MITS5001 IT Project Management 2

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MITS5001 IT Project Management 2

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MITS5001 IT Project Management 2

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Course Code: MITS5001
University: Victorian Institute Of Technology

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Country: Australia

Topic for the assignment is Towards effective AI-Powered Agile Project Management.
This assessment item relates to the unit learning outcomes. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter.
Learning Outcomes
1. Critically analyse project parameters and identify the key processes from the available project management book of knowledge in practical case scenarios.
2. Carry out research studies to provide expert judgement on the project progress and strategize contingency and fall-back plans to ensure project deliverables are met as planned.


Artificial intelligence refers to the imitation of intelligence of humans in machines those are programmed to think like humans and imitate the actions of the humans. The utilisation of artificial intelligence in project management is on the rise and it will help the managers of the project to take decisions those are smart and effectively administer the constraints of the project. The artificial intelligence in project management assists in order to uncover insights, automate various tasks and understand the important parameters of project performance.
The report deals with the effective AI-powered agile project management.  This report discusses about the ways artificial intelligence can leverage the agile project management.
Agile Project Management
The project management of agile is an approach to guiding and planning procedures of project, those are iterative. The main benefit is agile is its ability to respond to the issues as they happen all through the project. Making appropriate change to a project at appropriate time can save the assets and provide the clients a successful project within the budget of project and on time [1]. The agile project management splits projects into little components those are finished during the work sessions. It runs from the stage of design to testing and assurance of quality. These work sessions are also known as sprints. The sprints are usually short and they are two to four week long. The methodology of agile enables various teams to release various segments, as they are finished. This continuous release of schedule permits for teams to show these segments are appropriate and if these are not appropriate, then the issues will be solved quickly. This helps to minimize the failures, as there are continuous enhancements throughout the lifecycle of project. The project management of agile do not require a manager of project but a project manager is important for the success in the conventional methodologies of management of project such as the waterfall model [2]. The roles and responsibility of manager of project in management of project of agile is distributed among the members of the team. The agile project management methodology is utilised for the development of software.
Challenges of Agile
There are various challenges in the project management of agile and the teams of agile need to come up with the solution to mitigate the challenges of the project management of agile [3]. The challenges are as follows:

Reluctance to change: the common problem when transferring to agile is breaking the work habits of the people. The project teams those are used to work with traditional approach of project management can have a tough time in order to switch to agile project management. The leader, managers of the project teams all required to be ready in order to grip new ideas for agile in order to become effective and efficient.
Lack of experience: the lack of experience is one of the common challenges in agile project management and if the team has no experience then implementation of agile can be tough [4]. Training is no enough and the organization requires being open in order to make some changes. The agile team must have an experienced scrum master that can help the team to use the agile principles in a project.
Lack of team ownership: the aim of agile is to assist the members of the team take ownership of their job. For the agile to work appropriately, the members of the team require learning to take ownership of their job and make effective decisions without any instructions.
Poor communication: in the agile project management, communication is very important. If the members of the tem cannot communicate among each other, then the project can be harmed [5]. Communication is easier when the teams work in the same office but when the team are distributed, then it becomes hard for the teams to communicate among each other.
Recognising the items of backlog: the items in the backlog of the product can be derived from various sources such as the specification of the requirement, new functionality requests from the clients, bug fixes, discussions among the teams of agile and many more [6]. If is very difficult for the teams of agile, especially for the owners of the product in order to process large amounts of data to recognise and develop new items for the backlog of the product.

AI Powered Agile Project Management
Artificial intelligence has large potential in upgrading and speeding up the preciseness of the development procedure of software. It has an essential contribution in the development of the software and AI focus in maximizing the effectiveness and the efficiency of the project [7]. Concerning these advantages, the organizations are interested in order to invest in the artificial intelligence to upgrade the profitability. The benefits of AI in the agile project management include:

Prototyping: before artificial intelligence, it took large amount of time in order to convert the requirements of the clients into technology. With the advent of artificial intelligence, it minimizes the time of development and finishes the procedure efficiently.
Estimation of risk: In the development of software, while taking essential decisions on the estimation of risk is very complicated and factors in constraints of budgeting and scheduling [8]. After the project begins, the external environment and inter dependencies develop scenarios of probability. Artificial intelligence permits to collect the parameters of data. With the assist of model of artificial intelligence, the managers of the project can collect data of project from beginning to the finish dates. In this way, the project manager can get a schedule that is realistic for the development of project.
Error handling and analytics: the assistance of coding those are based on artificial intelligence easily recognises the patterns in the data and the errors of the humans. During the creation, if an error is made the assistance of coding will help flag it. After the implementation of the application, artificial intelligence can be utilised in order to evaluate the flag and then find out the errors that can be fixed. In future, the artificial intelligence will correct the errors in the application without the involvement of the errors.
Assistance of coding: Most of the developers spent their time on debugging the code and documentation. With the introduction of code assistants, those are smart with embedded artificial intelligence, the developers can retrieve feedback and the recommendations those are based on code [9]. The examples of code assistants are python kite and java codota.
Strategic decisions: the developers spent time for prioritizing and then discussing the features of the product. An artificial intelligence model  those are trained with the data of previous projects of development can evaluate the  way application will execute, help the teams of engineering in identifying the increased impact and minimum risk
Refactoring of auto coding: it is important to make clean code than protected collaboration. Refactoring is needed to maintain a sanitary code. In order to mitigate this, artificial intelligence is utilised in order to evaluate code for better outputs.
Resource management of project: Providing any project of IT depend on the people those are working on the project. With the combination of artificial intelligence into various projects, real-time information about the developers working on various projects can be obtained. It provides accurate information about the developers who are there for the implementation. Based on integration of artificial intelligence, the number of developers can be maximized or reduced for a project [10]. Based on the structure of the project, artificial intelligence will allocate developers and execute the project by giving required knowledge and skills. Artificial intelligence helps to deliver the project fast.

With the advent of artificial intelligence, it helps in order to identify the pattern in the data and it is essential for the making of decisions. The algorithm of machine learning contrasts the data with the database and then makes appropriate decisions.
The system of artificial intelligence should have the following features [11]:

Analytics engine: the engine of analytics offer decision support through the analytics those are predictive, descriptive and prescriptive.
Planning engine: the planning engine an engine where the problem is planned and the state of the goal is specified in the goal of the sprint.
Optimisation engine: this engine assists in a role that is supportive, the engine of planning execute and calculate the sequence of actions in a situation.
Conversation dialog engine: this engine assists and works with the teams of agile. It is a type of chatbot that operates as an interface between the end users and the other part of the system of AI.

From the report, it can be inferred that artificial intelligence can be utilised in project management of agile in order to successfully develop software. The agile project management is an important methodology in project management and it follows iterative method in order to develop software. The project gives a discussion of the project management of agile. It is different from other conventional methods of project management. The report discusses about the challenges of agile project management. There are various challenges of agile project management that should be solved by the team of the project. With the advent of artificial intelligence, it can be easily utilised in the agile project management in order to make decisions and successfully implement the project management.
P. Serrador and J. Pinto, “Does Agile work? — A quantitative analysis of agile project success”, International Journal of Project Management, vol. 33, no. 5, pp. 1040-1051, 2015. Available: 10.1016/j.ijproman.2015.01.006.
A. Rasnacis and S. Berzisa, “Adaptation of Agile Project Management Methodology for Project Team”, Information Technology and Management Science, vol. 18, no. 1, 2015. Available: 10.1515/itms-2015-0019.
S. Mukhopadhyay and R. Gupta, “Reviewing Commonalities between Agile Software Development Methodology and Grounded Theory Methodology”, SSRN Electronic Journal, 2019. Available: 10.2139/ssrn.3326376.
D. Ciric, B. Lalic, D. Gracanin, N. Tasic, M. Delic and N. Medic, “Agile vs. Traditional Approach in Project Management: Strategies, Challenges and Reasons to Introduce Agile”, Procedia Manufacturing, vol. 39, pp. 1407-1414, 2019. Available: 10.1016/j.promfg.2020.01.314.
H. Pussella and A. Bandara, “Exploring the Challenges in Transitioning from Traditional Project Management to Agile Project Management”, Peradeniya Management Review, vol. 1, no. 1, p. 17, 2018. Available: 10.4038/pmr.v1i1.25.
G. Koi-Akrofi, J. Akrofi and H. Akwetey Matey, “Understanding the Characteristics, Benefits and Challenges of Agile it Project Management: A Literature Based Perspective”, International Journal of Software Engineering & Applications, vol. 10, no. 5, pp. 25-44, 2019. Available: 10.5121/ijsea.2019.10502.
J. Shah, “Artificial Intelligence in Project Management”, The Management Accountant Journal, vol. 54, no. 3, p. 34, 2019. Available: 10.33516/maj.v54i3.34-37p.
M. Lauras and T. Comes, “Special Issue on Innovative Artificial Intelligence Solutions for Crisis Management”, Engineering Applications of Artificial Intelligence, vol. 46, pp. 287-288, 2015. Available: 10.1016/j.engappai.2015.09.002.
J. Mihajlovi? Mili?evi?, F. Filipovi?, I. Jezdovi?, T. Naumovi? and M. Radenkovi?, “Scrum Agile Framework in E-business Project Management: An Approach to Teaching Scrum”, European Project Management Journal, vol. 9, no. 1, pp. 52-60, 2019. Available: 10.18485/epmj.2019.9.1.7.
J. Shah, “Artificial Intelligence in Project Management”, The Management Accountant Journal, vol. 54, no. 3, p. 34, 2019. Available: 10.33516/maj.v54i3.34-37p.M. Haenlein and A. Kaplan, “A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence”, California Management Review, vol. 61, no. 4, pp. 5-14, 2019. Available: 10.1177/0008125619864925.

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