A business model template for AI solutions

Metelskaia, Iuliia; Ignatyeva, Olga; Denef, Sebastian; Samsonowa, Tatjana

doi:10.1145/3233740.3233750

Cited by 19 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial intelligence is accepting the phenomenal changes of the business framework and transforming the business culture how to work in the digital era (Davenport, 2018, El-Najdawi & Stylianou, 1993, Hamscher, 1994, Metelskaia, Ignatyeva, Denef, & Samsonowa, 2018. Basically, it is not just changing the culture of business but also fundamentally renovating the conventional thinking and significance of alliance and collaboration, vigorous competition, and digital and enriched innovation (El-Najdawi & Stylianou, 1993;Hagras, 2018;Niu, Li, Tang, Zhou, & Shi, 2011;Schulte & Liu, 2017).…”

Section: Business Model Transformationmentioning

confidence: 99%

AI business model: an integrative business approach

Mishra

Tripathi

2021

J Innov Entrep

View full text Add to dashboard Cite

Artificial intelligence is the ecosphere’s prevalent and most comprehensive general acquaintance common-sense cognitive engine. The artificial intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on the top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in the digital data fluid through the coordination, fueling business enhancements over phases. In this business model, the business will safekeep a recurrent subscription. This paper endeavors to emphasize on the preventative side of the use of AI and machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics. We acme the strategic implications and innovations with analytics. We explore the derivations of data-driven insights, models, and visualizations.

show abstract

Section: Business Model Transformationmentioning

confidence: 99%

AI business model: an integrative business approach

Mishra

Tripathi

2021

J Innov Entrep

View full text Add to dashboard Cite

show abstract

“…Hailed as the next big wave in computing, AIaaS is currently offered by major vendors including Amazon Web Services, Microsoft Azure, Google Cloud, IBM Cloud, Oracle and SalesForce. AIaaS is also the key business model for a host of start-ups and small-medium enterprises (Metelskaia et al., 2018).…”

Section: Ai-as-a-servicementioning

confidence: 99%

Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

Newlands

2021

Big Data & Society

View full text Add to dashboard Cite

Artificial Intelligence-as-a-Service (AIaaS) empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations.

show abstract

“…Comprehensive AI/Industry 4.0 strategy [16][17][18]29,33,41,42,[48][49][50] Perceived business benefits [33,42,47,50,51] Top management support [16][17][18]34,41,42,47] Business models/use cases suitable for AI/I4.0 [18,34,40,47,48,50,52] Technology knowledge [13,16,17,34,41,42,46,49,53] Technology partners/vendors [16,33,34,37,42] Organizational culture [16][17][18]29,33,34,41,…”

Section: Factor Sourcementioning

confidence: 99%

The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives

2021

View full text Add to dashboard Cite

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.

show abstract

A business model template for AI solutions

Cited by 19 publications

References 11 publications

AI business model: an integrative business approach

AI business model: an integrative business approach

Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

The Giant Leap for Smart Cities: Scaling Up Smart City Artificial Intelligence of Things (AIoT) Initiatives

Contact Info

Product

Resources

About