Artificial Intelligence in Project Management – Decision Support

By: Dr. Michael Shick, MSPM, PMP, CSM

Project managers encounter numerous challenges in their daily operations, including making prompt and accurate decisions about specific tasks despite limited time frames, likely insufficient amounts of information, and complex data sets. Furthermore, human biases can hinder decision-making, resulting in costly errors, which further reinforces the crucial element of decision-making to ensure the successful management of a project.

Considering the 35% project success rate articulated by Nieto-Rodriguez and Vargas (2023a) in the Harvard Business Review, it is clear that many projects fail to meet their objectives or are not completed within the predetermined timeframe and budget. This statistic underscores the critical need for improved decision-making in project management, demanding innovative solutions that can address the above-mentioned challenges. With these challenges in mind, we turn toward what many consider a revolution in the advancements of technology – Artificial Intelligence (AI).

Considering that AI had a limited audience discussion in 2022, and now it has considerable media attention, as well as multiple version updates and AI techniques and applications hitting the market, a legitimate question arises: With the various project challenges and constraints and the potential for limited information available on any particular topic associated with a project, how can I use the Artificial Intelligence technology to help me to accelerate and make more accurate decisions?

Theory and Decision-making

When understanding the human brain and how humans make decisions, we must first consider the Information Processing Theory (IPT). The theory has its roots in research by George Miller (1956), who discusses the limitations of short-term memory, and Atkinson and Shiffrin (1968), who introduced their multi-store memory model. The theory suggests that humans process the information they receive rather than merely responding to stimuli. Our brains take in information from our environment, process it, and then act upon it.

In decision-making, this process involves several steps, including identifying the problem, gathering and evaluating information, generating potential solutions, selecting a course of action, and assessing the decision’s effectiveness. However, human decision-making is bounded by cognitive limitations and biases, leading to less-than-optimal choices, otherwise known as bounded rationality.

Bounded Rationality is a concept that acknowledges the limits of human decision-making, which Herber Simon developed in the 1950s. This was in response to Adam Smith’s rationality argument that people decide in favor of their self-interest. Other prominent scholars such as John Stuart Mills, Milton Friedman, and Lionel Robbins refined Smith’s argument.

When considering bounded rationality, humans, constrained by their information, the cognitive limitations of their minds, and the amount of time they have to decide, often resort to satisficing – a decision-making strategy that aims for adequacy rather than optimization. In other words, decisions are made based on the available information rather than exploring all possible options and deciding on the best choice.

Artificial Intelligence Overcoming Decision-making Limits

Understanding the shift from bounded to full rationality with AI’s intervention, the limits traditionally imposed by the human mind, cognitive constraints, and biases may be significantly reduced. AI models, such as GPT-4, equipped with advanced machine learning and predictive analytics capabilities, can process vast amounts of data far beyond human capacity. They can sift through countless variables, identify patterns, and suggest optimal choices with speed and efficiency that surpass human capabilities. Essentially, AI enables a transition from the satisficing approach, constrained by bounded rationality, to a more optimal decision-making process, allowing individuals to get closer to making a fully rational decision (Shick et al., 2023).

In the realm of project management, this shift has profound implications. Project managers with AI tools can make decisions based on comprehensive data analysis and deep learning models rather than relying solely on readily available information or gut instinct. Thus, AI has the potential to significantly increase the probability of project success by enabling more informed and rational decision-making.

Conceptual Artificial Intelligence Roles to Support Decision-making

One of AI’s most promising potential applications in project management lies in real-time project monitoring and proactive risk detection. AI algorithms and advanced data analytics can continuously monitor project performance, detect anomalies, and highlight potential risks in real-time. It can sift through vast data streams, identify patterns, and provide predictive insights, allowing project managers to anticipate and proactively manage potential roadblocks.

AI-powered project management tools can generate real-time dashboards, providing invaluable, up-to-the-minute project insights. These dashboards can visualize complex data in a user-friendly format, making it easier for project managers to understand the project’s current status, identify bottlenecks, and take corrective actions promptly.

Artificial intelligence may be used in project management to automate tasks, analyze data, and improve decision-making. It helps in optimizing resource allocation and predicting project outcomes, enhancing overall efficiency.

Furthermore, AI’s ability to learn and adapt over time means these systems will grow more accurately and effectively with each project. Over time, it can build a rich repository of historical data and learn from past project outcomes, continually refining its risk prediction capabilities. This results in a more proactive approach to risk management, where potential issues are identified and addressed long before they escalate into significant problems.

Preparation Tips for Project Managers

On August 10th, 2023, Nieto-Rodriguez and Vargas presented a White Paper via Zoom. Subsequently, they made it available to download, where they unpacked the findings of a global survey on project managers’ perceptions of AI use. They revealed that a significant majority, precisely 74.79% of the experts surveyed, acknowledged the potential of AI to boost project execution, decision-making, and strategic alignment (Nieto-Rodriguez & Vargas, 2023b). Despite the prevailing challenges, this recognition of AI’s capabilities underscores its transformative power in project management. In a further exploration of the data, an exciting narrative unfolded. It was noted that 65.13% of the project managers surveyed saw AI as a tool for efficiency and a catalyst for innovation in project management (Nieto-Rodriguez & Vargas, 2023b). They identified AI’s potential to unlock new approaches and techniques that could revolutionize managing projects. Interestingly, approximately 50.72% of the respondents expressed a high propensity to invest in exploring AI’s potential, showing considerable enthusiasm (Nieto-Rodriguez & Vargas, 2023a).

With such a high level of interest in AI adoption among project managers based on the global survey findings, investing in training programs focused on AI tool management and data analytics skills is recommended. These programs can equip managers with the expertise to leverage AI platforms effectively and enhance data literacy, which is key to making well-informed decisions powered by AI.

The Imperative of Data Collection and Cleansing for Artificial Intelligence Training

A crucial step in the journey towards AI-driven project management is collecting and cleaning historical project data. High-quality, relevant data is the foundation for AI models like ChatGPT, Bard, and Anthropic to learn, adapt, and provide accurate insights. Gathering this data involves compiling information from completed projects, including project timelines, costs, quality metrics, and risk occurrences.

However, merely collecting data is not sufficient. It must be cleaned and normalized – a process that involves removing duplicates, correcting errors, filling in missing values, and ensuring the data is consistent. This is essential to prevent skewed or inaccurate analyses to solve problems by AI models.

Project managers should prioritize this, as data quality directly influences the effectiveness of AI in decision-making. Investing time in thorough data cleaning ensures that the information feeding into the AI models is accurate and enables the models to make better predictions, improve risk identification, and provide more valuable insights. Consequently, the diligence applied to data collection and cleansing can significantly amplify AI’s contribution to project management decision-making.

Fostering a Culture of Continuous Learning

Project managers and their teams should be committed to staying updated with the latest AI advancements, understanding their implications, and adapting their workflows accordingly. Regular training sessions, webinars, and workshops can be conducted to brief the team on emerging AI trends and their potential impact on project management. This allows for a more dynamic approach to project management, where AI tools are regularly updated and optimized based on the latest research and innovations.

Addressing Ethical and Legal Considerations in Artificial Intelligence

First and foremost, transparency in deep learning is a prime concern. The decision-making process in AI models, often dubbed the ‘black box,’ should be as transparent as possible to maintain trust among project stakeholders. This entails a clear understanding of how AI models derive their conclusions, allowing for assessing the AI’s reasoning and identifying potential biases.

Bias in AI models can inadvertently lead to unethical and, in some cases, illegal outcomes. Thus, measures should be in place to avoid and mitigate biases in AI decision-making. This includes thorough testing of training data for large language models and regular audits of AI systems to identify and correct any preferences that may be present in the data or the model itself.

AI applications in project management should align with stakeholders’ values. The goals set by AI should not only aim for project success but also consider organizational values and objective truth rather than bending to subjective perspectives, which may be contrary to objective reality. Further, ethical and legal considerations involve ensuring that AI does not compromise on factors such as privacy and fairness. Regular stakeholder consultations can be helpful in this context, giving all involved parties a say in the AI’s operational parameters and objectives.

Conclusion

The revolutionary potential of AI in reshaping project management is truly immense. From optimizing decision-making processes to providing data-driven insights, AI has the potential to transform traditional project management methods. It can enhance efficiency, accuracy, and productivity, thus leading to improved project outcomes. However, the journey toward AI integration in project management is not without challenges. Ethical and legal considerations, continuous machine learning models and needs, and the imperative of quality data collection and cleaning are significant aspects that organizations need to address. Balancing the power of AI with the necessary human judgment is equally essential to ensure effective and sustainable decision-making. AI doesn’t replace human project managers; instead, it augments their capabilities, providing them with tools and insights to help them make better, more informed decisions. As organizations continue to navigate their journey towards AI-driven project management, they must remain adaptive, proactive, and ethically conscious, ensuring that technology enhances, not replaces, human expertise and judgment.

Synergy Between Human Expertise and Artificial Intelligence Capabilities

The fusion of human expertise and AI capabilities heralds a new era in project management. The two don’t exist in isolation but in a symbiotic relationship where each enhances the other’s strengths and compensates for weaknesses.

Human project managers, with their nuanced understanding of the project terrain and intuitive decision-making abilities, can provide context and meaning to AI-generated insights. They can interpret the data, discern relevant and irrelevant information, and apply their experiential knowledge to make informed decisions.

Conversely, AI can process vast amounts of data, predict outcomes and augment human decision-making by providing quantitative insights that humans may overlook. It can highlight unseen patterns, identify risks, and suggest optimal choices, aiding project managers in making more informed, data-backed decisions.

This synergy between human expertise and AI capabilities is not just about optimizing decision-making but also about fostering a project management approach that is both data-driven and human-centric. It’s about integrating the best of both worlds to create a robust, effective, and adaptable project management strategy. This harmonious blend of human and AI capabilities is the cornerstone of ai programs and next-generation project management, ai research is promising to revolutionize how organizations plan, execute, and evaluate projects.

Looking Towards the Future: The Evolution of Artificial Intelligence in Project Management

As we look toward the future, the role of Artificial Intelligence in project management is poised to evolve significantly. The advent of increasingly sophisticated AI models, such as GPT-4, is expected to usher in a new wave of transformation. These advanced models, capable of processing and analyzing vast amounts of data at unprecedented speeds, will further enhance the decision-making capabilities within project management.

Emerging AI technologies such as predictive modeling, natural language processing, and decision management systems are set to redefine project planning, task allocation, risk management, and more. Predictive modeling, for example, will allow project managers to forecast project outcomes with greater accuracy, enabling them to make proactive decisions and mitigate potential risks. Natural language processing should streamline communication within project teams, enhancing collaboration and efficiency. Decision management systems, on the other hand, will likely automate routine decision-making, freeing up project managers to focus on strategic aspects of the project.

However, it’s crucial to acknowledge that integrating evolving AI technologies into project management will present challenges and ethical considerations. As AI technology becomes more complex, transparency, bias, and data privacy issues of human intelligence will become increasingly intricate. Organizations must proactively address these concerns, ensuring the ethical use of AI in project management.

The Imperative of Human Oversight in Artificial Intelligence-Driven Decision Making

Despite the advances in AI and its ability to clean and process vast amounts of data, human intervention and oversight remain an irreplaceable part of the decision-making process. Equipped with their expertise and intuition, project managers are needed to validate AI’s findings, ensuring an optimal blend of human judgment and AI-driven insights in decision-making.

Fostering a culture of continuous machine learning is essential not just about AI but also for enhancing knowledge in project management. This is because AI, despite its formidable capabilities, still requires human intelligence oversight and validation of information. For instance, while AI was utilized in generating the framework and content of this article, a significant amount of misinformation and disinformation was identified. Moreover, it could not answer the question posed without significant revectoring. Understanding the limitations calls for a discerning eye to separate the accurate from the misleading; furthermore, it is necessary to validate the information presented to ensure accuracy and relevance.

This article, while developed with the efficiency of AI, required a considerable amount of groundwork, due diligence, and an editorial process to bring it to fruition. Human oversight and validation of AI should always be maintained, especially regarding decisions that impact the real world. Therefore, as we leverage the potential of AI in transforming project management, we must not lose sight of the human element that brings meaning, human language, context, and ethical consideration into the mix.

Frequently Asked Questions (FAQs)

What is artificial intelligence with an examples?

Artificial intelligence has become a very popular and effective technology in many industries. AI is currently used for the detection of financial fraud by smartphones or voice-assisted devices. It also can be used to draft emails on your behalf or to help personalize your search results on the web. An example of AI in project management could be using a machine learning algorithm to predict resource availability and optimize task scheduling. Furthermore, natural language processing (NLP) techniques can be used for automatic summarization of project progress and identifying possible risks.

What is a project manager?

A project manager is the person who is responsible for managing a project from start to finish. They are typically in charge of creating and maintaining project plans, assigning tasks, monitoring progress and budgets, ensuring deadlines are met, and helping solve any issues that arise during the course of the project.

What is the meaning of decision-making?

Decision-making refers to identifying decisions, gathering information, and examining alternative solutions. Using the step-by-step decision-making method can help to organize pertinent information and identify alternative choices.

What do mean by rationality?

Rational decision-making is the process of making decisions that are based on logical, rational thinking and facts. Rational decision-making involves evaluating all available options in a structured way, so as to understand the potential outcomes of each before selecting the best solution. It also includes taking into account any risks associated with each option when making a final decision.

What is bounded rationality?

Bounded rationality as it relates to decision-making is the concept that a person’s decision-making process is limited by their cognitive capacity and the amount of time they have to make a decision. This means that people are able to use logical reasoning and make informed decisions, but are limited by their own individual understanding and experience. Bounded rationality takes into account human limitations when making decisions.

References

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89-195). New York: Academic Press.

Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97.

Nieto-Rodriguez, A., & Vargas, R. V. (2023a). How AI will transform project management. Harvard Business Review. Retrieved on August 11, 2023 (https://hbr.org/2023/02/how-ai-will-transform-project-management)

Nieto-Rodriguez, A., & Vargas, R. (2023b). Unleashing the power of artificial intelligence in project management: First global survey.

Shick, M., Johnson, N. and Fan, Y. (2023), “Artificial intelligence and the end of bounded rationality: a new era in organizational decision making,” Development and Learning in Organizations, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/DLO-02-2023-0048

About the author: Dr. Michael J. Shick, MSPM, PMP, CSM, founder of ROSEMET, is a combat-wounded warrior and retired senior military officer turned esteemed academic and project management expert. Holding a doctorate from Creighton University and serving as an Assistant Professor at Western Carolina University, Dr. Shick’s dedication goes beyond credentials, as he commits to empowering individuals and organizations toward project excellence. With an extensive military, academic, and project leadership background, he epitomizes resilience, expertise, and a steadfast devotion to fostering growth and success in others.