Content uploaded by Gunnar Auth
All content in this area was uploaded by Gunnar Auth on Jun 11, 2019
Content may be subject to copyright.
Revisiting automated project management in the
digital age – a survey of AI approaches
Gunnar Auth, Leipzig University of Telecommunications, Germany, email@example.com
Oliver Jokisch, Leipzig University of Telecommunications, Germany, firstname.lastname@example.org
Christian Dürk, Corivus AG, Germany, email@example.com
In this decade, remarkable progress has been made in the field of artificial intelligence (AI).
Inspired by well-known services of cognitive assistance systems such as IBM Watson, Apple's
Siri or Google Duplex, AI concepts and algorithms are widely discussed regarding their
automation potentials in business, politics and society. At first glance, project management (PM)
seems to be less suitable for automation due to the inherent uniqueness of projects by definition.
However, AI is also creating new application possibilities in the PM area, which will be
explored in this contribution by involving an extensive literature review as well as real-world
examples. The objective of this article is to provide a current overview of AI approaches and
available tools that can be used for automating tasks in business project management.
Keywords: Artificial intelligence, project management, automation, machine learning, bot.
The recent rise in expectations of Artificial Intelligence (AI) performance in automating complex
activities (e.g., PwC, 2018a) is also reflected in the discussion on the future of project
management (PM). In addition to the various possible uses of AI methods in PM such as Support
Vector Machines (Wauters & Vanhoucke, 2014) or Predictive Analytics (Fauser,
Schmidthuysen, & Scheffold, 2015), the discussion also revolves around the question of whether
or when the human project manager will be replaceable by AI (e.g., Bailey, 2017; Burger, 2017;
& PwC, 2018b). Particularly optimistic authors are already predicting a revolution in PM, driven
by AI (e.g., Birch, 2018; Branscombe, 2018).
As a generic term Automated Project Management (APM) summarizes all approaches for the
most complete automation of PM tasks and activities. The use of the term can already be found
in the early 1980s (Smith & Mills, 1983). From the available literature, we identified two
different meanings of APM: as part of the related term Automated Project Management System
(APMS) it focuses on special software systems supporting project scheduling and controlling in
terms of time, resources and cost (Nicols, 1986). A second, narrower meaning of APM can be
located in software engineering where it describes the automation of software development tasks,
which are typically organized as a software project (Campbell & Terwilliger, 1986). In parallel,
expert systems had evolved and become popular, leading to a growing supply of commercial
software solutions (Liebowitz, 1997). Nevertheless, APMS were not designed as expert systems.
The use of AI was not a central characteristic of early APMS designs. Still, the discussion of AI
potentials for project management also started around that time (e.g., Hosley, 1987) and has
recently become vital again (e.g., Wang, 2019).
The high expectations of AI technology in earlier times, which can be observed in a similar way
today (see Gartner, 2017; PwC, 2018a), lead to ambitious development plans for knowledge-
based decision support along the entire project lifecycle (Hosley, 1987; Levitt & Kunz, 1987).
However, the so-called AI winter (Russel & Norvig, 2010) made such early approaches
disappear from practical use a little later due to unfulfilled expectations. With the recently
rekindled AI enthusiasm, the term APM has been rediscovered and loaded with a tighter
relationship to AI (Jordan, 2018). The latest advances in AI development increasingly pose the
question of substitutability of the human project leader (Bailey, 2017; Pielmeier & Lommel,
2017; PwC, 2018b). With the extended possibilities of AI-based automation through novel
procedures and extensive data availability (Bitkom & DFKI, 2017), the question again arises in
project management, which potentials for automation can be realized in view of the current state
of development and which future development trends are already emerging. For this purpose,
new term creations such as Data-driven Project Management, Predictive Project Analytics or
Project Management Bot need to be clarified and evaluated.
This article contributes to the discussion of using AI for automation in project management from
an application-oriented perspective. The objective of the presented survey is to provide a current
overview of AI approaches and available real-world applications that can be used for automating
tasks in business project management. In order to understand and to conceptualize the role of AI
in project management, we started our research with a literature review focused on research
outcomes and applications following Brocke et al. (2009). The search engines and electronic
databases we used included Google Scholar, SpringerLink, IEEE Xplore, and ReserachGate.
From the review results we defined a conceptual framework for AI applications in PM, which is
presented in the next section. The data for the application-oriented part of our study is mainly
based on vendor-side and third-party information resulting from a second search process based
on the conceptual framework and using Google Web search as well as the academic search tools
mentioned before. In subsequent section, we summarize the search results in an overview of
current AI applications in PM structured into the three main categories 1) Data-driven project
management, 2) AI platforms for PM, and 3) Project management bots. With the last section we
conclude the paper.
From Process to Project Automation with AI
In the era of digitalization with its transformation processes in business and society, driven by
rapid developments and high processing speed in information and communication technology
(ICT), the feasibility limits of automating human tasks seem to soar. This was impressively
demonstrated 2016 by Google’s software AlphaGo, which won four of five matches against the
18-time world champion of the board game Go, Lee Sedol. Such an AI performance was not
expected before, due to the complexity of Go, although already in 1997, IBM’s super computer
DeepBlue had beaten the world champion of Chess, Garry Kasparov. In 2018, the system Google
Duplex was presented, which can automatically make appointments via telephone with human
agents using natural language. In the given examples, the persons did not even seem to notice
that they were talking to a machine (Leviathan & Matias, 2018).
Requirements of Progressive Automation
Automation can be defined as the technology by which a process or procedure is performed with
a minimum of human assistance (e.g., Groover, 2008). For a long time, the according technical
system could only be constructed as a finite state machine. Program-controlled automation
technology, such as looms with punch card control, considerably expanded the automation
capability of processes and tasks at the beginning of the 19th century. Throughout time, the limits
of automation were always determined by the current state-of-the-art in technology. At the end
of the 20th century, the amount of information processing required to perform a task was
considered as a limiting factor. Recently, the available ICT has progressed so much through
advances in AI that the general automation capability has also expanded enormously (Bitkom &
DFKI, 2017). Therefore, it is hardly surprising that high expectations are currently being made of
the potential of automation. For the German economy alone, the accounting firm
PricewaterhouseCoopers (PwC) estimated the added value potential of AI by the year 2030 at a
total of around 430 billion euros (PwC, 2018a). PwC was not only focusing on work processes,
which are more easily accessible to automation due to their repetitive nature on the basis of
previously defined solutions and fixed decision parameters. Moreover, the PwC (2018a) study
highlighted learning AI systems that can adapt to new situations and act without human support.
This allows for bringing projects into the focus of application. A project can be compared to a
Go game, which demands creativity, intuition and strategic thinking from the player (or project
manager). As prerequisites basic cognitive skills are necessary which are characteristic of human
intelligence, such as audiovisual cognition, memory, learning, planning and problem solving.
A Conceptualization of AI for Project Automation
According to Russell and Norvig (2010), AI is concerned with the development of intelligent
agents, which can perceive their environment and carry out derived actions. Furthermore, such
artificial systems have the ability to (1) act autonomously, (2) persist for longer, (3) adapt to
changes, and (4) set and track objectives (Russel & Norvig, 2010). With regard to the successful
planning and execution of projects, so-called rational agents suggest a particular potential: their
extended capabilities enable them to strive for the best result in their actions or the most valued
result under uncertainty. Regarding its application-oriented scientific context, AI is utilizing
models and methods of mathematics, statistics/stochastics, computer science, psychology and of
cognition and neuroscience. For more complex applications, agents often process large amounts
of formalized knowledge, which in turn is derived from data (Rowley, 2007). Through this
connection between knowledge and data, concepts from the field of data management are also
relevant in the discussion about AI, but their references to AI are not always clearly demarcated.
With regard to the practical focus of this article, these terms include in particular Business
Intelligence (BI), Analytics and Big Data. In contrast to AI, BI pursues a specific application
reference, which is centered around the support of human decision-makers in operational
decision-making processes through appropriate information provision (Schieder, 2016). The term
BI is strongly influenced by certain technical implementation concepts such as multidimensional
analysis, dashboards and reporting. However, the term analytics has a more neutral, technically
oriented focus, but nevertheless aims at more specific “local” decision support from a technical
point of view (Winter, 2016). Big Data, probably the most recent term in this series, extends the
already mentioned concepts by “new” technological possibilities for processing and using
extremely large (feature volume) or rapidly growing (velocity) as well as strongly heterogeneous
(variety) data sets (Dittmar, 2016). In turn, AI systems or methods can be used in all three
concepts as tools to achieve advanced purposes. The combination of methods of AI with data
analysis methods in the field of the above-mentioned concepts (e.g., data mining) has led to the
development of new conceptual creations such as predictive analytics, advanced analytics or
data-driven AI (Bencke, 2017; Gluchowski, 2016). To create a conceptual framework for AI in
the PM domain, we followed the organization of AI topics, related concepts and procedures by
Russel and Norvig (2010) as well as combined it with the AI taxonomy by Davis, Hoffert, and
Vanlandingham (2016). We also made adaptations by leaving out nonrelevant elements and
aggregating other elements according to our research objectives based on personal judgment.
The result of this conceptualization is shown in Table 1. Guided by this framework, the search
for AI applications in PM was carried out – summarized in the according section.
Table 1. Conceptual Framework for This Study Based on Russel and Norvig (2010) as well as
Davis, Hoffert, and Vanlandingham (2016)
1. Problem solving
2. Knowledge representation
3. Machine learning
Learning with or w/o supervisor
Support vector machines
Decision tree or forest
5. Perception & recognition
Natural language processing
Image or audio classification
A Unified Understanding of Project Management
So far, it has become clear that AI is a multi-layered and diverse term. However, the second
central term in the focus of this article – project management – is hardly different in this regard.
Although the basic meaning is largely consensual, closer examination opens up a very broad and
dynamic spectrum of content-related conceptual components and their characteristics, such as
sub-areas, process models, roles and structures, methods, techniques or types of projects. This
diversity is offset by many years of standardization efforts in the PM discipline that provided
accepted definitions of terms. From a task-oriented perspective, the ISO 21500 and ANSI/IEEE
PMI PMBOK international standards consistently identify ten subfields of PM (ISO, 2012; PMI,
2017): (1) integration, (2) stakeholders, (3) scope, (4) (human) resource, (5) Time, (6) Cost, (7)
Quality, (8) Risk, (9) Procurement, (10) Communication, which are integrated into a five-phase
project lifecycle in which the two standards also agree (except for marginal differences in phase
designations): (1) Initiating, (2) Planning, (3) Implementing / Executing, (4) Monitoring and
Controlling, (5) Closing.
AI Applications in The Domain of Project Management
Ever since the beginning of the development of AI systems, the key question that has remained
immanent to date was: Can a human-developed technical system have human intelligence? Alan
Turing already investigated this question in 1950 with the still relevant Turing test. Due to the
close relationship between AI, knowledge and data processing, the limitations of AI have always
been defined by the power of Information Technology (IT) in terms of data volume and
processing speed. Rich (1983) put it in a nutshell as early as 1983 with her AI definition:
“Artificial Intelligence is the study of how to make computers do things at which, at the moment,
people are better” (p. 1). In the following, three categories are presented, which were identified
as content-related focal points within the results of our investigation on the current state of AI
development in project management. The succession of the categories results from an increasing
proportion of ‘strong’ AI.
Data-Driven Project Management
The core idea of data-driven project management (DdPM) is well-known – the more relevant
information about a decision problem is available, the more reliable the best decision alternative
can be selected (Sullivan III, 2016). Since information is based on data, any PM decision should
be founded on a solid data basis (L & George, 2004). In the DdPM notion, this database needs to
be combined with experience and intuition of a human project manager to actually make
decisions (L & George, 2004; Vanhoucke, 2018). DdPM's focus is initially on the classic
problem of resource-constrained project scheduling and thus the planning and controlling
functions in terms of time, costs, risks and quality (Vanhoucke, 2012). The repertoire of methods
includes known mathematical-statistical methods such as Program Evaluation and Review
Technique (PERT), Critical Path or Chain, Earned Value Management (EVM), Analytical
Hierarchy Process (AHP), and (Lean) Six Sigma.
In the course of the digitalization, however, more and more data as well as high-performance IT
infrastructure is available for processing. Against this background, DdPM increasingly uses
analytical methods. These pick up the results of classical, past or present-oriented methods to
derive predictions about future developments (hence predictive analytics). For example, Singh
(2015) described processes and application examples based on linear regression for the
prediction of cost changes through extension of project scope and duration for the PM area.
Since the informational value of predictive analytics results depends heavily on the amount of
data and the number of variables, special data analysis tools are indispensable for practical
application. Some authors (Duggal, 2018; Ou, 2007; Rechenthin, 2013) summarized this new
development under the term Project Intelligence (based on Business Intelligence), which has not
yet been widely accepted.
AI Platforms for Project Management
AI platforms for PM can be understood as an evolution stage of DdPM, targeted to unlocking
new potential through AI in the context of big data and analytics (Ruchi & Srinath, 2018).
Because of the combination of high implementation effort for a single company on the one hand
and high user expectations on the other hand, certain vendors have developed cloud-based
service platforms that provide AI-based services.
As a well-known company, for example, the consulting firm Deloitte offers a consulting service
under the name Predictive Project Analytics, which is based on a special analytics engine
combined with a comprehensive database, which was obtained from more than 2,000 projects
(Fauser, 2015). Furthermore, neural networks and generic algorithms are used (Fauser, 2015),
extending the conventional DdPM approach. Key areas of application include complexity and
success analyses, risk assessments and employee selection for project teams (Deloitte, 2016).
Team member selection is also featured by other application examples based on the platform
approach. E.g., startup Cloverleaf develops software for the compilation of project teams using
employee data, which, in addition to characteristics such as experience and qualifications, also
takes into account “invisible facets of a person”, e.g., the ability to adapt to the desired working
model or the agreement with (work) cultural values (Cloverleaf, 2018). A more comprehensive
approach is pursued by the Californian vendor TARA with its eponymous platform. Originally
designed to automate the recruiting process for external software developers, the focus has now
greatly expanded towards project planning and monitoring (TARA, 2018). TARA uses machine
learning to automate the initial definition of the project focus, task and time planning, creating
the project team as well as monitoring and forecasting for the current project.
Project Management Bots
The term Project Management Bots (PMB) was coined in 2017 by consulting firm Gartner in the
Hype Cycle for Project and Portfolio Management (Schoen, 2017), meaning a class of intelligent
software agents specializing in project management. In contrast to RPA bots, however, the focus
on graphical user interfaces is missing. PMB are more likely to be equipped with speech or text
interfaces for communicating with humans, and thus have features of chatbots (Gaton, 2017).
While a bot externally presents itself as one actor through one or more central communication
interfaces, in the case of bots with extended capabilities, it is mostly multi-agent systems. These
are characterized by the fact that the associated agents interact with each other in order to
achieve a common goal (Olfati-Saber, Fax, & Murray, 2007). For example, interaction can take
the form of negotiation and is based on communication between the agents. Considerations to
apply multi-agent systems in project management are already much older than the new term
creation PMB (de Medeiros Baia, 2015; Petrie, Goldmann, & Raquet, 1999; Yan, Kuphal, &
Bode, 2000). What is new, however, is that today not only research prototypes but also
commercial products are available for practical use, due to the technological developments of
recent years. PMB solutions are often based on a proprietary cloud platform that enables server-
side storing and processing of data as well as communication with and between client-side bot
components. The current product range for PMB can be divided into three categories:
(1) Independent products specialized in PM such as PMOtto, which is offered by a Danish
startup of the same name and is also referred to as the “Personal Project Management
Assistant” (PMOtto, 2018). PMOtto assists human users in working with conventional
PM software (Currently, only Microsoft Project online, which is part of the Office365
cloud software package, is supported). For this purpose, the bot understands natural
language, which it transforms into operating steps for the PM software and executes it.
The system continues to learn with machine learning and is thus able to improve
(2) Vendor-side extensions of established products to support project teams. Currently, these
are mainly found in the field of modern collaboration and communication tools such as
Microsoft Teams. This product was launched in 2017 and is a communications service
for teams integrated in the Office365 product family. Teams includes two preinstalled
chatbots, T[each]-bot and Who-bot (McDonald, 2016). T-bot supports new users in
learning system operations. Who-bot can answer questions of the type “Who knows
about x” and analyse communication via teams. Beyond the relatively simple
functionality, the two bots demonstrate the integrated functionality for developing custom
(3) Extensions for established third-party products. Especially for the Atlassian products Jira,
Confluence and HipChat/Stride a larger range of bot extensions has been developed. For
example, the company Stratejos offers a Project Assistant Bot for Atlassian products that
supports project teams in data entry and editing, risk analysis and project monitoring
Further AI-Related Aspects in Project Management
In addition to the previous AI examples in the domain of project management, a few close-knit
areas of development can be identified, such as Intelligent Information Management (IIM),
which emphasizes the integration of current AI processes and technology into information
management (e.g., Bailin & Truszkowski, 2001). The potential of IIM is closely linked to project
management information systems (PMIS) (PMI, 2017). Furthermore, Robotic Process
Automation (RPA) is suited for well-structured, less complex routine tasks, in which the term
“robotic” refers to software agents (“robots”) that are able to learn manual activities and then
perform them automatically (van der Aalst, Bichler, & Heinzl, 2018). PM is not within the
primary focus, but RPA may include monitoring and controlling of projects (e.g., to keep
thresholds) (Sharma, 2017), reporting and documentation (which is similar to IIM) or even
planning and optimization (Branscombe, 2018).
With regard to the initial question about the substitutability of the human project leader by AI,
based on our investigation of the current state of research and development, the first all-clear can
be given. The expectations exceed (still) today’s possibilities. In particular, the solutions
available in practice hardly meet the requirements of ambitious terms such as Automated Project
Management or Project Management Bot. The broad and dynamic field of tasks of a project
manager can currently only be automated in small, clearly defined areas. In the metaphor of an
autopilot, today's situation is more like a car with early assistance systems such as ABS and ESP
than a Tesla or Google Driverless Car. However, the development is very fast and prototypes
like Google Duplex give an impression of realistic potential. Much is already technically
feasible, but still needs to be brought to product maturity. So, if the technical feasibility fades
into the background, is it only a matter of time until APM becomes reality?
The results of our study point the way to further research. In general, we found a number of
interesting approaches and use cases for AI in PM. To understand the potential of AI for PM
even better, a business process perspective seems to be appropriate. Nowadays, PM is widely
considered as a set of specific business processes; standards like ISO and PMI describe
normative process models. This allows for evaluating new methods for process automation and
digitalization (e.g., Denner, Püschel, & Röglinger, 2018) whether they could be adapted for
identifying and exploiting the potential of PM processes for AI-based automation.
Apart from implementation questions, AI technology in particular raises questions of acceptance,
reliability, transparency and legal as well as ethical and moral responsibility. While these have
been discussed in other areas of application such as autonomous driving for some time, the
discussion in project management is still in its infancy. Here, too, the wheel does not have to be
re-invented. Scientifically, for example, information ethics deals with relevant questions and
provides answers (Bendel, 2016). In practice, first companies have begun to create guidelines
and framework conditions on this basis. For example, Deutsche Telekom has recently issued
“Guidelines for the Use of Artificial Intelligence” (Fulde, 2018). In addition to the further
development of the technical possibilities, the respective implications for the ethics of project
management must be constantly re-examined and evaluated.
Bailey, G. (2017). Will AI replace the project manager? ITProPortal. Retrieved from
Bailin, S. C., & Truszkowski, W. (2001). Ontology negotiation between agents supporting
intelligent information management. Proceedings of the Workshop on Ontologies in
Agent Systems, 5th International Conference on Autonomous Agents, Montreal, Canada.
Baldassarre, G. (2018). Stratejos helps teams manage daily admin and coordination of projects.
Retrieved from https://www.startupdaily.net/2018/07/stratejos-helps-teams-manage-
Bencke, M. (2017). Why 2017 is the year of data-driven AI. VentureBeat. Retrieved from
Bendel, O. (2016). 300 keywords informationsethik: Grundwissen aus computer-, netz- und
neue-medien-ethik sowie maschinenethik. Wiesbaden: Springer Gabler (in German).
Birch, D. (2018). The potential for artificial intelligence to revolutionise project management.
PM Today. Retrieved from https://www.pmtoday.co.uk/articles/the-potential-for-
Federal Association for Information Technology, Telecommunications and New Media (Bitkom)
&German Research Center for Artificial Intelligence (DFKI) (2017). Artificial
intelligence – economic importance, social challenges, human responsibility. Position
paper. Retrieved from https://www.bitkom.org/sites/default/files/file/import/171101-PP-
Branscombe, M. (2018). How AI could revolutionize project management. Retrieved from
Brocke, J. v., Simons, A., Niehaves, B., Reimer, K., Plattfaut, R., & Cleven, A. (2009).
Reconstructing the giant: On the importance of rigour in documenting the literature
search process. Proceedings of the 17th European Conference on Information Systems
(ECIS), Verona, Italy, Paper 161, Retrieved from http://aisel.aisnet.org/ecis2009/161
Burger, R. (2017). I, project manager: The rise of artificial intelligence in the workplace.
Retrieved from https://blog.capterra.com/i-project-manager-the-rise-of-artificial-
Campbell, R. H., & Terwilliger, R. B. (1986). The SAGA approach to automated project
management. In Conradi, R., Didriksen, T. M., & Wanvik, D. H. (Eds.). Advanced
Programming Environments. Proceedings of an International Workshop. Trondheim,
Norway: Springer, LNCS 244, pp. 142-155.
Cloverleaf (2018). About – Cloverleaf. Retrieved from https://cloverleaf.me/about
Davis, J., Hoffert, J., & Vanlandingham, E. (2016). A taxonomy of artificial intelligence
approaches for adaptive distributed real-time embedded systems. Proceedings of the
2016 IEEE International Conference on Electro Information Technology (EIT).
de Medeiros Baia, D. (2015). An integrated multi-agent-based simulation approach to support
software project management. Proceedings of the 37th International Conference on
Software Engineering – Vol. 2, Florence, Italy, pp. 911-914.
Denner, M.-S., Püschel, L. C., & Röglinger, M. (2018). How to exploit the digitalization
potential of business processes. Business Information Systems Engineering, 60(4), 331-
Deloitte The Netherlands (2016): Predictive project analytics 2.0. Retrieved from
Dittmar, C. (2016). Die nächste evolutionsstufe von AIS: Big data – Erweiterung klassischer BI-
architekturen mit neuen big data technologien. In Gluchowski, & Chamoni, 2016 (pp.
55-65, in German).
Duggal, J. (2018). The DNA of strategy execution: Next generation project management and
PMO. Hoboken, NJ: Wiley.
Fauser, J., Schmidthuysen, M., & Scheffold, B. (2015). The prediction of success in project
management – predictive project analytics. projektManagement aktuell, 5/2015, 66-74.
Fulde, V. (2018). We need a “Digital Ethics” policy: Deutsche Telekom defines its own policy
for the use of artificial intelligence. Retrieved from https://www.telekom.com/en/
Gaton, J. (2017). Rise of the project bots. Microsoft Project User Group (MPUG). Retrieved
Gartner, Inc. (2017). Gartner identifies three megatrends that will drive digital business into the
next decade. Retrieved from https://www.gartner.com/en/newsroom/press-
Gluchowski, P., & Chamoni, P. (Eds.). (2016). Analytische informationssysteme – business
intelligence-technologien und –anwendungen (5th ed.). Wiesbaden: Springer Gabler (in
Gluchowski, P. (2016). Entwicklungstendenzen bei analytischen informationssystemen. In
Gluchowski, & Chamoni (pp. 225-238, in German).
Groover, M. P. (3rd ed.). (2008). Automation, production systems, and computer integrated
manufacturing. Upper Saddle River, NJ: Pearson Prentice-Hall.
Hosley, W. N. (1987). The application of artificial intelligence software to project management.
Project Management Journal, 18(3), 73-75.
International Organization for Standardization (ISO) (2012). ISO 21500:2012 – Guidance on
Jordan, A. (2018). Automated project management? Retrieved from https://www.project
L, B., & George, A. (2004). Data driven project management – a scientific art. Presented at
Annual Project Management Leadership Conference, Bangalore, India, 2004. Retrieved
Levitt, R. E., & Kunz, J. C. (1987). Using artificial intelligence techniques to support project
management. Artificial Intelligence for Engineering Design, Analysis and
Manufacturing, 1(1), 3-24.
Leviathan, Y., & Matias, Y. (2018). Google duplex: An AI system for accomplishing real-world
tasks over the phone. Retrieved from https://ai.googleblog.com/2018/05/duplex-ai-
Liebowitz, J. (1997). The handbook of applied expert systems. Boca Raton, Florida: CRC Press.
McDonald, K. (2016). Microsoft teams introduces T-Bot and Who-Bot. Retrieved from
Nicols, J. (1986). Selecting an automated project management system. International Journal of
Project Management, 4(3), 132-137.
Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked
multi-agent systems. Proceedings of the IEEE, 95(1), 215-233.
Ou, R. (2007). Project intelligence. Proceedings of the 25th Annual Pacific Northwest Software
Quality Conference, Portland, Oregon, 267-274.
Petrie, C., Goldmann, S., & Raquet, A. (1999): Agent-based project management. In Woolridge,
M., & Veloso, M. (eds.), Artificial intelligence today: Recent trends and developments
(pp. 339-364). Berlin, Germany: Springer.
Pielmeier, H., & Lommel, A. (2017). Will AI eliminate the need for project managers? Common
Sense Advisory. Retrieved from http://www.commonsenseadvisory.com/abstractview/
Project Management Institute (PMI) (2017). A guide to the project management body of
knowledge (PMBOK® Guide, 6th ed.).
PMOtto (2018). PMOtto – What I do. Retrieved from https://www.pmotto.ai/#comp-ioig0sqf
PricewaterhouseCoopers (PwC) (2018a). Impact of Artificial Intelligence in Germany. Retrieved
June 06, 2018 from https://www.pwc.de/de/business-analytics/sizing-the-price.pdf
PricewaterhouseCoopers (PwC) (2018b). AI will transform project management. Are you ready?
Retrieved June 06, 2018 from https://news.pwc.ch/wp-content/uploads/2018/04/AI-will-
Rechenthin, D. (2013). Project intelligence. Project Management Institute.
Rich, E. (1983). Artificial intelligence. New York: McGraw-Hill.
Rowley, J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of
Information Science, 33(2), 163-180.
Ruchi, S., Srinath, P. (2018). Big data platform for enterprise project management digitization
using Machine learning. Proceedings of the 2nd International Conference on
Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.
Russell, S. J., & Norvig, P. (2010). Artificial intelligence – A modern approach (3rd ed.). Upper
Saddle River, NJ: Prentice Hall.
Schieder, C. (2016). Historische fragmente einer integrationsdisziplin – Beitrag zur
konstruktgeschichte der business intelligence. In Gluchowski & Chamoni (pp. 13-32, in
Schoen, M. (2017). Hype cycle for project and portfolio management, 2017. Retrieved from
Sharma, R. (2017). How to leverage RPA (Robotic Process Automation) in PM? Online
discussion in forum Project Management Central. Retrieved from
Singh, H. (2015). Project management analytics – A data-driven approach to making rational
and effective project decisions. Old Tappan, NJ: Pearson Education.
Smith, L. A., & Mills, J. (1983). Reporting characteristics of automated project management
systems. International Journal of Project Management, 1(3), 155-159.
Sullivan III, M. (2016). Statistics: Informed decisions using data (5th ed.). Harlow, England:
TARA (2018). How TARA works. Retrieved from https://tara.ai/how-it-works/
van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business
Information Systems Engineering, 60(4), 269-272.
Vanhoucke, M. (2012). Project management with dynamic scheduling: Baseline scheduling, risk
analysis and project control. Berlin, Germany: Springer.
Vanhoucke, M. (2018). The data-driven project manager. A statistical battle against project
obstacles. Gent: Apress.
Wang, Q. (2019). How to apply AI technology in project management. PM World Journal, 8(3),
Wauters, M., & Vanhoucke, M. (2014). Support vector machine regression for project control
forecasting. Automation in Construction 47, 92-106.
Winter, R. (2016). Analytische informationssysteme aus managementsicht: Lokale
entscheidungsunterstützung vs. unternehmensweite informations-infrastruktur. In
Gluchowski & Chamoni (eds.) (pp. 67-95, in German).
Yan, Y., Kuphal, T., & Bode, J. (2000). Application of multiagent systems in project
management. International Journal of Production Economics, 68(2), 185-197.
Dr. Gunnar Auth is a full professor for Business Information Systems at
Leipzig University of Telecommunications (HfTL), Germany. He completed his
master’s degree in business information systems at the University of Bamberg,
Germany, and received a Ph.D. in economics from the University of St. Gallen,
Switzerland. He started his professional career as an internal consultant at
DaimlerChrysler where he worked in several management positions in logistics,
operations and quality management. Before assuming his current position, he
was IT director and representative of the CIO board at Leipzig University. His research focuses
on IT project management, IT service management and information management.
Dr.-Ing. Oliver Jokisch is teaching as a professor for signal and system theory
at the Leipzig University of Telecommunications (HfTL), Germany. He studied
information technology at TU Dresden in Germany as well as at the
Loughborough University in United Kingdom. Oliver graduated as a diploma
engineer and holds a PhD degree in information technology from TU Dresden.
His research is dedicated to different AI areas and audio/speech communication
such as audio coding, speech prosody and synthesis or language learning
systems. Oliver has co-founded or mentored the IT-oriented companies voiceINTERconnect
GmbH, Linguwerk GmbH, COSEDA Technologies GmbH and ambisone GmbH as well as the
education and knowledge management firm IBWM GmbH.
Christian Dürk is Managing Director of the consulting firm CORIVUS AG and
one of the company’s very first employees. Graduated in industrial engineering
he has more than 15 years of experience in managing complex IT and
organizational projects. One of his specialties is the balancing act between
turning a project at a short notice and its long-term organization – in particular,
sustainable personnel planning that ensures the more permanent functioning of a
business area. He lives with his wife and three children on the German