Abstract
Despite the promise of AI and IoT, the efforts of many organizations at scaling smart city initiatives fall short. Organizations often start by exploring the potential with a proof-of-concept and a pilot project, with the process later grinding to a halt for various reasons. Pilot purgatory, in which organizations invest in small-scale implementations without them realizing substantial benefits, is given very little attention in the scientific literature relating to the question of why AI and IoT initiatives fail to scale up for smart cities. By combining extensive study of the literature and expert interviews, this research explores the underlying reasons why many smart city initiatives relying on Artificial Intelligence of Things (AIoT) fail to scale up. The findings suggest that a multitude of factors may leave organizations ill prepared for smart city AIoT solutions, and that these tend to multiply when cities lack much-needed resources and capabilities. Yet many organizations tend to overlook the fact that such initiatives require them to pay attention to all aspects of change: strategy, data, people and organization, process, and technology. Furthermore, the research reveals that some factors tend to be more influential in certain stages. Strategic factors tend to be more prominent in the earlier stages, whereas factors relating to people and the organization tend to feature later when organizations roll out solutions. The study also puts forward potential strategies that companies can employ to scale up successfully. Three main strategic themes emerge from the study: proof-of-value, rather than proof-of-concept; treating and managing data as a key asset; and commitment at all levels.
Highlights
Accepted: 4 November 2021The powerful convergence of AI and the Internet of Things (IoT), or Artificial Intelligence of Things, AIoT for short, is no longer on the horizon; it has already arrived.Individually, both AI and IoT are impressive technologies, to say the least
We asked open-ended questions, such as “How would you describe the current state of AIoT?”, “To what extent do you think AIoT initiatives scale up?” or “What holds organizations back from scaling up AIoT initiatives?”, allowing interviewees to express what they see as the most significant factors for scaling
Last but certainly not least, AIoT solutions tend to involve multiple technical components and integration of systems from different areas and organizations involved in smart cities that might not have been designed to be connected in the first place
Summary
The powerful convergence of AI and the Internet of Things (IoT), or Artificial Intelligence of Things, AIoT for short, is no longer on the horizon; it has already arrived. Sustainability 2021, 13, 12295 different and heterogeneous data sources results in value Speaking, fa such as data quality and coverage, compatibility and interoperability, external data, i mation technologies and software, analyticalcooperation, techniques, culture, cooperation, culture, pr information technologies and software, analytical techniques, privacy and confidentiality, and public procurement enable or constrain organizations’. We define scaling as “the industrialization of IoT-enabled AI solutions tions whereby, following the proof-of-concept and the pilot experiments, whereby, following the proof-of-concept and the pilot experiments, these technologies arethese tech gies are routinized practices on a of large [9]. As the challenges of AIoT become increas more relevant tomore smartrelevant cities, further research needed to investigate factors that influence to smart cities, isfurther research is needed to investigate factors that scaling up.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call