Integration of artificial intelligence of things (AIoT) in the public sector: drivers, barriers and future research agenda

Abstract

Purpose With the development of information technology (IT), governments around the globe are using state-of-the-art IT interfaces to implement the so-called 3E’s in public service delivery, that is, economy, efficiency and effectiveness. Two of these IT interfaces relate to Artificial Intelligence (AI) and Internet of Things (IoT). While AI focuses on providing a “human” garb for computing devices, thereby making them “intelligent” devices, IoT relies on interfaces between sensors and the environment to make “intelligent” decisions. Recently, the convergence of AI and IoT – also referred to as Artificial Intelligence of Things (AIoT) – is seen as a real opportunity to refurbish the public service delivery formats. However, there is limited understanding as to how AIoT could contribute to the improvisation of public service delivery. This study aims to create a modular framework for AIoT in addition to highlighting the drivers and barriers for its integration in the public sector. Design/methodology/approach This descriptive-explanatory study takes a qualitative approach. It entails a thorough examination of the drivers and barriers of integrating AI and IoT in the public sector. A review of literature has led to the development of a conceptual framework outlining the various factors that contribute to creating public value. Findings Value creation occurs when AI and IoT coalesce in the public service delivery mechanisms. Originality/value AIoT is a cutting-edge technology revolutionizing health care, agriculture, infrastructure and all other industrial domains. This study adds to the growing body of knowledge on the public sector's use of AI and IoT. Understanding these disruptive technologies is critical to formulating policies and regulations that can maximize the potential benefits for the public-sector organizations.

Organisational Enablers of Artificial Intelligence Adoption in Public Institutions: A Systematic Literature Review

“…The following tables include the facets within the five elements of the Despite the technology (AI) and its adoption being discussed in the study, people (i.e., employees -public managers, public servants, external AI experts) must be put first while discussing ICT novelties/transformations that are being introduced into public organisations. Several authors (Alshahrani et al, 2022;Campion et al, 2022;Chen et al, 2023;Criado et al, 2022;Effendi and Pribadi, 2021;Ishengoma et al, 2022;Mikalef et al, 2019;Mikalef et al, 2022;Neumann et al, 2022;Noymanee et al, 2022;Ojo et al, 2019;Peretz-Andersson et al, 2021;Plantinga, 2022;Schaefer et al, 2021;van Noordt and Misuraca, 2020a;van Noordt and Misuraca, 2020b;Wirtz and Müller, 2019) studied the enablers of successful AI adoption related to the human factor. Many of these authors stress that, first, top management's understanding and positive perception of the application and value of AI are the key enablers of AI adoption, followed by top management's support in terms of developing a strategy, providing resources, and change management activities.…”

“…(2) inter-and cross-organisational alignment between project interests and expectations surrounding the data sharing; and (3) engagement within the organisational hierarchy, leading to the unification of expectations on the top and bottom levels of the organisation -Internal collaboration: data and knowledge from different departments along with a common understanding of the aims, benefits and goals of AI projects -Clarifying roles and responsibilities within the collaboration (e.g., by appointing champions) -Project-oriented measures, agile project management Alshahrani et al, 2022, Campion et al, 2022, Chen et al, 2019, Chen et al, 2023, Ishengoma et al, 2022, Mikalef et al, 2019, Mikalef et al, 2022, Plantinga, 2022, Schaefer et al, 2021, van Noordt and Misuraca, 2020a, van Noordt and Misuraca, 2020b Source: authors' elaboration.…”

“…While examining the changes in organisational structure that enable AI adoption, researchers (Alshahrani et al, 2022;Campion et al, 2022;Chen et al, 2019;Chen et al, 2023;Ishengoma et al, 2022;Mikalef et al, 2019;Mikalef et al, 2022;Plantinga, 2022;Schaefer et al, 2021;van Noordt and Misuraca, 2020a;van Noordt and Misuraca, 2020b) detected the following sets of enablers: (1) introducing new organisational structures, e.g., projects along with agile management approaches; (2) inter-organisational collaboration, e.g., public-private partnerships, joint projects etc. ; (3) intra-governmental digital service units that attract talents and accelerate implementation; (4) knowledge transfer and data sharing between and within organisations (on all hierarchical levels); and (5) intra-organisational collaboration -between the organisational units (departments) -to ensure a common understanding of the goals, purpose and benefits of the AI adoption.…”

“…Accordingly, the key enablers concerning the organisational structure are the collaboration between and within public institutions and other stakeholders, reorganisations, and the introduction of agile management. financial and other incentives along with a push from higher levels -Innovations that all stakeholders perceive as 'value adding' -Innovations regarded as easy to use and to experiment with -Innovations compatible with the organisational values -IT managers guided by public values will implement more ethical AI technologies -IT managers developing an organisation-wide readiness perspective, not merely infrastructure investments and pools of data -Senior management allowing experimenting with new ideas and technologies -Methods of agile project management and a culture that allows a degree of failure -Individual motivation -identification of employees interested in AI and thinking flexibly and innovatively Alshahrani et al, 2022, Campion et al, 2022, Criado et al, 2022, Ishengoma et al, 2022, Mikalef et al, 2022, Neumann et al, 2022, Ojo et al, 2019, Plantinga, 2022, van Noordt and Misuraca, 2020a, van Noordt and Misuraca, 2020b.…”

“…Moreover, the unique purpose and context of the public sector create distinct challenges that must be addressed. To understand the subject of AI adoption in the public sector, several detailed literature reviews have already been performed (e.g., de Sousa et al, 2019;Dwivedi et al, 2019;Haug et al, 2023;Ishengoma et al, 2022;Mergel et al, 2023;Pencheva et al, 2020;van Noordt and Misuraca, 2022;Zuiderwijk et al, 2022) in an effort to understand the dimensions of AI in public institutions and its associated challenges, opportunities, and agenda for research, practice and policy. Mergel et al (2023) and Haug et al (2023) strongly highlight the need to address the theoretical research gap with regard to AI adoption in the public sector.…”