Artificial Intelligence in Enterprise Digitalization – Project Management in Corporate

Abstract

Project Management usually involves many methodologies, among which the most commonly used are Kanban, Waterfall, and Agile. These approaches involve planning, organizing, resource management, and task tracking to meet project deadlines and goals. The project managers need to play a vital role in all these activities, which consume managers' bandwidth. There are tools used to track these processes, but these further involve managers' manual efforts. Additionally, project managers need to make a note of the team’s capacity on a sprint basis. All the PBIs need to be managed manually for the estimation, capacity of work, and completion of work, which again involves manual effort. If there is any drop in project management, then the delivery would be delayed, and the deadline would be missed off which means project management plays a key role in all organizations across all projects.
The Burndown chart in the tracking process helps to make sure that the progress is on track and there is no deviation from meeting the goals on time, which also requires manual efforts from project managers. The chart shows the hours burnt by each resource across the board and also the entire team's efforts burnt. But, still few of the PBIs are not completed within the sprint and get carried forward, which is due to a lack of data collection, documents, quality of work involved, time time-consuming. To overcome all these, organizations started using Artificial Intelligence in project management. AI has proven to be superior to human decision-making in certain areas. Artificial Intelligence is better than humans at finding and enacting the best policies in certain areas concerning science, engineering, and complex societal macro-economic issues.
The thesis shows the integration of project management with an AI-enabled project management tool and also the training framework to train employees on AI in project management. The data is collected from various participants on how traditional project management happens, what the drawbacks are, and areas it has failed through a survey. The research work elaborates on the use of Artificial Intelligence in project management with information from a survey that was collected from project managers or resources working with AI in project management. The data is also collected using a literature review approach and performing a comparative analysis. The thesis uses a hybrid model approach along with agile methodology, which improves the delivery and avoids the tasks being carried forward further. The Top-Down model sets clear goals, budgets, and timelines with AI to ensure these goals are aligned with organizational objectives, predict potential risk, and identify resource allocation gaps. While the Bottom-up model allows the team in decision-making that supports accuracy, estimations, real-time performance, and also communication with the management. During the planning, all the documents need to be uploaded to the board, which is accessible to everyone. Each document needs to be updated with the full details required to complete the task within the sprint. It needs to be ensured that there is no requirement gap left behind, which is a major cause for the task to be carried forward to the next sprint. The estimation for each task is done based on the previous sprint data using Artificial Intelligence, which will avoid wrong estimations. The stakeholders need to be communicated in prior with the full demo of the new functionalities, which in turn will avoid re-work and hence the time is saved. The Artificial Intelligence-enabled project management tool will provide automatic tracking of the efforts, status change of the task, and send us a notification via email for better planning, for which managers need to be well-trained in using the Artificial Intelligence-enabled project management tool. Resources are not familiar with Artificial Intelligence and would need to be trained accordingly. All the task needs to be updated with details, which will be useful when there is a need to get the summary of the associated task as well. The responses from the survey show that AI-enabled project management is more efficient than manual project management, like Jira, Azure DevOps, etc., in many ways. The bugs need to be logged in a project management tool and should be associated with the corresponding task on the board. The bugs will be automatically tracked by an Artificial Intelligence-enabled project management tool. So, managers can just get the report of the bugs, which will reduce the manual effort spent on making notes of all the bugs associated.
In addition, if the resources are not aware of Artificial Intelligence, they need to be trained by organization by following ADDIE model with the 10-step framework mentioned as Identify Training Needs, Basic AI Concepts, Technical Skills Development, Data Literacy Training, Practical Application, Feedback and Support, Ethical and Responsible AI Use, Continuous Learning, Integration with Existing Workflows, Evaluation and Assessment. Based on the training provided, the resources will be able to gain more knowledge on Artificial Intelligence.
KeyWords: Artificial Intelligence in project management, Hybrid model in project management, Agile Methodology.