulation, including for market surveillance activities, as regards AI systems provided or used by regulated and supervised financial institutions unless Member States decide to designate another authority to fulfil these market surveillance tasks. Those competent authorities should have all powers under this Regulation and Regulation (EU) 2019/1020 to enforce the requirements and obligations of this Regulation, including powers to carry our ex post market surveillance activities that can be integrated, as appropriate, into their existing supervisory mechanisms and procedures under the relevant Union financial services law. It is appropriate to envisage that, when acting as market surveillance authorities under this Regulation, the national authorities responsible for the supervision of credit institutions regulated under Directive 2013/36/EU, which are participating in the Single Supervisory Mechanism established by Council Regulation (EU) No 1024/2013 (52), should report, without delay, to the European Central Bank any information identified in the course of their market surveillance activities that may be of potential interest for the European Central Bank’s prudential supervisory tasks as specified in that Regulation. To further enhance the consistency between this Regulation and the rules applicable to credit institutions regulated under Directive 2013/36/EU, it is also appropriate to integrate some of the providers’ procedural obligations in relation to risk management, post marketing monitoring and documentation into the existing obligations and procedures under Directive 2013/36/EU. In order to avoid overlaps, limited derogations should also be envisaged in relation to the quality management system of providers and the monitoring obligation placed on deployers of high-risk AI systems to the extent that these apply to credit institutions regulated by Directive 2013/36/EU. The same regime should apply to insurance and re-insurance undertakings and insurance holding companies under Directive 2009/138/EC and the insurance intermediaries under Directive (EU) 2016/97 and other types of financial institutions subject to requirements regarding internal governance, arrangements or processes established pursuant to the relevant Union financial services law to ensure consistency and equal treatment in the financial sector. ## Literature ### REGULATION OF APPLIED ARTIFICIAL INTELLIGENCE IN BIOMEDICAL ENGINEERING AS A HIGH-RISK ARTIFICIAL INTELLIGENCE SYSTEM IN THE EU AI ACT *Source: AFMN Biomedicine, 2026-07-13 — https://overview.legal/posts/132435 — original: https://doi.org/10.65641/afmnai-2026-075* lt;p style= quot;text-align: justify; quot; gt; lt;span class= quot;a_GcMg font-feature-liga-off font-feature-clig-off font-feature-calt-off text-decoration-none text-strikethrough-none quot; gt;Artificial intelligence (AI) represents a global phenomenon changing all spheres of human life. Biomedical engineering is no exception, as many AI systems are applied to biomedical engineering inventions. The European Union has enacted the new EU AI Act, one of the world amp;rsquo;s first laws on AI. The ### The Magician’s Eye *Source: Journal of Ethics and Emerging Technologies, 2026-07-01 — https://overview.legal/posts/53832 — original: https://doi.org/10.55613/jeet.v36i2.239* David Eliot trained as a card magician before turning to AI research. His Artificially Intelligent sets out to democratise AI for general readers, and on its own terms it succeeds: the history is rich, the writing is clear, and the central argument that AI is socially constructed and democratically redirectable is held with conviction. This review reads the book alongside the regulatory architecture being built on the same theory of agency: the EU AI Act, the harmonised standards beneath it, the ## Related topics - **Provider Obligations for AI Systems** — https://overview.legal/topics/provider-obligations-ai The content specifically addresses obligations imposed on providers of high-risk AI systems, which is a distinct and important category of requirements that des - **AI Risk Assessment** — https://overview.legal/topics/ai-risk-assessment The AI Act employs a risk-based regulatory approach to determine which practices are prohibited, requiring assessment and classification of AI system risks, whi - **AI Value Chain Actors and Roles** — https://overview.legal/topics/ai-value-chain-actors The content focuses on responsibilities distributed across different actors in the AI value chain. 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