Responsible AI in Business

TL;DR

This paper presents a modular framework for Responsible AI in business, targeting SMEs, by synthesizing four pillars—EU AI Act compliance, Explainable AI, Green AI, and Local models—to guide governance, risk management, and sustainable deployment. It articulates how to classify AI use cases, ensure transparency and accountability, optimize resource use through reuse and compression, and deploy data-sensitive systems on-premise or at the edge. Practical guidance covers risk assessments, documentation, AI literacy, and an implementation roadmap that aligns with GDPR and data-protection requirements. The work aims to enable legally compliant, comprehensible, and environmentally sustainable AI adoption that preserves strategic independence and reduces liability and reputational risk.

Abstract

Artificial intelligence (AI) and Machine Learning (ML) have moved from research and pilot projects into everyday business operations, with generative AI accelerating adoption across processes, products, and services. This paper introduces the concept of Responsible AI for organizational practice, with a particular focus on small and medium-sized enterprises. It structures Responsible AI along four focal areas that are central for introducing and operating AI systems in a legally compliant, comprehensible, sustainable, and data-sovereign manner. First, it discusses the EU AI Act as a risk-based regulatory framework, including the distinction between provider and deployer roles and the resulting obligations such as risk assessment, documentation, transparency requirements, and AI literacy measures. Second, it addresses Explainable AI as a basis for transparency and trust, clarifying key notions such as transparency, interpretability, and explainability and summarizing practical approaches to make model behavior and decisions more understandable. Third, it covers Green AI, emphasizing that AI systems should be evaluated not only by performance but also by energy and resource consumption, and outlines levers such as model reuse, resource-efficient adaptation, continuous learning, model compression, and monitoring. Fourth, it examines local models (on-premise and edge) as an operating option that supports data protection, control, low latency, and strategic independence, including domain adaptation via fine-tuning and retrieval-augmented generation. The paper concludes with a consolidated set of next steps for establishing governance, documentation, secure operation, sustainability considerations, and an implementation roadmap.