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Project management isn’t confined to software engineering; it originated in non-software industries like manufacturing and evolved to encompass software development. The process can involve building new products, enhancing existing software, or integrating different solutions. Over time, various models emerged, including the traditional waterfall and agile methodologies, with hybrid approaches adapting to project requirements and complexity. Projects can be categorized based on their focus, whether on research, cost, or time constraints.

In recent years, AI has entered the realm of project management. AI in project management is useful in handling data-driven decision-making and tasks such as progress tracking, cost management, and scheduling. Enhanced with AI capabilities, platforms like Microsoft Project can streamline tasks like resource allocation and dependency management.

GS Lab | GAVS conducted a webinar, Everything Products Episode 9: Predicting the Unpredictable : Unlocking Project Management Secrets Using AI, with Ms. Anaya Bakshi Associate Director Engineering, GS Lab | GAVS and Rahul Bidwe Associate Manager Engineering, GS Lab | GAVS as the panelists.

Understanding Project Management

Project management involves strategically coordinating skills and techniques to accomplish tasks, planning, organizing, and overseeing projects to meet specific goals. Project managers, seen as stewards of projects, must create a collaborative team environment, engage stakeholders, focus on project values, and navigate complexities while optimizing risk responses. Adaptability and embracing change are critical, along with adhering to PMI’s 12 principles and four values: responsibility, respect, fairness, and honesty. This role goes beyond technical expertise, requiring strong leadership and multifaceted skills.

Traditional ML vs. Emerging Generative AI

Traditional machine learning typically involves analyzing historical data to identify patterns and make predictions based on statistical analysis. In contrast, generative AI, particularly evident in recent advancements like OpenAI’s GPT, focuses on generating new content based on the input data it receives. Generative AI’s strength lies in Natural Language Processing (NLP), making it adept at understanding and producing human-like text.

While traditional ML has been effective in predictive analytics, generative AI expands its capabilities into face recognition, emotional recognition, and sentiment analysis. AI, particularly generative AI, enhances and generates new content based on input data, offering potential for innovative applications across various domains.

Importance of AI in Project Management

AI can quickly analyze numerical and statistical data, identify anomalies, and provide insights for informed decision-making. Additionally, AI can assist in budgeting, financial planning, and identifying key cost drivers, especially in rapidly changing technology domains. However, there is also a need for human intervention in risk management and project planning, as AI is still evolving in these domains. AI presents promising opportunities to enhance project management efficiency and effectiveness, but human expertise remains crucial in certain critical aspects of project management.

Uses of GenAI in Project Management

Generative AI can assist project managers in areas such as the following:

Automation: AI can handle simple tasks like summarizing meeting notes or generating standard reports with minimal or no human intervention. This saves time and ensures accuracy in routine tasks.

Complex activities with human intervention: In tasks such as cost-benefit analysis, scheduling plans, or risk analysis, AI can provide initial drafts based on prompts. Project managers can then review these drafts, incorporate expert opinions, and make necessary adjustments according to project requirements.

Augmentation for complex tasks: In more intricate activities like building a business case with multiple interdependencies and variables, AI can assist in providing initial data and supporting with interdependencies. While the bulk of the work still requires human decision-making and expertise, AI can streamline the process by handling repetitive tasks and providing valuable insights.

Leveraging AI in Product Engineering

AI, particularly generative AI, can streamline complex tasks in product engineering, particularly in tools like Jira. AI simplifies and accelerates various aspects of product engineering, significantly reducing the time and effort required for project management activities. Here are some use cases:

Seat allocation in hybrid working models: With limited seats and rotating teams, AI can analyze statistical data and generate multiple scenarios to allocate seats efficiently, reducing conflicts and simplifying decision-making.

Portal development in Jira: AI can generate epics and user stories in Jira based on input data, streamlining the portal development process. It creates user stories for database population, UI design, and backend development, reducing the time and effort required for brainstorming and planning.

Release planning: AI identifies interlinked stories and creates release plans, determining when minimum viable products can be released to consumers, thereby optimizing release management workflows.

Test plan creation: AI reviews each story and generates test plans, highlighting stories with lower testing coverage. Thus, comprehensive testing coverage is ensured without the need for manual intervention.

Reporting: AI analyzes data to create velocity reports, release-based burndown charts, and engineering-based performance reports, providing insights into project performance and reducing the time spent on manual reporting tasks.

This blog offers only a high-level gist of the webinar. However, you can watch the entire discussion here. For more such videos, pl. visit and

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