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AI Technologies in Project Management

Introduction

Attempts to automate work processes began as far back as the Middle Ages and the Enlightenment era, when researchers aimed to develop mechanisms capable of replacing human labor. A notable example is the mechanical calculator invented by Blaise Pascal in the 17th century, which could perform addition.

However, the possibility of developing programs capable of performing complex intellectual tasks emerged much later. It was only in the 1950s that scientists across various fields began to contemplate the possibility of creating artificial intelligence. During that time, researchers began attempting to build a machine that could simulate the functions of the human brain. Around the same time, Alan Turing, the renowned English mathematician and cryptographer, set out to determine whether a machine could think and published the paper 'Computing Machinery and Intelligence,' in which he proposed the idea of an empirical test - the Turing test. The standard interpretation of this test is as follows: 'A person interacts with both a computer and a human through text-based communication. Based on the responses to their questions, the person must determine which one is the human and which one is the computer program. The computer program's task is to deceive the person, leading them to make the wrong choice.' Just a year later, in 1951, Marvin Minsky, a Harvard graduate student, created the first neural network named SNARC.

The widely known term 'artificial intelligence' was first coined at the Dartmouth Conference in 1956, and it was during this event that the scientific discipline of 'Artificial Intelligence Research' was established.

Artificial Intelligence (AI) is a system or machine capable of emulating human behavior, performing tasks, and gradually learning from acquired information. In other words, artificial intelligence is the ability of computer systems to simulate human brain activity.

The question of applying artificial intelligence in project management has been under exploration for around 35 years. In 1987, William Hosli gained global recognition for publishing an article titled 'The Use of Artificial Intelligence Applications in Project Management.' In the same year, a NASA-sponsored study was conducted, focusing on the effectiveness of applying AI methods in project management.

At present, artificial intelligence has reached a stage where it can be effectively employed in project management and viewed as a system capable of performing auxiliary project management functions without constant human oversight.

According to the Project Management Institute, around 80% of users are confident in the positive impact of AI on their company operations. Digital intelligence not only automates processes and managerial tasks but also generates insights and recommendations for making decisions related to projects.

Key Trends in Artificial Intelligence Development

This year, governments and corporations around the world are planning to invest over 500 billion dollars in artificial intelligence development, so it's not surprising that new advancements in the field of AI emerge almost every day.

Among the myriad of applications and developments in AI, several of the most promising and interesting trends can be highlighted at present:

Generative AI: This new dynamic direction has revolutionized perceptions of AI capabilities across various domains, from art and poetry to journalism and coding. A prominent example of generative AI is ChatGPT, which has become a breakthrough in the world of AI. The appeal of generative artificial intelligence lies in its ability to create original content such as paintings, news, poems, and fragments of code. Neural networks can generate text that appears visually and contextually human-written. In the near future, generative AI is expected to create email campaigns, assist in developing marketing concepts, slogans, and brands, as well as generate drafts, industrial model concepts, and advertising materials. This will enable creativity without the need for years of skill acquisition.
Voice and Language AI: The evolution of this trend is driven by Large Language Models like GPT-3 and GPT-4 from OpenAI, LaMDA from Google, and LLAMA from an independent community of over a thousand researchers from 70 countries. Openness toward developing open language models, such as BLOOM, is of particular importance. Researchers and developers are willing to share details about the data used to train these neural networks, as well as the challenges faced during their development and methods for assessing performance. However, OpenAI and Google do not disclose their code and do not make their models publicly available. This means that these models remain unknown to external researchers as the exact algorithms by which these models are trained are only known to the developers themselves.
Edge AI: This AI allows machine learning algorithms to operate directly on electronic devices such as vacuums, toothbrushes, smartphones, and cars. The advancement of Edge AI will result in data processing taking place in mere milliseconds, providing real-time feedback. There will be no need to spend time and traffic on sending information to and from cloud storage, as is often the case now. Of course, data exchange between hardware devices and the parent AI model will still be necessary, as the main model needs to continue learning with feedback from end devices. Deep analysis of this information can only occur in a powerful neural network. However, at least the autonomy of these devices will significantly increase, as constant connectivity and data exchange will no longer be required.
Explainable AI: This type of AI provides exhaustive explanations for its actions, enabling users to easily comprehend them. The development of explainable AI is particularly crucial in healthcare, where most modern neural networks do not offer clear explanations for why and how they make certain decisions. Without proper training and an understanding of decision-making principles, one cannot be confident that patient diagnoses will be accurate. Consequently, developers responsible for implementing such AI systems will work diligently to ensure systems can explain in detail how decisions are made and what information was used for their conclusion.
AI-Based Cybersecurity: According to expert forecasts, an increasing number of companies will be investing in the use of artificial intelligence in cybersecurity projects. Neural networks will analyze cyberattacks, identify suspicious trends, and recognize patterns. However, it's important to remember that cybercriminals will also employ AI for illegal activities. As a result, this process is expected to evolve into an endless cat-and-mouse game.
Virtual assistants: They are becoming more and more in demand, and the integration of virtual assistants into various domains is gaining momentum. This trend is especially active in hotlines, government services, and healthcare systems. According to Statista, the growth of voice assistants in 2023, compared to 2019, will be nearly 60%. The main goal of this trend is to teach bots to recognize human emotions and adjust their algorithms based on the user's mood. There's also exploration into using bots in the form of holograms or smart screens.

It's worth noting that ethical considerations surrounding artificial intelligence remain of utmost importance. AI is constantly being tested in decision-making processes. In areas like resume processing, medical diagnosis, and sentencing, the practice has shown that errors in training neural networks can lead to incorrect conclusions. This can result in discrimination and improper treatment of individuals. This is especially unacceptable when artificial intelligence acts as a judge in employment, justice, or healthcare matters.

The Importance of Implementing Artificial Intelligence in Project Management Systems

Every year, approximately $48 trillion is invested in various projects. However, according to statistics, only 35% of these projects are considered successful. The remaining 65% of projects that receive resources fail to achieve the desired outcomes, raising serious questions about the quality of their management.

One of the reasons for the poor project management quality is outdated project management systems. The technologies used in such projects are at a low level of maturity. Many organizations and project managers still rely on spreadsheets, slides, and other applications that haven't undergone significant changes over the past decades. In such conditions, these tools prove insufficient when projects and tasks require constant adaptation and changes in the business environment. While portfolio management tools have shown some improvements, they still lack planning, team collaboration, automation, and "intelligent" capabilities.

If the use of artificial intelligence and other technological innovations in project management can increase the proportion of successful projects by just a quarter, it would equate to trillions of dollars in benefits for organizations, society, and individuals.

According to research from the analytical platform Cognilytica, the vast majority of project managers approve the use of AI technologies for project management. Among those who use AI, project productivity has increased by more than 20%.

Currently, artificial intelligence can oversee all five stages of the project lifecycle: initiation, planning, execution, testing, and closure.

The primary advantage of AI is processing large volumes of data, including extensive project documentation over time, as well as performing routine tasks in the background. In essence, AI automates the work of an analyst or an analytical group. According to Gartner's forecast, by 2030, AI could handle up to 80% of routine project management tasks, allowing project managers to free up time for tackling more complex and responsible tasks.

Advantages of Implementing Artificial Intelligence in Project Management Processes

Predictive Capability

Determining task completion deadlines is one of the key aspects of project management. The application of data analysis using artificial intelligence and machine learning helps project managers refine their plans based on past results.

The use of machine learning enables the prediction of the time required to complete a task based on data from previous projects and other factors. Machine learning models analyze information from past projects, taking into account various variables such as complexity, participants' qualifications, and other factors that may impact task completion time. Using this data, the model can predict the time needed to complete a task in the current project. This allows project managers to make more informed decisions regarding resource allocation and prioritization.

With the aid of statistical data and machine learning technology, artificial intelligence can not only provide recommendations but also predict project success, task completion times, project profitability, and overall lifecycle.

Decision Support

Artificial intelligence has the ability not only to process data and automate simple tasks but also to provide recommendations for project managers in managing specific projects.

For instance, machine learning technologies can be applied to optimize the allocation of resources in a project based on resource data, such as time and skill levels of project participants.

Priority Setting Assistance

Task prioritization is a key element in project management, enabling resource optimization and expediting project completion. Artificial intelligence can be used to automate this process. Machine learning models can analyze data from previous projects, such as task completion time, complexity, and importance, to implement the most effective task prioritization system.

Choosing and defining priorities is a form of forecasting, where it's crucial to precisely determine which projects and tasks will bring the most benefit to the organization. With relevant data, AI can identify patterns that might be unnoticeable by other means, greatly surpassing human accuracy in making forecasts.

The application of artificial intelligence in priority setting aids in the following aspects:

Swift identification of projects ready for launch;
Selection of projects with higher chances of success and greater benefits;
Improved balance in the project portfolio and organizational risk analysis;
Minimization of the human factor's influence in decision-making.

Error Prevention and Risk Detection

One of the most advanced areas of project management automation is risk management, and as a result, AI technologies are increasingly being used to prevent errors and detect risks associated with project execution.

New applications utilize large volumes of data and machine learning methods to help project managers predict risks that might otherwise go unnoticed. These tools can already suggest measures to mitigate consequences and, in the future, may automatically adjust plans to avoid certain types of risks. Risks can include technology issues, resource constraints, financial challenges, and other factors that might impact project success.

For risk assessment, completed project life cycles are studied, and based on historical data, artificial intelligence analyzes team interactions, documentation, task flows, reporting, and many other factors. Using this data, machine learning models identify potential risks and alert project managers to possible disruptions at specific stages of the project life cycle, providing recommendations for prevention or management of these risks.

Cost Reduction in Projects

Project managers are well aware of the challenges in maintaining budget constraints. However, research shows that the implementation of AI interfaces for project management contributes to more effective cost control.

Budget overruns often occur unexpectedly and are linked to the inability to anticipate and forecast numerous factors influencing the project lifecycle. The implementation of artificial intelligence helps analyze these factors and offers project managers optimal strategies to stay within the budgetary limits.

Utilizing Virtual Assistants

Recently, Oracle introduced a new digital assistant for project management, which assists users in updating information about task completion progress and time through text, voice, or chat interfaces.

In the field of project management, such tools are referred to as "bots" or "virtual assistants." Most major projects are planning to incorporate the use of chatbots into their strategies in the near future. Over half of managers believe that chatbots enhance project efficiency and team engagement.

Bots excel in handling labor-intensive and monotonous administrative tasks such as data management, creating schedules, maintaining documentation, and generating invoices.

Furthermore, bots will help reduce communication time within teams, interacting with team members through corporate messengers like Slack. Bots can remind colleagues of tasks, inquire about progress status, and request reports.

Trend Identification

In the marketing field, AI can automatically test various marketing hypotheses, enabling the identification of the most effective strategies and tactics.

Artificial intelligence technologies aid in analyzing large volumes of data and identifying trends, enabling the optimization of marketing strategies and predicting future changes in audience behavior.

Changing Role of the Project Manager

For many project managers, the automation of a significant portion of their current tasks might seem daunting, but successful managers learn to harness these tools for their benefit. At this stage, AI still doesn't replace the actual project manager; it can currently act as an assistant. Project managers won't disappear; they shouldn't fear these changes and should take advantage of the benefits of new technologies.

As they gradually free themselves from administrative tasks, the project managers of the future must cultivate strong "soft" skills, such as leadership, empathy, strategic thinking, and business intuition. They should focus on achieving expected outcomes and aligning them with strategic goals.

Conclusion

Some organizations are already incorporating artificial intelligence into their educational and certification programs for project management. And while currently, a cross-functional project team is composed of people, it won't be long before it transforms into a group of both people and robots.

The list of examples provided above does not encompass the growing impact of artificial intelligence on project management. Artificial intelligence frees up time from routine and technical tasks, allowing project managers to focus on complex areas such as communication management, conflict resolution, strategic planning, and others.

However, it is still too early to talk about artificial intelligence possessing consciousness and self-awareness, and therefore, at this stage of development, project management cannot do without human involvement. Furthermore, the application of artificial intelligence in project management is a relatively new endeavor and carries certain risks. It should be noted that even the most well-trained algorithms in artificial intelligence for project management do not guarantee flawless performance. Therefore, employees must regularly monitor the data used by artificial intelligence to prevent potential distortions of results.

It is essential to consider that the implementation of artificial intelligence requires a high level of culture and discipline in project management. Artificial intelligence works with data, so this data must be accurate and provided in a timely manner.

However, despite the risks, the application of artificial intelligence in project management offers vast opportunities and represents the future in this field. The world stands on the brink of creating Artificial General Intelligence, capable of thinking and acting like an ordinary human. According to forecasts, this technology could emerge in the next 10-15 years.

Therefore, right now, artificial intelligence is a primary direction of development for management systems, which can help extract the maximum from existing projects and create new, highly efficient ones. Based on this, leaders of large companies are interested in AI applications to some extent.

Even now, the application of artificial intelligence in project management brings significant benefits not only in automating administrative and minor tasks but, more importantly, helps project managers more successfully choose, define, and implement new directions and tasks.

Published Nov 13, 2023