The powerful convergence of AI and the Internet of Things (IoT), or Artificial Intelligence of Things (AIoT) in short, is no longer on the horizon; it has already arrived. By combining AI’s ability to quickly wring insights from data and the ever-growing network of connected devices and systems that generate data, companies can avoid unplanned downtime, increase operating efficiency, and enable new and enhanced products and services. Put simply, IoT acts as a digital nervous system in which AI is the brain that drives decisions. Despite the promise of AI and IoT, many organizations’ efforts to scale up seems to fall short. Companies generally start by testing the waters with a proof-of-concept and pilot project, often later grinding to halt with no clearly defined strategy to scale for a variety of reasons. By combining extensive literature study of more than 200 academic papers, 11 expert interviews, and an exploratory case study, the research explores the underlying reason why most AIoT initiatives fail to scale up by investigating four main aspects, namely the barriers to scale up, the cross-industry differences, the temporal variances, and the potential strategies for companies to escape from the “pilot purgatory.” The study reaches to an exhaustive list of 22 factors, classifying the factors into five main categories: strategy (AI/I4.0 strategy, competing investment opportunities, and technology partners), data (data quality and availability, data governance, data security and privacy, data analytics capabilities), people (top management support, user support/resistance, technology sponsors/champions, skilled staff and expertise, technology knowledge, organizational culture, organizational agility, organizational structure, and alignment between departments), process (perceived business benefits, business models/use cases and operating models), and technology (ICT capabilities and infrastructure, integration with other systems, and technology characteristics). The study also reveals that some factors tend to be more influential in certain stages. While strategic factors tend to be more prominent in the earlier stages, people and organizational factors tend to arise later when organizations roll out solutions. The research also confirmed the dominant role of top management and technology sponsors in igniting as well as leading the scale-up process. Albeit limited, two main variables stood out as cross-industry differences: regulations and digital maturity of the industry. In the study, three big points emerged as the potential strategies for companies to eliminate, or at least mitigate, the barriers. These are proof-of-value, not proof of concept, treating and managing data as a key business asset, and top-down and bottom-up support. As opposed to proving the feasibility of the technology, as many have before, organizations can start by assessing the technology from the business lens way to identify high-value use cases and prove the real value -if there is one. The research shows that companies require to pay utmost attention to collecting, structuring, and managing their data, even before initiating AIoT projects. The study discovers that the support from all levels -executive sponsorship from the top, and ensuring user acceptance and upskilling employees on the frontline- is essential to scale up, as it likely is for most digital transformations.