Discussion response
saalecynthia123
PMAN638WK9RESPONSE.docxPMAN 638 QUESTIONS
QUESTION:
3) Look for articles that discuss the relevance of artificial intelligence (AI), machine learning (ML), and cybersecurity in project management. Here are a few to get started:
· https://www.forbes.com/sites/cognitiveworld/2019/07/30/ai-in-project-management/?sh=f113059b4a00
· https://pmtips.net/article/what-project-managers-need-to-know-about-cybersecurity
How do you envision projects of the future using AI and ML to get the right information, alerts, and reports to the right stakeholders at the right time in the most effective manner and tailored exactly to the needs of each stakeholder - all with little, if any, human involvement? And what might the project manager of the future need to do to ensure that these AI and ML "bots" are not hacked to deliberately create malicious and/or misleading information/reports that might impair project progress? Finally, how might Stakeholder Engagement change if and when AI and ML capabilities like these are common?
RESPONSE 1. (Bradley)
AI and ML are going to be instrumental in the years to come. We are already seeing AI and ML to identify trends, outliers, and other important factors within the world of healthcare. Within my own work, I have previously engaged with clinical informatics teams and primary data sourcing teams that regularly utilize AI and ML throughout their everyday responsibilities. However, both of these decision-making and data analysis tools do come with their challenges.
Part of the challenge of machines is that while they can do A LOT within a small period of time, they are only capable of doing what they are programmed to do. Part of the challenges with integrating AI and ML into normal every-day processes is making sure all of the data is correct. More specifically, a lot of larger corporate firms are working with years, if not decades, of data and are likely working with legacy systems that are not built to filter data, maintain data, and establish a good quality master data source. In my experience, my work has multiple silos of databases that are structure different, run off of different legacy systems, and most importantly, do not communicate particularly efficiently. Implementing AI or ML to read, assess, and make decisions from that data is no small feat. While studies have been shown to utilize ML to ensure data integrity moving forward, the old data can serve as a significant barrier of entry (Anwar, Mahmood, Ray, & Tari, 2020).
To summarize this point, a project manager would need to ensure that all data factors related to each stakeholder such as timeline, time zone, responsibilities, values, etc. are all accurate. There is little to stand in between of a ML or AI program and incorrectly inputted data. In some instances, it may be easier to approach a project with an already established team rather than spend the resources cleaning up the old data or re-configuring a system to work with a new AI or ML process.
Secondly, AI and ML may be more applicable in scenarios where there is less human interaction. Automated processes and projects that require a lot of manual man power without input from multiple stakeholders may be a good example of a project type that could benefit from AI. However, projects or businesses that require a “human” element may be more difficult to implement. Scenarios such as healthcare assessments, where body language, tone, etc. are important factors may be much more difficult to use AI and ML. There are many instances in the healthcare world where our project variables involve non-tangible factors that would take multitudes of the resources needed for a traditional team to implement an AI or ML process.
Lastly, both security and stakeholder engagement are significant factors that need consideration. It is impossible to hack a human brain, while it is very possible to hack a database. Dependent upon the security or privacy of the data being handled, traditional team structures may be more appropriate to avoid data leaks. Also, studies have shown that stakeholder engagement decreases with the implementation of AI (Prentice, Weaven, & Wong, 2020). Overcoming the hurdle of stakeholder engagement with AI or ML implementation is a significant barrier of entry that needs to be considered. As mentioned above, factors such as body language, tone, and other factors received from in-person interaction are important factors that can only be picked up (at least for now) via person-to-person interactions.
To close, AI and ML has its place within project management, and as technical development increases, it is likely to become more and more utilized in the world of project management. However, there are many factors that may interrupt the implementation process and serve as significant barriers for future use when considerations of human elements and cost are considered.
References:
Anwar, A., Mahmood, A., Ray, B., Mahmud, M. A., & Tari, Z. (2020). Machine learning to ensure data integrity in power system topological network database. Electronics, 9(4), 693.
Prentice, C., Weaven, S., & Wong, I. A. (2020). Linking AI quality performance and customer engagement: The moderating effect of AI preference. International Journal of Hospitality Management, 90, 102629.
QUESTION:
1) Work Performance Reports are described in small sections of Chapters 4, 9, 10, and 11 of the PMBOK Guide. Review all of these sections and provide a summary explaining what are work performance reports. Also discuss the potential content and audience for these project reports. Provide an example of how you would use these in your workplace (preferably in a project setting if you work with projects).
RESPONSE 2 (JENNY)
Work Performance Reports
Work performance reports includes information relating to work performance data such as key performance indicators, and technical performance measures (PMI, 2017). Reports consolidate and disseminate the information so that is available for decision making and taking required actions within the project. Progress and status reports are two examples of work performance reports. They aid in managing teams by providing forecasting information on resource needs and help to identify team members eligible for recognition and rewards. Work performance reports also serve as a critical input for project communication. Additionally, work performance reports contribute to monitoring performance related risks within the project and evaluating the effectiveness of project risk management (PMI, 2017).
In the project I am currently working on, work performance reports could be used to support implementation and enhance communication within our department. Work performance reports would help to inform the internal processes and policies we develop as we implement the new project management tool. Since the work performance report includes project progress, we would have a better timeline and understanding of where the project stands. This would allow us to identify the critical points in the project life cycle and have our implementation guidance prepared with this foresight. Also, we would be able to provide substantive updates to our department on a continuous basis, which would significantly support change management efforts.
Project Management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide)–Sixth Edition: Vol. Sixth edition. Project Management Institute.
