TOWARDS AN ARTIFICIAL INTELLIGENCE

MATURITY MODEL FOR PROJECT

MANAGEMENT
A STUDY OF INFORMATION TECHNOLOGY PROJECTS IN EUROPE
ANDREA CIOLINI
SBS SWISS BUSINESS SCHOOL
DBA PROGRAM
BUSINESS CHALLENGE

• Projects have become increasingly complex due to technological advancements (Morcov et al., 2021),
internal strategic pressures, and increased dependence on external expertise (Bosch-Rekveldt et al., 2018).

• Artificial Intelligence (AI) has attracted interest in supporting project managers in overcoming complexity,
but there are several barriers to adoption, such as integrating AI in the overall business strategy, finding skilled
resources, and ensuring high-quality data (Alshaikhi & Khayyat, 2021; Alsheibani et al., 2018; Sadiq et al.,
2021; Schuster et al., 2021):

• Competencies and readiness at different levels are required for adoption. A maturity model could be
proposed to evaluate the current AI maturity level in organizations, identify areas requiring
improvement, and track progress toward optimal utilization (Reichl & Rudolf, 2023). This is currently a gap,
considering the subsequent literature review
LITERATURE REVIEW: ARTIFICIAL INTELLIGENCE

• AI is a broad term defined “a system’s ability to correctly interpret external data, to learn from such data,
and to use those learnings to achieve specific goals and tasks“ (Kaplan & Haenlein, 2019, p. 15).

• AI is a general-purpose technology or GPT, similar to electricity or the steam engine, with considerable potential
for improvement but destined to power economic growth (Brynjolfsson & Mcafee, 2017)

• Three incremental anthropomorphic classifications (Kaplan & Haenlein, 2019) :

• Artificial Narrow Intelligence (ANI), also known as weak AI.
• Artificial General Intelligence (AGI), or strong AI.
• Artificial Super Intelligence (ASI) or self-aware/conscious AI
LITERATURE REVIEW: AI AND PROJECT MANAGEMENT

• Scopus database searching for “artificial intelligence” (or “AI”) and “project management” in the title. Scopus
provided 35 results to be reviewed, primarily articles (16) or conference papers (16)

• Although the assessment of AI adoption in project management started as early as 1987, with a seminal article on
expert systems in the Project Management Journal (Hosley, 1987)

• AI is still considered young in the project management field (Alshaikhi & Khayyat, 2021), and attention has been
focused on its perception and potential for the future (Bodea et al., 2020).

• Process areas and specific functions have been investigated regarding the potential benefits of AI adoption
(Fridgeirsson et al., 2021; Holzmann et al., 2022; Lahmann et al., 2018; Mikhaylov, 2023).

• Key theme include the impact on project management jobs and the perceived risk of losing jobs (Alshaikhi &
Khayyat, 2021). Leadership, empathy, verbal/nonverbal communication, and negotiation skills will be in high
demand for future project managers (Lahmann et al., 2018). In contrast, AI will assist and advise based on
predictive and prescriptive analytics (Mikhaylov, 2023).
LITERATURE REVIEW: AI MATURITY / DIMENSIONS
Only AIMMs published in academic publications were considered, and secondary research was the starting point,
with three systematic literature reviews covering 22 unique papers (Reichl & Rudolf, 2023; Sadiq et al., 2021;
Schuster et al., 2021)

Yams et al. (2020)

Sirrenberg (2019)

Saari et al. (2019)

Mikalef & Gupta

Noymanee et al.

Coates & Martin

Alsheiabni et al.

Burgess (2018)

Gentsch (2019)

Jaaksi (2018)
Ellefsen et al.

Fukas (2022)

Limat (2022)
Jöhnk et al.
Holmström

Kreutzer &

Lee (2020)
(2019)

(2022)

(2021)
(2019)

(2021)

(2022)

(2019)
Authors

Column2

Reference SLRs [1] [2] [3] [1] [2] [1] [2] [3] [1] [1] [2] [3] [1] [3] [1] [2] [1] [3] [1] [2] [1] [1] [3] [1] [1] [2] [1] [3] [2] [2]
Totals

Data 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15

Organization 1 1 1 1 1 1 1 1 1 1 1 1 1 13
Dimensions

People 1 1 1 1 1 1 1 1 1 1 1 1 12

Technology 1 1 1 1 1 1 1 1 1 1 1 11

Strategy 1 1 1 1 1 1 1 1 8

Ethics 1 1 2

61

Note. Own work adapted from [1] Schuster et al. (2021) and incorporated [2] Sadiq et al. (2021) and [3] Reichl and Rudolf (2023).
 LITERATURE REVIEW: AI MATURITY / DIMENSIONS
• Data in terms of quantity, quality, and processing perspectives. This involves a data strategy, governance, and
integration across various departments and applications within the organization.
• Organization encompasses establishing appropriate structures, business processes, and financial resources to
ensure AI technology's effective introduction, utilization, and advancement.
• People dimension assesses the competencies of human resources in developing and utilizing AI technology. It
also considers whether workplace culture fosters innovation and transformation and establishes appropriate
practices for training and recruiting individuals with AI skills.
• Technology refers to the collection of tools, including hardware and software, necessary for developing, deploying,
and efficiently functioning AI applications.
• Strategy evaluates whether the AI goals are aligned with the company's strategic objectives. This includes
leadership commitment to the future and top management’s support for current initiatives.
• Ethics is at the bottom of the list and assesses whether ethical values and standards are established to promote
responsible, transparent, fair, safe, and secure practices in AI implementation.
LITERATURE REVIEW: AI MATURITY / LEVELS
Seven maturity levels have been mapped, and the final naming (in the rightmost column) was based on the
highlighted items in green.

Lichtenthaler (2020

Abele & D’Onofrio

Noymanee et al.
Alsheiabni et al.

Burgess (2018)

Gentsch (2019)

Jaaksi (2018)
Ellefsen et al.

Fukas (2022)

Holmström

Yams et al.

Seetharam
Seger et al.
Saari et al.
Kreutzer &
Sirrenberg

Bangalore
(2019)

(2022)
(2019)

(2019)

(2022)

(2019)

(2019)

(2020)

(2020)

(2020)
Authors

Totals

[1] [2] [3] [1] [2] [1] [2] [3] [1] [1] [2] [3] [1] [3] [1] [2] [1] [2] [1] [2] [1] [1] [2] [1] [2] [1] [3] [3] [2]
Selected Maturity
Levels

Manual Isolated 0 - Does not Unaware or risk

Level 0 processing
None 0%
Ignorance exist averse None

Traditional IT Non- 1 - New to AI, Smart

up to 20% Aware and
Level 1 Intial enabled AI Novice Intial Algorithmic Low but willing to
(missing)
Initial intent Rookie 1 - Preliminary
resourceful
Foundational information Early Learning Intention
automation Enterprise invest systems

2 - few
Isolated basic Semi-
projects with 20-40% Independednt Reactive
Level 2 Assessing automation AI ready Assessing Automated Moderate
limited (Point by point) initiative
Beginner 2 - Defined Experimenting
machines
Lay Foundation Experimentation
attempts Enterprise
Maturity levels by author

internal skills

Tactical
3 - building 40-60% Artificial
individual Automated interactive
Level 3 Determined
automation
AI Proficient Determined
Enterprise
High vision and (individual areas
implementation
Operational 3 - Managed Operational Narrow Leverage Determination
experience - not connected) Intelligence
tools

4 - AI is adding 60-80%
Tactical
value with (in many areas - Interdependent Fully developed
Level 4 Managed range of AI Advance Managed Excellent
roadmap for AI partly innovation
Expert 4 - Excellent
strategic plan
Inquiring Accelerate Acceleration
automation
ambitions connected)

End-to-end- 80-100% AI harnessed at Artificial

Integrated
Level 5 Optimise strategic Optimised (completely
Intelligence
Mastery scale and as Integrated General Optimization
automation connected) strategic asset Intelligence

Intuitive
Super
Ingenuity
Level 6 Intelligence
(some
Self-aware AI Cognition
Enterprise
consciousness)

Note. Own work adapted from [1] Schuster et al. (2021) and incorporated [2] Sadiq et al. (2021) and [3] Reichl and Rudolf (2023).
LITERATURE REVIEW: PM-AIMM

Note. Adapted from Chen et

al. (2022). This view shows
the draft model in a three-
dimensional space with the
maturity level as the third Data
axis. Note the hypothesis of
process groups getting Technology
closer horizontally with
increasing maturity.
People
Additionally, the distinction
between cross dimensions
Level 5 - Optimization
and specific dimensions Organization
vertically. Level 4 - Acceleration

Ethics Level 3 - Determination

Level 2 - Experimentation
Level 1 - Intention
Strategy
Level 0 - None
Initiating Planning Executing Monitor Closing
Process Process Process And Control Process
Group Group Group Process Group
Group
LITERATURE REVIEW: PRODUCTIVITY

• The degree of contribution of the PM-AIMM to project management success will be evaluated through productivity
using the classical project’s triple constraints (time, cost, and quality) and stakeholder benefits (Atkinson, 1999)

• Time refers to the impact of maturity on the ability to meet project deadlines and milestones.

• Cost refers to its impact on controlling project costs and preventing budget overrun.

• Quality refers to the ability to deliver high-quality project outputs with minimal defects or issues.

• Stakeholder satisfaction affects the ability to engage and satisfy stakeholders successfully.
RESEARCH QUESTIONS

This research aims to develop a Project Management-specific Artificial Intelligence Maturity Model (PM-AIMM)
that will provide a range of maturity levels based on the five project management process groups as outlined by PMI
(2021)

1. What is the current AI maturity in project management by process group?

2. How does the maturity level of AI implementation affect the productivity in each group?

3. What dimensions determine an organization's AI maturity across the five process groups?

4. Can a PM-AIMM be developed based on the observed impact of AI on productivity?

Monitor and
Initiating Planning Executing Control Closing
Process Group Process Group Process Group Process Group
Process Group
HYPOTHESES
MV AI Maturity dimensions

Data H2a1

Organization H2a2
AI maturity
by process group IV People H2a3

Initiating H1a1 H2a4

Technology
DV Project productivity
Planning H1a2
Strategy H2a5
Time
Executing H1a3
Ethics H2a6 Cost

Monitor and Control H1a4

H2 Quality

Closing H1a5 H1 Stakeholder

Satisfaction
 HYPOTHESES
H10: AI maturity level does not significantly impact productivity within the project management process groups.
H1a: Higher AI maturity levels correlate with increased productivity across project process groups.

This will have a sub-hypothesis for each domain:

H1a1: Higher AI maturity levels are correlated with more productive project initiation.
H1a2: Higher AI maturity levels are correlated with more productive project planning.
H1a3: Higher AI maturity levels are correlated with more productive project execution.
H1a4: Higher AI maturity levels are correlated with more productive project monitoring and control.
H1a5: Higher AI maturity levels correlate with more productive project closure.
 MODERATING HYPOTHESES
H20: The relationship between the AI Maturity levels and project productivity is not
moderated by the AI Maturity dimensions (data, ethics, people, technology, strategy, and organization).
H2a: The AI Maturity dimensions moderate the relationship between the AI Maturity levels and project productivity.

This will have a sub-hypothesis for each domain:

H2a1: Data quantity, quality, and processing capability have a moderating effect on the relationship between AI maturity and project productivity.
H2a2: The availability of organizational structures, business processes, and financial resources to support AI has a moderating effect on the relationship between AI
maturity and project productivity.
H2a3: The availability of skilled people and human resources practices geared towards hiring or training AI skills have a moderating effect on the relationship between
AI maturity and project productivity.
H2a4: The availability of technology to design, develop, and deploy AI tools and applications has a moderating effect on the relationship between AI maturity and
project productivity.
H2a5: The alignment of AI with the company’s strategic objectives and top management support has a moderating effect on the relationship between AI maturity and
project productivity. H2a6: Ethical values and standards have a moderating effect on the relationship between
AI maturity and project productivity.
REFERENCES
Abele, D., & D’Onofrio, S. (2020). Artificial Intelligence – The Big Picture. In (pp. 31-65). https://doi.org/10.1007/978-3-658-27941-7_2

Alshaikhi, A., & Khayyat, M. (2021). An investigation into the impact of artificial intelligence on the future of project management. 2021 International Conference of Women in

Data Science at Taif University, WiDSTaif 2021,

Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial Intelligence Adoption: AI-readiness at Firm-Level. PACIS, 4, 231-245.

Alsheibani, S., Cheung, Y., & Messom, C. (2019). Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts. PACIS,

Atkinson, R. (1999). Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria. International Journal of Project

Management, 17(6), 337-342. https://doi.org/https://doi.org/10.1016/S0263-7863(98)00069-6

Bangalore Seetharam, S. (2020). Developing a Digital AI Roadmap for Retail http://www.theseus.fi/handle/10024/342414

Bodea, C., Ronggui, D., Stanciu, O., & Mitea, C. (2020). Artificial Intelligence impact in Project Management. IPMA & PwC.

https://www.ipma.world/assets/IPMA_PwC_AI_Impact_in_PM_-_the_Survey_Report.pdf

Bosch-Rekveldt, M., Bakker, H., & Hertogh, M. (2018). Comparing Project Complexity across Different Industry Sectors [Conference paper]. Complexity, 2018, 1-15, Article

3246508. https://doi.org/10.1155/2018/3246508

Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard business review, 1, 1-31.

Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial

intelligence: An agenda (pp. 23-57). University of Chicago Press.

REFERENCES
Chen, W., Liu, C., Xing, F., Peng, G., & Yang, X. (2022). Establishment of a maturity model to assess the development of industrial AI in smart manufacturing [Article]. Journal of

Enterprise Information Management, 35(3), 701-728. https://doi.org/10.1108/JEIM-10-2020-0397

de Bruin, T., Freeze, R., Kulkarni, U., & Rosemann, M. (2005). Understanding the Main Phases of Developing a Maturity Assessment Model. Australasian Conference on

Information Systems.

de Wit, A. (1988). Measurement of project success. International Journal of Project Management, 6(3), 164-170. https://doi.org/https://doi.org/10.1016/0263-7863(88)90043-9

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal

of Information Management, 48, 63-71. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2019.01.021

Ellefsen, A. P. T., Oleśków-Szłapka, J., Pawłowski, G., & Toboła, A. (2019). Striving for excellence in AI implementation: AI maturity model framework and preliminary research

results. LogForum, 15(3).

Favari, E. (2023). Project Management: Leading Change in the Age of Complexity [Book]. Springer International Publishing. https://doi.org/10.1007/978-3-031-25031-6

Fridgeirsson, T. V., Ingason, H. T., Jonasson, H. I., & Jonsdottir, H. (2021). An authoritative study on the near future effect of artificial intelligence on project management

knowledge areas [Article]. Sustainability (Switzerland), 13(4), 1-20, Article 2345. https://doi.org/10.3390/su13042345

Gentsch, P. (2019). Artificial Intelligence in Marketing, Sales and Service - How Marketers without a Data Science Degree can use AI, Big Data and Bots.

https://doi.org/10.1007/978-3-658-25376-9
REFERENCES
Gruetzemacher, R., & Whittlestone, J. (2022). The transformative potential of artificial intelligence. Futures, 135, 102884.

https://doi.org/https://doi.org/10.1016/j.futures.2021.102884

Hashfi, M. I., & Raharjo, T. (2023). Exploring the challenges and impacts of artificial intelligence implementation in project management: A systematic literature review.

International Journal of Advanced Computer Science and Applications, 14(9).

Holzmann, V., Zitter, D., & Peshkess, S. (2022). The Expectations of Project Managers from Artificial Intelligence: A Delphi Study. Project Management Journal, 53(5), 438-455.

https://doi.org/10.1177/87569728211061779

Hosley, W. N. (1987). The application of artificial intelligence software to project management. Project Management Journal, 18(3), 73-75.

https://www.pmi.org/learning/library/application-artificial-intelligence-software-pm-5234

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business

Horizons, 62, 15-25.

Krosnick, J., & Presser, S. (2010). Question and Questionnaire Design. In Handbook of Survey Research (pp. 263-313). Emerald Group Publishing Limited.

Lahmann, M., Keiser, P., & Stierli, A. (2018). AI will transform project management. Are you ready? Retrieved 12/09/2023 from https://www.pwc.ch/en/insights/risk/ai-will-

transform-project-management-are-you-ready.html

Lichtenthaler, U. (2020). Five Maturity Levels of Managing AI: From Isolated Ignorance to Integrated Intelligence. Journal of Innovation Management, 8.

https://doi.org/10.24840/2183-0606_008.001_0005
REFERENCES
Mikhaylov, A. (2023). A Survey of Artificial Intelligence Tools in Project Management [Conference paper]. Lecture Notes in Networks and Systems,

Morcov, S., Pintalon, L., & Kusters, R. J. (2021). Definitions, Characteristics and Measures of IT Project Complexity - A Systematic Literature Review. International Journal of

Information Systems and Project Management. https://doi.org/10.12821/ijispm080201

PMI. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute.

PMI Sweden Chapter. (2024). Artificial Intelligence and Project Management. P. M. I. S. Chapter.

Prifti, V. (2022). Optimizing Project Management using Artificial Intelligence. European Journal of Formal Sciences and Engineering, 5, 29. https://doi.org/10.26417/667hri67

Quinlan, C., Babin, B., Carr, J., Griffin, M., & Zikmund, W. G. (2019b). Business Reearch Methods (2nd Edition). Cengage.

Reichl, G., & Rudolf, G. (2023). Maturity Models for the Use of Artificial Intelligence in Enterprises: A Literature Review. https://doi.org/10.24867/IS-2023-VP1.1-5_07341

Sadiq, R., Mohd Satar, N., Abd Rahman, A. H., & Goudarzi, S. (2021). Artificial intelligence maturity model: A systematic literature review. PeerJ Computer Science, 7.

https://doi.org/10.7717/peerj-cs.661

Schuster, T., Waidelich, L., & Volz, R. (2021). Maturity Models for the Assessment of Artificial Intelligence in Small and Medium-Sized Enterprises. In (pp. 22-36).

https://doi.org/10.1007/978-3-030-85893-3_2

Seger, J., Miailhe, N., & Mueller, S. (2019). The AIGO: A Framework for Planning, Developing, and Deploying Artificial Intelligence in Intergovernmental Organizations.

Turner, J. R., Anbari, F., & Bredillet, C. (2013). Perspectives on research in project management: the nine schools. Global Business Perspectives, 1(1), 3-28.

https://doi.org/10.1007/s40196-012-0001-4