Enterprise intelligence 5.0: Human + AI + Analytics

Abstract

Enterprise Intelligence 5.0 represents a new organizational intelligence paradigm that integrates human expertise, artificial intelligence, advanced analytics, organizational knowledge, and intelligent decision-support technologies into a unified enterprise environment. The concept extends traditional Business Intelligence by shifting organizational intelligence from retrospective reporting toward predictive, adaptive, collaborative, and human-cantered decision-making. The development of Industry 5.0 has emphasized the importance of human-centricity, sustainability, resilience, and collaboration between humans and intelligent technologies, providing an important foundation for Enterprise Intelligence 5.0 (Breque et al., 2021). The human-cantered perspective is further reinforced by research describing Industry 5.0 as an environment in which advanced technologies augment human capabilities rather than simply replace human activities (Nahavandi, 2019). At the same time, the evolution from Industry 4.0 toward Industry 5.0 demonstrates increasing attention to the integration of technological intelligence with human values, creativity, and decision-making (Xu et al., 2018). Enterprise Intelligence 5.0 combines Business Intelligence, big data analytics, machine learning, deep learning, generative artificial intelligence, predictive analytics, explainable AI, and human-AI collaboration. Traditional Business Intelligence primarily transforms organizational data into reports and analytical information, whereas advanced enterprise intelligence seeks to transform data into predictive insights, recommendations, and coordinated decisions. Business Intelligence and analytics have evolved significantly as organizations increasingly use large-scale data and analytical techniques to improve decision-making and organizational performance (Chen et al., 2012). The strategic importance of analytics has also been established through research demonstrating how analytical capabilities can support competitive advantage and organizational decision-making (Davenport & Harris, 2007). The proposed Enterprise Intelligence 5.0 framework places human intelligence at the center of AI-enabled analytics. Human-AI collaboration is particularly important because machines can process large volumes of information, identify patterns, and generate predictions, while humans contribute contextual knowledge, creativity, ethical reasoning, strategic understanding, and accountability. Human-machine collaboration has been identified as a fundamental characteristic of Industry 5.0 environments (Longo et al., 2020), while human-robot collaboration research demonstrates how complementary human and technological capabilities can create new organizational possibilities (Demir et al., 2019). The concept of Hybrid Intelligence further explains how human and artificial intelligence can be combined to achieve outcomes that are difficult to achieve through either capability independently (Dellermann et al., 2019). Enterprise Intelligence 5.0 also requires appropriate organizational structures for distributing decision authority between humans and AI systems. Human-AI collaborative decision-making should therefore be treated as an organizational design challenge involving decision rights, responsibilities, information flows, and accountability (Puranam, 2021). The development of hybrid intelligence systems similarly requires mechanisms that support continuous interaction and learning between humans and intelligent technologies (Dellermann et al., 2021). Recent Industry 5.0 research additionally emphasizes knowledge integration and human-AI collaboration through intelligent architectures and knowledge-based technologies (Rožanec et al., 2022; Krause et al., 2024). This research proposes an integrated Enterprise Intelligence 5.0 framework based on five interconnected capabilities: enterprise data management, advanced analytics, artificial intelligence, human intelligence, and collaborative decision-making. The framework incorporates explainable AI, responsible governance, organizational learning, and continuous feedback to ensure that AI-generated insights remain understandable and actionable. Recent developments in agentic AI further indicate that intelligent systems may increasingly act as strategic cognitive partners within organizations, creating new opportunities as well as governance challenges (Ahmad Chardhiwal & Carbon, 2026). The proposed framework therefore seeks to establish a balanced relationship between automation and human judgment, enabling enterprises to become more adaptive, intelligent, resilient, and capable of achieving sustainable business value.