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Saidas Naik 1 article
Residence: IN Thane, Maharashtra
Project Manager - Artificial Intelligence at Lionbridge

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Application of AI in modern-day Project management

The role of project management has always been critical in any industry despite its complexity & diversity. Gone are the days when the use of spreadsheets for tracking different processes was deemed revolutionary. Experts across industries are now banking on AI as the future of project management, and we can already see the change happening with small components of AI integrated into different knowledge areas of project management.

The level of application, usage, and integration of AI in any project process is directly proportional to the extent to which its operations are data-driven.

The high reliability of AI on data has encouraged stakeholders to improve how it is gathered and used. This direct output provided by its application facilitates making critical decisions; it also helps to cater and connect with external stakeholders efficiently.

Following are a few applications of AI in specific knowledge areas of modern-day project management:

Integration: Preparation of Request for proposals (RFPs) for projects has been a tedious task for project managers; AI can help prepare them instantly based on the previous similar projects done. It will save on time taken to make each RFP. Project charters can be readily extracted just by entering relevant project-specific information in suitable templates.

Scope: AI can be used to define a Work breakdown structure (WBS) of the project work based on the established guidelines. It helps to validate the scope against previously undertaken similar projects. It makes the execution of project plans relatively easy for the team.

Schedule: Defining, sequencing, and estimating activity durations takes a lot of effort; AI can help us with optimizing the activities and flag risks involved. The advanced crashing of activities/events is possible by using a knowledge-based expert system (KBE) that has a knowledge base and an inference engine. It provides insights utilizing a series of IF-THEN statements and allows project managers to make crucial decisions.

Resource: Resource allocation can be done based on scope, schedule, and pre-defined skill set required to perform the project activities. The system can automatically calculate the number of resources in place. It helps identify the risk of workforce shortage in the middle of the project as it progresses.

Cost: One of the most significant constraints of any project in an organization is the value it generates in terms of quality and revenue. Cost becomes extremely important to control. Budgets are determined based on the comparable data available for other previous similar projects. Complex AI concept like Artificial Neural Networks (ANN) is used by organizations (especially construction projects) to predict cost overruns based on parameters like project size and contract type.

Risk: Project risks are underrated aspects of project management that safeguard the project interests of stakeholders. AI is used to identify potential risks well ahead of time with the help of specific flagging algorithms, and the system suggests tailored risk responses based on qualitative and quantitative risk analysis using risk parameters. Stakeholders can choose appropriate suggested actions based on their understanding and viability of the recommended solution.

Communication: This area, as a backbone of the entire project, is evolving with chatbots taking over the role of project assistants. When beautifully designed chatbots start using advanced algorithms to perform predictive tasks, then we can say that the AI has integrated with project management in real sense.

AI has the potential to reduce uncertainty substantially in many areas of project management. The only thumb rule is that the data driving this phenomenon must be of top quality, even the quantity of data matters; the more, the better. Different entities in the markets focus on producing project management tools that use Machine learning to provide process output. As of now, customization is costly as it involves an advanced workforce to develop AI-enabled tools.

Experts have predicted that the role of human intervention in project management with AI would drastically evolve from analysis to barely judgment. It will be disruptive, but only when extensively used in every aspect of project management.


Published at pmmagazine.net with the consent of Saidas Naik