Organizations require a structured approach for managing projects/programs/portfolios. This approach ensures that projects are commenced, planned, executed, monitored, controlled, and closed efficiently. It helps in assuring that the business goals are aligned to the vision and mission of organizations. To achieve this, tasks must be consolidated, resources must be organized, and schedules and timelines must be set in a way that risk and dependency, assumptions, and performance are optimized. This optimized performance also leads to improved communication and collaboration among team members. Furthermore, working with stakeholders and clients having common objectives leads to increased satisfaction levels.
Business Project Management in the Days of No AI and ML—Methodical Style
Before artificial intelligence (AI) and machine learning (ML) emerged, project managers and their teams handled project updates manually, relying on traditional technologies and processes. There was a certain rigidity in systems that worked in organizations in terms of defining goals, distributing resources, and managing risks. Moreover, cross-team variances in the execution processes also led to inconsistencies in project outcomes.
Let’s see how the project management processes were executed before the advent of artificial intelligence (AI) and machine learning (ML):
Manual Scheduling and Task Management
For as long as Gantt charts, MS Excel, and MS Project have existed, project managers have used them to schedule tasks manually. They have become quintessential staples in the business ecosystem.
Scheduling was carried out by hand, requiring project managers to create detailed plans with precise start and end dates for the milestones, tasks, and subtasks. It was an exhaustive process that was prone to human mistakes, such as wasting time setting up the manual management plan and adjusting all the tasks and timelines, along with being extremely cumbersome.
Consulting Through Meetings and Emails
In businesses, receiving accurate information is crucial to ensure the timely resolution of issues. Project managers/scrum masters used to spend nearly 80% of their time coordinating with stakeholders and teams. They frequently emailed stakeholders with project updates and held meetings with team members and senior management for problem-solving. However, these tasks were tedious and often resulted in lost communication in piles of emails.
Resource Scheduling Efforts in Projects
Effective project management requires efficient resource allocation, particularly in large teams with a broad range of skill sets. Determining the best resource for a task while considering the capabilities and workloads of teams was cumbersome. This challenge was particularly counterproductive in large, multi-regional teams with varying capabilities and diverse skill sets.
Intuitive Management of Project Risks
In most cases, action was only taken once the risks had already emerged. However, it was possible to control certain factors, such as resource burnout and cost escalation.
Manual documentation of risks often came up with challenges such as scope increase, extended work hours, deadline extension, and budget overflow. Without a clear picture, controlling risks with proactive measures remained challenging.
Quality Assurance
Quality control teams, along with the project managers, checked deliverables using their predefined quality assurance (QA) checklists, testing manuals, and peer reviews to ensure consistency. This process was not only tedious but also led to bugs slipping by, particularly in intricate projects. Moreover, manual testing and reviewing were painstakingly sluggish, making quality assurance inefficient.
Conclusion:
Artificial intelligence (AI) and machine learning (ML) tools, algorithms, and models help in making project management smart, productive, and forward-thinking. The use of artificial intelligence (AI) and machine learning (ML) tools enables organizations to boost their business outcomes, speed up their project completions, stick to their budgets, and focus on creating value.
By switching from a reactive to a proactive approach and adopting data-driven risk management, automated resource scheduling, and improved decision-making, project managers have increased awareness of the potential challenges and surged control over them. This enables them to handle tricky projects with less hassle.
As these technologies advance, we expect to see significant breakthroughs in them that will simplify processes, make old-school project management smarter, and lead to quicker value creation.
To Know More: https://academian.com/post/blog/the-significance-of-project-management-ai-ml-transforming-the-future/
The Significance of Project Management: AI & ML Transforming the Future