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Tag: decision making

PMTimes_July24_2024

The Role of Artificial Intelligence in Project Management

Written by Oghenekevwe Kofi on . Posted in Articles.

1. Introduction to Artificial Intelligence

In the field of project management, AI can provide three significant benefits.

 

The first is improving project planning. Deep learning can be used to predict the tasks and resources needed, and intelligent virtual assistants can find optimal and efficient project plan scheduling under resource constraints.

The second benefit is in helping avoid risks and complications by identifying patterns that predict potential problems. The third is through enhancing decision-making regarding further resource management, strategic responses to change, and resolving potential resource conflicts.

The third and final benefit is helping project management evolve in the direction of adaptive projects. Natural language processing algorithms can analyze and distill project documents into accurate structural data used by adaptive project management, as can machine learning and data visualization employing AI.

Artificial intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal.

The term “artificial intelligence” is often used to describe machines or computers that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving.” As machines become increasingly capable, mental facilities once thought to require intelligence are removed from the definition. For instance, optical character recognition is no longer perceived as an example of “artificial intelligence,”  having become a routine technology. Capabilities currently classified as AI as of 2020 include successfully understanding human speech, competing at the highest level in strategic game systems, such as chess and Go, autonomous cars, intelligent routing in content delivery networks, and military simulations.

 

2. Applications of AI in Project Management

To sum up, the principle behind machine learning is above all to detect an algorithm based on a set of training inputs and outputs, and once these are entered, the computer understands how to reproduce the outputs of those inputs. One of the applications of artificial intelligence in project management, as we have already mentioned, is to be able to estimate costs and completion dates. However, this is a very narrow use of everything AI can offer. There are many applications of artificial intelligence in project management, but here we are going to show you the most important ones.

One of the terms closest to artificial intelligence is the concept of machine learning. The concept that closely fits artificial intelligence when it comes to project management is the idea of machine learning. When we refer to the concept of machine learning, we are talking about computerized systems that have learned to perform a given task. The principle is similar to how humans learn. In other words, it consists of training computers so that they are able to learn from the data we have prepared them. It is a question of learning to do things and not simply of giving precise orders to a computer.

 

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3. Challenges and Limitations of AI in Project Management

Currently, project management software products do not offer AI features. The incorporation of AI features can make intelligent project management tools more attractive, leading to a competitive advantage. However, there are also limitations of AI in project management. Many popular AI techniques, such as neural networks, are “black box” models, i.e., they are difficult to interpret. This makes it hard to identify the key inputs and processes and also judge their quality. It is therefore challenging to verify the correctness of the results and the decisions made by these models. Consequently, it is not practical to solely rely on AI for the control of project management problems.

AI assistance to support human decision-making is likely to be the most practical application of AI to project management. Despite major progress, current AI capabilities give relatively poor results when the demands for actions are more complex, and project management activities involve complex human interplay or cognitive decision-making requiring business intuition and strong business communication skills. The more cognitive AI can do to improve its abilities to make complex decisions, navigate uncertainty, and efficiently communicate project management activities, the more frequently it will be asked to confront problems that are outside of its current expertise.

It is difficult for AI systems to detect when unintended consequences occur and be covered effectively by different forms of liability standards and frameworks. There is a recognized need to regulate AI to ensure that the technology is used ethically and does not bring unintended harm. Despite the significant potential benefits of applying AI and ML to project management, the risks associated with their use must be addressed. Legislation and industrial standards need to be developed to mitigate these risks accurately while furthering innovation.

Currently, many challenges require the attention of researchers to make AI techniques more competitive in their applications to project management. Machine learning techniques are sensitive to the quality and size of data. Insufficient and low-quality data can lead to overfitting problems. Project data availability is often an issue. Data sets tend to be quite small in the project management domain and require advanced methods to deal with such high-dimensional and small-size problems.

AI techniques involving NLG and image processing are complex to design and manage. It remains an NP-hard problem to search for the best sub-network. In these settings, different specific indexes will have a combined negative effect on the heuristic search complexity and the performance of the translations. There are generally limited best algorithms for the training of high models and yet to identify effective features. Moreover, problems related to biased training data, privacy, and interpretability are becoming increasingly critical. The development of domain-independent AI techniques is important to explore the potential power of AI in complex project management tasks.

 

4. Future Trends and Implications

In particular, AI’s applications have the potential to revolutionize the interactions and dynamics within the project environment. AI has the capacity to transcend some specifically human deficiencies as it can perform some tasks more quickly and accurately, around the clock. Other important characteristics of AI include its ability to decrease costs, provide alternatives that can take into account diverse sources of data and information, and help reduce misunderstandings due to communications across different parties throughout the project lifecycle.

AI brings new instruments that can support this complex chain across the asset lifecycle, such as project management that ranges from planning, monitoring, quality control, risk probability assessment, resource allocation, closure, and feedback. Tools range from the use of virtual reality for providing training of operators well in advance of assets in operation, building up the cognitive learning curve, and obtaining optimal and sustainable production. However, successful implementation of AI does raise a set of technical and ethical issues that need to be addressed, such as the real-world impact of AI, privacy, and security concerns.

Disruptive technologies have a great opportunity to revolutionize project management practices. In this context, this chapter reviewed recent research efforts in two general categories about artificial intelligence (AI) strategies: redefining project management through AI and AI for project management.

It aimed to present the potential of AI applications in making projects more efficient and contributing to maximizing asset performance throughout the whole asset lifecycle. A closer collaboration is also needed between the project management and the AI research fields to create a better understanding of project management requirements with respect to AI.

The exploration of the possible impacts of future trends in the implementation of AI is a great gap to be filled in the coming years. The technology, the organization, the project structure, and the environment are also core determinants in the success of AI incorporation in the industry.

PMTimes_Apr17_2024

Collaborative Decision Making

Written by George Pitagorsky on . Posted in Articles.

Meditation teacher Tejaniya advised, “Never try to force an issue. Just acknowledge, accept, and keep observing until things unfold naturally.”

 

This might be fine when there is all the time in the world for the issue to be resolved, but from a project management perspective, it sounds far too passive an approach.

However, when you consider what happens when you force an issue by using your power as a manager or a majority in a decision-making group, there may be some wisdom in acknowledging, accepting, and observing things unfold naturally.

 

Scenario

In a program to improve the way a complex organization operated, a narrow majority of the Program Steering Group that was responsible for making decisions regarding which of several projects was to be done and in what order decided by a slim majority to authorize a project to renovate a process in one department.

They hired a design consultant and created a Design Team to provide feedback regarding the design. The Design Team reflected on the decision and was influenced by some of the minority members of the program steering group. They came back to the steering group with their unanimous opinion that the chosen project was not the best one to take on first, provided their reasoning, and called for the steering group to provide an overall plan that identified all the projects that would be part of the program, a capital financial plan, and an overall architecture before deciding on which project would be done first.

 

The steering group pushed back using their authority. They said that the Design Team was asked to give feedback on the design and not question the steering group’s decision. The steering group forced the issue.

The Design Team grumbled, but since they reported to the members of the Program Steering Group were left with no choice but to quit or comply, so they chose to comply.

 

The result was, as the Design Team predicted, a well-designed process with supporting systems that within a few years needed to be significantly changed, at great cost to fit with the other processes and systems that emerged as part of the overall renovation program. The resulting architecture resembled a patchwork – “something composed of miscellaneous parts; hodgepodge.”[1]

Over time, system maintenance was a nightmare. Further, some useful renovations were not included in the program because avoidable costs of initial projects used up the program’s budget.

 

The Consequences of Forcing an Issue

Here we see that forcing a decision led to the postponement of due diligence. Not doing capital planning and architectural design led to avoidable consequences, as pointed out in my article The Karma of Postponing Due Diligence[2]. And there are other consequences of forcing an issue, for example, disgruntled staff, and loss of respect for the decision-makers.

 

A Path Forward

So what can we do? As leaders in positions of power, we can step back and assess our decisions in the light of feedback and conflicting ideas. We can apply emotional and social intelligence along with wise decision-making, and servant leadership concepts.

Emotional intelligence comes into play when the decision-makers apply self-awareness to see why they find it necessary to force an issue. Is it because they are emotionally attached to their decision or to their power? With social intelligence, they can assess the impact of their use of authority on their staff, superiors, and peers.

As wise decision-makers, they recognize the need to look at the issue from multiple perspectives – long and short-term impacts, financial and quality consequences, and more. Servant leadership involves respect for others and their opinions and the positive impact of helping followers become wise decision-makers.

 

In a recent article[3] the author points out “the value of working with those with whom we disagree.” The author relates how Dr. Daniel Kahneman, who explored judgment and decision-making and how easily people become less than rational when making decisions, “experienced real joy working with others to discover the truth, even if he learned that he was wrong (something that often delighted him).” Kahneman favored “adversarial collaboration.”

When adversaries work together, they face the issue rather than each other. This requires acknowledging that one can be all wrong or half wrong and that the other party or parties may be right or half right, whether they are peers, superiors, or subordinates.

 

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Don’t Force an Issue

Let’s return to Tejaniya’s advice, “Never try to force an issue. Just acknowledge, accept, and keep observing until things unfold naturally.”

 

I try never to say never. There are times as a manager that we will choose to use authority to force a decision. But that is a last resort, for example when faced with a tight deadline leaving no time for further dialogue. Acknowledging, accepting, and observing until things unfold naturally is a superior way of operating. But only when we have a clear sense of what that means.

Acknowledging, accepting, and observing are active, not passive. Acknowledge and accept that there are differences of opinion and different positions. Observe your own and the other parties’ positions and behavior. Listening to content and tone is part of observing as is seeing others’ body language and facial expressions and observing your own.

Open your mind to the possibility that your position is not the best or only effective alternative. This is part of accepting. You let go of your attachment to having it your way (even if your way is not the best way).

 

Then clarify, present your view, and consider that to be part of what is unfolding naturally. You are letting go of your position and allowing the right expression of your knowledge and experience for the situation. You seek to understand the needs and wants, facts and opinions.

In the example from the article cited above, Professor Kahneman and his adversary found through a collaborative effort that they were both partially right and partially wrong. They came to a resolution that they could not have reached working on their own.

 

Never say Never

What if your opponent is closed to a collaborative approach? Then you acknowledge and accept the reality that collaboration is no longer possible and naturally force the issue (if you have the power to do so.) When you do, if you have been open-minded, asked the right questions, and objectively considered the answers, your decision might not be the same one you made before you tried to collaborate.

 

[1] Merriam Webster https://www.merriam-webster.com/dictionary/patchwork.
[2] https://www.projecttimes.com/articles/the-karma-of-postponing-due-diligence/
[3] The Nobel Winner Who Liked to Collaborate With His Adversaries https://www.nytimes.com/2024/04/01/opinion/nobel-daniel-kahneman-collaboration.html