Business model canvas to create and capture AI-enabled public value
Open accessAbstract
The compatibility between the business model and AI-enabled value creation is paramount for the sustainability of organizations. The public sector lags the private sector in the race to AI readiness and adoption. Although the concept of the business model for the public sector has previously been discussed, we found a lack of evidence for the process of adaption of the business model as a value creation and capture tool from commercial motives to public value motives. This paper adapts the conventional business model canvas for the public sector as it pertains to the design and development of AI systems. Employing a design-science research approach, we postulate five design principles that public agencies must follow to design and deploy AI-enabled public services.
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Co-creation of AI solutions with stakeholders provides varied viewpoints and helps develop a clear definition of the problem.
“Co-creation of AI solutions with stakeholders provides varied viewpoints and helps develop a clear definition of the problem”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
Section 5.5 (page not available in our copy) advocates co-creating AI-enabled public services with stakeholders and says citizens can suggest novel solutions. Section 6.1 identifies several types of stakeholders, but neither section says that co-creation helps develop a clear definition of the problem; the varied-viewpoints benefit is also not stated explicitly. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThe public value of duty is characterised by using AI in facilitating the democratic will by enabling citizen engagement and participation at scale.
“The public value of duty is characterised by using AI in facilitating the democratic will by enabling citizen engagement and participation at scale”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
Section 5.5, “Co-creation of public value,” supports citizen engagement in designing AI-enabled public services; Section 6.5, “Citizen Relationships,” also discusses channels for citizen participation in service co-creation. Neither passage characterizes this as a “public value of duty” or says AI facilitates democratic will or participation at scale. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOther negative implications include the social costs of job losses, re-skilling, and workforce displacement.
“Other negative implications include the social costs of job losses, re-skilling, and workforce displacement”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
Section 6.10 (Social Cost; page unavailable) identifies job loss as a social cost and says AI may reduce the number of people needed for tasks, supporting the job-loss and workforce-displacement parts. It does not identify re-skilling as a social cost; Section 6.8 (Economic Cost; page unavailable) instead lists AI skill-building programs as an economic cost. (LLM, conf=0.88)
Experimental — accuracy not yet measuredAutomation of application processes enables instant approval and feedback improving quality and service time.
“Automation of application processes enables instant approval and feedback (Androutsopoulou et al., 2019;Fatima et al., 2021) improving quality and service time.”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
Section 5.1 says public-agency chatbots provide instant responses, and Section 2 says AI can improve the efficiency and quality of public services. Neither passage says that automating application processes enables instant approval; the feedback discussed in Section 6.7 is citizens’ feedback on services, not immediate feedback on an application. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis will be detrimental to the well-being and justice of citizens and public administration employees.
“This will be detrimental to the well-being and justice of citizens and public administration employees”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
Section 6.10 (Social Cost; page unavailable) identifies job loss, a digital divide, and socioeconomic disparity as potential costs of AI-enabled public services. Section 5.4 describes discriminatory outcomes that can harm citizens. These passages support risks to citizens and workers, but do not establish the categorical claim that both the well-being and justice of citizens and public administration employees will be harmed. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Pilot testing and experimentation are considered critical for AI applications in public administration to identify and mitigate risks of failure which may prove disastrous in eroding citizen trust.
“Pilot testing and experimentation are considered critical for AI applications in public administration to identify and mitigate risks of failure which may prove disastrous in eroding citizen trust”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
The socially-oriented ethos of protecting citizens from algorithmic harm might conflict with the temptations of efficiency and cost savings.
“The socially-oriented ethos of protecting citizens from algorithmic harm might conflict with the temptations of efficiency and cost savings”
From AI adoption and diffusion in public administration: A systematic literature review and future research agenda (2023) · cites this paper as Fatima et al., 2021
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Cites
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Cited by
Sorted by citations
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“The socially-oriented ethos of protecting citizens from algorithmic harm might conflict with the temptations of efficiency and cost savings (Fatima et al., 2021; H ¨ ochtl et al., 2016; Kuziemski & Misuraca, 2020; Marri et al., 2019; Ojo, 2019; Rogge et al., 2017; Schedler et al., 2019; Young et…”
Influential
- Authors
- Fatima, S., Desouza, K., Buck, C., & Fielt, E.
- Affiliations
- Queensland University of Technology (AU)
- Venue
- Proceedings of the Annual Hawaii International Conference on System Sciences
- Published
- 2021
- ISSN
- 1530-1605 (ISSN-L)
- 2572-6862
- OpenAlex
- W3127185804
- Semantic Scholar
- 7184054c041fa6813b5b44eed1341e505911c417
- Cited by
- 8
- References
- 30
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2022: 3
- FWCI
- 6.31
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
- ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
- Journal of the Association for Information Systems (submittedVersion)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Innovative Approaches in Technology and Social Development
- Social Sciences › Public Administration › Public Policy and Administration Research
- Keywords
- business model canvas, public value, public sector AI adoption, value capture, business model adaptation, public agencies, AI system design, design principles
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