Over the past two decades, the rapid advances in computing, networking and big data technologies have fueled digital transformation across business and society. This has led to the seamless integration of smart devices, cloud services, telecommunications and data analytics into daily life, significantly enhancing convenience, connectivity and productivity and fundamentally reshaping how we work, learn, communicate and access information (Qiu, 2014). For organizations, businesses, societies and even individuals, embracing digital transformation is no longer optional but imperative (Qiu, 2022).
More recently, artificial intelligence (AI) innovations, including generative models and agentic AI, are further accelerating this transformation. They are enabling hyper-personalized services, autonomous decision-making systems and hyperautomation (Darwish, 2024) across business and society, promising to redefine efficiency, innovation and human-machine collaboration in both workplaces and daily life. However, despite substantial investments in AI, particularly in generative AI and large language models (LLMs), many business leaders remain skeptical about its impact on enterprise productivity. The return on investment has often fallen short of expectations due to factors such as high implementation costs, talent shortages, data quality issues and challenges in scaling AI solutions (Raitano, 2025). In other words, a noticeable discrepancy is created between AI’s touted potential and its actual implementation (Mir, 2025). We believe that this gap highlights a critical challenge that agentic AI is well-positioned to help address.
Agentic AI represents a significant leap in AI’s evolution, transitioning from systems that merely analyze data to those capable of autonomous action and decision-making, focusing on AI scaling, process integration and productivity gains (Acharya, Kuppan, & Divya, 2025). The rise of agentic AI heralds a transformative era in digital transformation, empowering autonomous systems to perceive, reason, act and learn independently. This will unlock significant advancements in efficiency, personalized experiences and complex problem-solving, with implications for the integration of sophisticated technologies like humanoid robots.
Agentic AI is characterized by its capacity for autonomous operation, making decisions and performing tasks without direct human intervention. Unlike generative AI, which focuses on content creation, agentic AI applies generated outputs to achieve specific objectives, marking a shift towards proactive AI that autonomously acts to fulfill organizational goals. Acharya et al. (2025) highlight several core characteristics that distinguish agentic AI, including autonomy and goal complexity, proactiveness and operational adaptability and reasoning and learning.
More specifically, agentic AI differentiates itself through the implementation of autonomous agents with advanced goal-directed behavior. These agents operate beyond the confines of pre-defined instructions, dynamically decomposing complex tasks into sub-goals and navigating through multi-step processes to achieve overarching objectives. They exhibit robust proactiveness, anticipating dynamic shifts in operational environments and proactively mitigating potential impediments. The foundation of these capabilities lies in their sophisticated reasoning and learning frameworks, enabling them to contextualize information, evaluate probabilistic outcomes and adapt strategies in real-time. This confluence of autonomy, proactiveness and advanced reasoning positions agentic AI as a disruptive technology, particularly in domains demanding independent, goal-driven and context-aware systems (Acharya et al., 2025).
Therefore, agentic AI integration is poised to revolutionize business operations by transcending traditional automation. It automates complex, multi-step tasks with high intelligence and adaptability, dynamically adjusting to real-time data and enabling autonomous decision-making. This shift enhances support for unstructured and dynamic business processes across industries, from supply chain management and finance to logistics, thereby improving efficiency and customer experience. Furthermore, agentic AI is set to reshape society by transforming education, healthcare, governance and everyday interactions. It provides solutions for critical societal challenges, including climate change, resource management and accessibility.
While agentic AI’s transformative potential is substantial, it’s imperative to acknowledge the ethical challenges it presents, such as bias, accountability, job displacement and concerns regarding transparency, privacy, and security. Addressing these challenges is crucial for responsible innovation and ensuring that agentic AI integration benefits both productivity and quality of life.
In conclusion, digital transformation, driven by technologies like AI, robotic process automation (RPA) and the Internet of Things (IoT), has been instrumental in streamlining business operations and public services and reshaping societies globally, leading to significant gains in productivity, efficiency and agility. Agentic AI integration amplifies this transformation, offering the capacity to deliver the productivity gains that many businesses have yet to achieve. By prioritizing autonomous operation, proactive reasoning and continuous learning, agentic AI is indeed set to revolutionize digital transformation across business and society, fostering efficiency, personalization and solutions to pressing global challenges.
