Introduction
Project management experiences a wide range of constraints that can potentially hamper the completion of a project. Potentially, constraints like scarcity of resources, uncertainty in completion time, and sometimes complexity of the project create distortions in schedules made on the completion of the project (Zehir et al., 2020, 90). The quality of the management also plays an important role in the success of a project. Some processes, such as inventory management and quality check-ups, can be done in a better way by the use of technology. Artificial intelligence has shown a potential rise in providing potential project management transformation into a simplified task despite the complexity arising (Grundy et al., 2019, 43). Further, when utilized properly, AI has helped managers automate repetitive processes, making them more efficient. The globalization and the mounting pressure caused by population rise have led to recent megaprojects. Further, these megaprojects are gaining complexity as time goes by. For instance, the California High-Speed Rail, targeted for completion in 2033, will ease transportation around California (Surya 2015, 2332). Such complex projects have relied on AI in several passes and processes to ensure its continuity.
Literature Review
Project management has previously involved the provision of schedules and following procedures in military precision. In the modern era, project managers look for more than precision but wholesome project management, which involves evaluation and monitoring for ultimate success (Grundy et al., 2019, 1). The team structure is a key component in the success of a project. However, decision-making and solving problems compute the worth of a project. Nevertheless, the planning, organizational processes, and work prioritization are focal in any project. Most project managers have incorporated AI in a majority of these processes. The application of AI in project management has carried along several consequences that have impacted the quality of the projects. Manufacturers of software and other Artificial Intelligence services aim at lessening the timeline it takes to complete a task, reducing the costs, increasing the efficiency and effectiveness of the processes employed in the projects, and ultimately making a project that is modernized and universally accepted.
The incorporation of AI in project management has been on the rise, as previously, managers have adopted machine learning and incorporated it into their projects. Much data has been processed based on Laney’s 5Vs of velocity, Volume, Variety, and value it accrues, which may not be identified easily by the naked eye (Grundy et al., 2019, 52). As such, project managers have incorporated AI to perform those tasks easily with fewer limitations. Further, with daily technological inventions, AI continues to evolve, and much labor cost is saved as machines can work tirelessly. On the flip side, this leads to job losses due to human labor replacement. However, the goal of the project manager is cost reduction to the minimalist and having a larger return on Investments following the right schedule.
Research Question
This research paper will aim to provide answers to the following question. What are the impacts of Artificial Intelligence on project management?
Methodologies
This research paper employs a quantitative research method to identify the impacts of AI on Project Management. Further, this study provides an outline of previous studies conducted on the usage of AI in project management. As a result, the data used illustrates the progress levels of AI incorporation in project execution and their importance. Further, most journal articles provided databases that provided information on AI, with the exclusion of journals published before 2015.
Impact of Artificial Intelligence on Project Management
Artificial intelligence is a critical component in project management which is elemental in most projects in the modern era. This section highlights the impacts of AI on project management.
Predictive Analytics
Technological evolutions have brought solutions that have increased the precision of the goals needed for completion. In the retail sector and other companies, the goal is to optimize stock management and ensure there is traffic flow in and out of business. Further, companies involved in projects that take a long period have engaged in predictive artificial intelligence (Zehir et al., 2020, 269). Predictive AI has the ability to simulate and predict many processes in a project. For instance, in a profit-oriented project which includes sales, a manager can use AI to showcase possible profitability in the future. Further, the incorporation of possible risks is made possible through the use of AI. A project forecast is yielded based on the past, present, and future of the market in which it operates. AI uses data in large volumes, which it processes to form definite data structures which are easily interpreted. Such structures include graphs, tables, and figures. Additionally, simulation is instrumental in predictive AI, which simulates the future of a project, thus enabling real-time scenario that is easily interpreted (Auth, 2019, 31).
Predictive AI algorithms have been instrumental in optimizing the majority of project aspects. Companies like Microsoft and cisco have adopted predictive AI to perform predictive sales and usefulness of their future inventions (Henriksen et al., 2020, 13). Production models are optimized through advanced predictions, which provide a futuristic insight into a project’s success or failure. As such, project managers make investment decisions based on predictions. Further, strategies are formulated based on the foreseen future of the project. Project managers who use AI do not rely on paperwork prediction but rather have tangible evidence that guides them from the predictive AI. Nevertheless, AI does not make general predictions but formulates predictions based on the objectives and constraints it observes through distinguished and deconstructed data that yields information.
Big Data Analysis
A project’s success is based on data analysis which forms the integral backbone of a project. Commencement, progress, and completion of a project depend on the data provided to the management. As a result, it is crucial to have timely data to determine the course of a project. The development of a machine that can reason and have a human-like simulated performance has been focal of the daily performance of companies, businesses, and organizations (Surya and Lakshmri, 2015, 1). Big data is a collection of data collated from different sources in raw form but yet to be processed into meaningful information. Computer software and machinery have been developed to work hand in hand with big data as they are symbiotic. Data collection technology exists on many platforms, which makes it easily applicable and utilizable. Additionally, AI has also been used to collect information. However, most researchers prefer data collection by humans without the use of technology to capture feedback through non-verbal cues of the interviewees (Ebel et al., 20221, 178). Most researchers only use AI for data collection as backups.
Artificial Intelligence tools are largely instrumental in processing data collection and storing processed information. Big chunks of data have been processed into information through the use of AI. Project managers have adopted AI to process data into meaningful information. For instance, in the construction industry, managers have utilized the use of AutoCAD, Vectorworks, Archicad, and other software which are used for architectural drawings (Ebel et al., 20221, 140). This technology has eased the work of project managers and their teams as they can detect anomalies at a time when little or no damage has been done. Further, with such technology, changing a plan is made easy without loss as a simulation is provided. Data processing has been made easy courtesy of AI. For instance, most supermarkets use point of sales to keep their sales record and inventory data. Getting specified information like sales on a specific day, profits made, or reorder points and levels is usually a click away. Ultimately, artificial intelligence remains key in big data analysis.
Insightful Automation and Cost Reduction
Project implementation and inception involve a series of steps and activities which culminate in the project’s final success. Big projects involve a lot of workers, resources, and tools that must be accounted for to the maximum (Hoffman et al., 2020, 39). As such, there are easy processes in a project that machines can do in a better way. For instance, role-calling workers in an institution with over 1000 workers are tedious. Similar to maintaining their profiles and ensuring all are in their working stations. Further, some precision processes like paint mixing in a car manufacturing industry require uniformity which human efforts cannot achieve. Project managers who incorporated artificial intelligence have achieved such precision, which has provided uniform production (Tsai et al., 2015, 10). With such processes being repetitively done, AI has made it possible to have such processes automated, which saves on production time. Nevertheless, a process carried out over time repetitively by human force is subject to alteration. Further, machines using AI have uniform rates of work which reduces wastage. Therefore, it is prudent to say that AI has led to the automation of many processes in projects which have resulted in efficiency and high output.
Management work involves overseeing the planning, controlling, organizing, and leading staff to project success. The success of a project is not only measured in terms of completion but also in the efficiency and effectiveness of the project’s process. As such, the use of artificial intelligence has led to a reduction in the costs of a project. Further, due to the constant rate of work by machines with AI and their uniformity, most project managers experience a low cost of production using them compared to when using human labor. Costs, resources, progress, and time are easily monitored using AI and ensure projects are completed in the scheduled time. Further, using AI as a predictive tool provides a platform for effective risk prevention and mitigation processes, reducing project contingency funds (Tsai et al., 2015, 10). When AI is used to predict productivity rates, gradual optimization can be done to increase productivity bit by bit. Consequently, AI usage will lower the costs of running a project, increasing the returns on investments (ROI) and increasing profits, which is the goal for each project manager.
Statement of Limitations
This study experienced some limitations which may alter the quality of the discussions conducted herein. However, the research aimed at providing the most accurate and up-to-date information which is factual. This study entirely depended on secondary data, which was conducted prior to this research. Therefore, some of the information may not be timely and in line with the current progress. Artificial intelligence is an evolving faction, and such timely information should be collected in real-time. However, the past information is essential as it provides insights into the development that has occurred in the field.
Further, using secondary data reduces the accuracy of the study as the previous researchers had different objectives in mind when they conducted their research (Hoffman et al., 2020, 54). As such, their sampling, data collection, and analysis methods may have pointed to different results, which may contribute to biases in their study. Therefore, it may be had to detect biases in peer-reviewed reports as the writers use words to eliminate the biasness. Consequently, it may bring a controversy between the data in this research. However, an effort has been made to ensure the information provided in this research is bias-free and contains the timeliest information available.
Conclusion
Artificial intelligence is proving to be pivotal in the modern era as challenges are increasing. The modern challenges are calling for modern solutions. In project management, the complexity level is increasing, reducing the complexity of most projects. As such, project managers have adopted AI and incorporated it into their projects for the achievement of their goals and cost reduction. Based on the above examples in which AI has proved it is focal in project management, managers should look for a way to incorporate AI in different segments of their projects. The incorporation will provide quality and quantity over a short period and under limited resources. AI is the modern way to go for megaprojects’ survival.
References
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