By Dr. Elissa Farrow and Dr. Harold Kerzner
Artificial Intelligence (AI) applications have become a major interest of the project management community of practice worldwide. Many companies have already begun implementing AI augmented applications into their processes, tools, techniques, and methodologies for project management. Articles on AI predict that by 2030, AI usage will permeate almost all companies.
The challenge facing companies is not “when” it will appear in use in project management, but “where” it will appear to provide the greatest benefit to the organization. The starting point in many companies might be to find the easiest locations where AI can be implemented. Unfortunately, these locations may not be in the longer-term best interest of the organization. Existing organizational architectures, changes in the environment, emerging opportunities, and economic conditions will influence where AI applications should begin.
For more than four decades, the focus on project management applications was heavily oriented to traditional projects that began with well-defined requirements, a business case, and a detailed work breakdown structure (WBS) down through level five or six. Many of the projects were repetitive for the same customers and stakeholders. Most companies used the “waterfall” project management methodology and relied heavily upon the traditional Earned Value Measurement System (EVMS) for information needed for problem-solving and decision-making. All decisions were made by the project team and often with assistance or support from project sponsors and stakeholders. The information needed was readily available and the need for software was seen as more of a luxury rather than a necessity.
As companies became aware of the benefits of project management, executives began considering how project management could be applied to other business activities rather than restricted to traditional project domains. For many companies, expanding the number of projects into strategic programs and portfolios, then adopting modern delivery methodologies such as agile or hybrid delivery lifecycles, brought with it new challenges such as those shown in Exhibit 1. The challenges identified are interrelated and identify significant opportunities for AI- augmented practices.
Exhibit 1. Challenges Facing Project Management
Changing Environmental Conditions
When managing traditional projects with well-defined requirements, we tend to assume that the enterprise environmental factors identified at the start of the project will remain constant for the duration of the project. The decisions we make are based upon information available in the requirements and business case.
In the past few years, the COVID-19 pandemic and the rapidly changing worldwide economic and geopolitical conditions have shown us that project decision-making must consider that we live in a VUCA environment, as shown in Exhibit 2, which is continuously changing. Stability in many industries (and critically supply chain) is now not guaranteed.
Exhibit 2. The Constantly Changing VUCA Environment
The VUCA environment and the fluid impacts on companies add complexity to strategic projects. Significant time and effort will be required to keep momentum via efficient problem-solving and effective decision-making. Without tools such as AI, more people will be required to staff portfolios and projects to scan new areas of risk and opportunity not considered relevant. How many organisations had a pandemic affecting operations on their 2019 risk registers? Effective and well implemented AI augmented practices will assist portfolio and project personnel by eliminating highly process- oriented and mundane tasks to enable leaders in making faster decisions based upon automatically generated insights.
New Types of Projects
Perhaps the greatest challenge for many project managers has been the responsibility for managing more complex strategic projects. Strategic projects are often based upon an idea to be investigated, prototyped, and tested without formal agreed requirements or a business case.
Strategic projects have forced project managers to recognize that they are no longer just managing projects; they are now managing a transformation of an entire business, introducing significant innovation, or new ways of working into key business lines. Agile based methodologies have challenged the role of project managers, with new roles of facilitating and coaching.
Problem-Solving and Decision-Making
Most project managers have never participated in the portfolio definition processes where executives prioritized and selected projects. Project managers sometimes refer to this process as the ‘fuzzy front end’ and didn’t understand the justification behind an initiative or the end benefits sought. Many project managers were contracted to commence delivery. As companies expand their use of project management, project managers will be called to participate in portfolio definition activities and all participants will recognize the need for AI assistance to support or do the collation and analysis first pass.
Making strategic decisions requires a wider pool of data and information in areas such as company mission, strategic vision, goals and objectives, competition, corporate strengths, weaknesses, opportunities, threats, and market and environmental conditions. Project managers can no longer rely purely upon the EVMS for the information needed for these decisions. As shown in Exhibit 3, companies must create new types of information systems and supporting tools to evaluate the larger pool of data and information. These systems and the developments are rapidly evolving as with the recent open release of ChatGPT as an example of a language processing solution.
Exhibit 3. Changing Landscape of AI in Projects
Defining Project Success
Project success in traditional projects is usually measured in terms of time, cost, and scope. But now that project managers see themselves as managing part of a business transformation rather than just projects, the parameters defining project success are changing as shown in Exhibit 4.
Exhibit 4. Changes in How We Define Project Success
Traditional definitions of project success are usually based upon performance at the time the deliverables are completed. Business success, which is based upon the creation of business benefits and business value, and many times predicated upon opportunities, may be based upon AI and analytical statistics that are projections of what will happen in the future and consider possible changes in the enterprise environmental factors.
The need for strong collaboration between project stakeholders means a key success criterion for a project which guaranteed the quality of the project leadership and sponsorship. In addition to technical project success, we are now including an “effective leadership component” for project success as shown in Exhibit 4.
New Cultures and People Issues
On traditional projects, staffing was provided by a mix of functional managers, portfolio resource managers, and contract pools. Project managers had little input on who would be assigned. Resources were treated as a “project cost” to be removed from the project as soon as possible.
The growth in new types of projects has made senior management aware of the need for integrating organizational and multiple project cultures into a single culture that satisfies functional and project unit needs. This is shown in Exhibit 5. With the new types of projects, resources are assigned to project teams for product development requirements. This may be longer than traditional projects where a clear end date and return to from the business was managed.
The collaboration between assigned resources, governance personnel, and project managers has brought to the forefront the need for project managers to be more actively involved in the selection process for team members. The use of AI-augmented resource utilization practices for project staffing has created significant opportunities for staffing new projects.
Exhibit 5. Components of a Culture
Under growing pressure to report accurate findings as they interpret increasingly larger amounts of data, project teams will need to ensure they follow quality statistical practices. This includes more data governance in relation to the outputs from AI-augmented systems to ensure no bias has been encoded in the interpretation. The growth in the new types of projects and transformation approaches is based on collaboration between new suppliers and distributors. AI-augmented scanning solutions may help identify potential ethical issues and how to best resolve them. But at this stage, leaders and quality assurance will need to be a stronger emphasis as AI-augmented systems learn and improve.
At a recent masterclass conducted by one of the authors on big data and AI in projects, key points on ethics discussed includes:
- Respect the dignity of internal and external stakeholders.
- Avoid deception about the nature or aims of the system or tool.
- Maintain a personalised approach when communicating decisions affecting people’s life or livelihood.
- Mitigate misleading, biased, or false reporting.
In this paper, we identified five challenges and forces that are promoting the acceptance of AI-augmented practices in project management. Using the Areas of Knowledge in the PMBOK® Guide is one way to analyse the potential utilization of AI in a project context. Based upon the challenges discussed in this paper, the most common Areas of Knowledge for AI appear in Exhibit 6. Personnel selection for project staffing may very well be the first application for AI, followed by elimination of the heavy process and often mundane tasks associated with cyclical reporting and Integration Management. This would be followed by AI practices related to projections of time and cost to complete certain activities, or the entire project.
Exhibit 6. Where AI Benefits May First Appear
The decision of where AI will be utilized in a company’s architecture is certainly company-specific and based upon the types of projects that the organization undertakes, and its commitment to customer quality and service. Increases in business risk and environmental complexities will certainly accelerate the acceptance and implementation of AI. There is no disagreement that AI will appear in project management. The choice for leaders is to ensure that the implementation of AI is more than just a process change, but a cultural transformation considering the opportunities it provides as well as the risks.
Dr. Harold Kerzner, Ph.D.
Senior Executive Director, International Institute for Learning
Harold D. Kerzner, Ph.D., is Senior Executive Director at the International Institute for Learning, Inc., a global learning solutions company that conducts training for leading corporations throughout the world. He is a globally recognized expert on project, program, and portfolio management, total quality management, and strategic planning. Dr. Kerzner is the author of bestselling books and texts, including the acclaimed Project Management: A Systems Approach to Planning, Scheduling, and Controlling, Thirteenth Edition.
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Dr Elissa Farrow, PHD
Senior Consultant and Facilitator, International Institute of Learning
Dr Elissa Farrow is a futurist, author, facilitator, coach and strategist. She has over 25-year’s experience in research, organisational innovation, design, adaptation, and benefit realisation. Dr Farrow is known for her compassionate leadership and engagement approach. She is an experienced leader and has been a partner in transformation in various industries. Dr Farrow is a published author and her doctoral research explored the implications of Artificial Intelligence on organisational futures. Her research created innovative adaptation principles for leaders and delivery teams as well as new knowledge relating to how to best transform organisations operating models to anticipate and create positive futures. In 2023 Dr Farrow became an Adjunct Fellow at the University of the Sunshine Coast.
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Disclaimer: The ideas, views, and opinions expressed in this article are those of the author and do not necessarily reflect the views of International Institute for Learning or any entities they represent.