Improve Scope and Change Management in Agile Projects
Introduction
Managing scope and handling changes are ongoing challenges for project managers, especially in agile projects which embrace changing requirements. While agile methodologies are flexible, uncontrolled changes can still lead to delays, cost overruns, and missed expectations. This paper examines common issues around scope and change management in agile projects, and proposes how artificial intelligence (AI) and machine learning (ML) technologies can help address them.
Literature Review
A systematic literature review identified 18 common challenges in seven categories: people and organization, user requirement prioritization, over-scope requirements, tools and processes, product backlog, communication and coordination, and culture and behavior. Best practices for resolving the issues focused on roles, responsibilities, communication, prioritization, documentation, and tools. However, few studies explored how emerging technologies could augment traditional approaches.
Role of AI and ML
AI and ML open new possibilities for enhancing agile scope and change management practices:
- Natural language processing (NLP) can analyze written requirements, documentation, emails, and meeting notes to automatically identify, categorize, and prioritize changes. This assists with planning, tracking progress, and managing expectations.
- Computer vision applied to video/screen recordings of meetings and demos can extract visual cues like body language and on-screen interactions to gain additional insights into stakeholder priorities and reactions to changes.
- Pattern recognition with ML algorithms can detect common traits of past successful vs problematic changes based on attributes like size, dependencies, stakeholder alignment. This informs impact assessments of new changes.
- Interactive virtual assistants using NLP and ML help socialize changes across distributed, multidisciplinary teams by answering questions and facilitating discussions in a consistent, transparent manner.
- Simulation and what-if analysis powered by ML predict outcomes of proposed changes, flag risks, and suggest mitigations to better plan iteration schedules and budgets.
- Automated documentation and version control using NLP and computer vision maintains living product descriptions, requirements, and histories that are always up-to-date for new team members.
Benefits and Conclusion
Leveraging AI/ML in these ways can reduce workload burdens on project managers, make insights more robust and transparent, and facilitate faster, data-driven decisions around scope and changes. While technical and process challenges remain, AI/ML show promise to strengthen agile practices and outcomes by augmenting, not replacing, human managers and teams. Future work involves developing and testing prototype AI/ML-powered tools in real-world project environments.
References:
Agile project management challenge in handling scope and change: A systematic literature review