Revisiting automated project management in the digital age – a survey of AI approaches

Auth, Gunnar; Jokisch, Oliver; Dürk, Christian

doi:10.36965/ojakm.2019.7(1)27-39

Cited by 44 publications

(28 citation statements)

References 15 publications

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“…With regard to the effective implementation of projects, the so-called rational agents suggest a special potential: their extended capabilities enable them to strive for the best result in their actions or the most valued result under uncertainty (Auth et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the use of artificial intelligence in the management of Industry 4.0 projects. The perspective of Polish industry

Wachnik

2022

Production Engineering Archives

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Artificial Intelligence (AI) supports project management in industry projects increasingly more often. The article documents research results regarding the use of AI in Industry 4.0 projects. The aim of the article is to define the range of AI use in Industry 4.0 projects throughout their entire life cycle and to indentify the main stages of AI development in project management. Additionally, the article indicates the main barriers to AI use in project management identified in the studied projects. In order to identify and systemise the range of AI use, the Conceptual Framework of Using AI in Project Management presented in literature is applied. Research results indicate that we are in the early stages of AI use in projects. The studied projects use AI mainly in project administration, i.e. to complete the following tasks: simple automation of routine activities, support, and, to a very limited extent, in the area of project management, i.e. in identifying anomalies and predicting the phenomena where the anomalies will occur. The research uses the case study method, where four projects were studied: an ERP system upgrade, an implementation of an IT system supporting high-bay warehouse management, an implementation of an IoT as a data-collection sensor platform and an E-learning platform implementation. The study was conducted between 2019 and 2021, and covered the entire project life cycle, including its three stages, i.e. preparation, implementation and operation.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of the use of artificial intelligence in the management of Industry 4.0 projects. The perspective of Polish industry

Wachnik

2022

Production Engineering Archives

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“…Existing research is slowly having an impact on product offerings. Today, most AI applications in PM are chatbots [19]. Here, the use of AI is at the input and output interface, but not in the PM task itself.…”

Section: Artificial Intelligence In Project Managementmentioning

confidence: 99%

“…In addition to the AI functions, different AI solution types exist. We refined the solution type approaches of Auth et al [19] and Lahmann et al [51] and distinguish four AI solution types depending on the role, extent, and value of AI usage: rule-based solutions, AI-enabled solutions, AI-based solutions, and full AI solutions. Rule-based solutions do not possess any AI component but rely on common rule-based programming.…”

Section: Understandingmentioning

confidence: 99%

Developing Purposeful AI Use Cases – A Structured Method and Its Application in Project Management

Hofmann¹,

Jöhnk²,

Protschky³

et al. 2020

WI2020 Zentrale Tracks

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An appropriate problem-solution-fit is essential to develop purposeful artificial intelligence (AI) applications. However, in domains with an unintuitive problem-solution-fit, such as project management (PM), organizations require methodological guidance. Hence, we propose a five-step method to develop organization-specific AI use cases: First, companies must consider the context factors technology, organization (in particular data and application domain), and environment. Second, companies must identify existing domain problems and AI solutions. Third, our method facilitates abstraction to understand the underlying nature of the identified problems and AI solutions. Fourth, our problem-solutionmatrix assists companies to match AI functions with the domain context. Fifth, companies derive necessary implications for the subsequent use case implementation. To construct and evaluate our method, we followed the design science research paradigm complemented by situational method engineering and based on 14 interviews. Our method addresses a relevant practical problem and contributes to identifying purposeful AI use cases in unintuitive application domains.

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“…A specific requirement in some cases is to manage corruption and fraud across the project cycle (Bach et al, 2018). Here, the report of the OECD (2019) on the use of analytics is path-breaking, not in terms of its technical approach but in terms of its recommendations, which relate primarily to countries where corruption in public projects is high, though its conclusions could apply to all large public projects.…”

Section: Data-driven Project Managementmentioning

confidence: 99%

“…The main research implication from all the above is that the superproject management problem is just one example of a more general problem, which has already been identified (Stone et al , 2019), but which need to be dealt with using learning from a much wider range of management disciplines, particularly as change management (Hornstein, 2015) and strategic decision making processes (Shepherd and Rudd, 2014), but also other areas such as leadership (Tyssen et al , 2014; Coleman and Bourne, 2018), big data (Merendino et al , 2018), cognitive bias (Montibeller and Von Winterfeldt, 2015), artificial intelligence (Auth et al , 2019, Prieto, 2019), military strategy (Payne, 2018) and knowledge management (Ekambaram et al , 2018). In some respect, the superproject literature is discovering phenomena that has been widely researched and documented in other literature, but in an interesting combination, which does need stronger integration.…”

Section: The Conclusion and Implications For Information Management In Superproject Controlmentioning

confidence: 99%

Controlling superprojects – information management requirements

Parnell¹,

Stone

Aravopoulou

2020

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Purpose This paper aims to explore the problems of managing superprojects and identifies how a different approach to controlling them can reduce the incidence of cost and time overruns and benefit shortfalls. Design/methodology/approach Literature review accompanied by conceptual analysis. Findings Project cost and timing overruns and benefit shortfalls are very frequent in superprojects. These problems can be ascribed partly to the way in which they are planned is not taken into account in designing and implementing control systems, particularly the governance processes and the information they have available. Practical implications This paper has serious implications for those designing control processes, governance and information management for superprojects. It suggests that if a new approach is taken, fewer superprojects will suffer from cost overruns and benefit shortfalls because remedial actions will be taken earlier for projects, which are experiencing problems, while learning will be fed back to those planning new projects. Social implications There will be saving of public money and reduced deferment of benefits that normally result from failed or delayed projects and reduced allocation of large incremental budgets dedicated to resolving problems. Originality/value The taxonomy of different types of superprojects is original, as is the idea of ambidextrous control, and the diagnosis of failure reasons lying in the nature of control and governance processes, and the lack of relevant information available during the control process.

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Revisiting automated project management in the digital age – a survey of AI approaches

Cited by 44 publications

References 15 publications

Analysis of the use of artificial intelligence in the management of Industry 4.0 projects. The perspective of Polish industry

Analysis of the use of artificial intelligence in the management of Industry 4.0 projects. The perspective of Polish industry

Developing Purposeful AI Use Cases – A Structured Method and Its Application in Project Management

Controlling superprojects – information management requirements

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