A project comprises a series of tasks designed to meet a specific objective. It can be developing a new product/service, constructing a bridge or building, house renovation, upgrading the data system, implementing new business, etc. All business people want to find a way to manage their projects effectively to boost the business’s productivity, and the only solution is to use the methodologies of project management.
AI is a field of computer science dedicated to solving problems that otherwise require human intelligence—for example, pattern recognition, learning, and generalization. Machine learning is a subset of artificial intelligence that uses statistical techniques to give computers the ability to learn from data without being explicitly programmed.
Artificial Intelligence (AI) and machine learning have found a wide range of business applications. The most important capabilities of AI, that we already know, are processing large amounts of data quickly, finding patterns in data, learning from it and making predictions. Due to its unique ability to monitor specific patterns and forecasting project scenarios and outcomes, AI is and will continue to be an emerging trend in the project management area.
As per PMBOK guidelines, there are five different steps in project management: Initiation, Planning, Execution,
Monitoring and Control, and Closing. Project planning is an essential component of project management. It establishes the project scope and defines the objectives to achieve them. A project plan enlists how the project will be executed, monitored, controlled, and closed. The plan must include every project constraint, such as the costs, risks, resources, and deadlines.
AI-based tools assist project managers in handling different tasks during each phase of the project planning process. It also enables project managers to process complex project data and uncover patterns that may affect project delivery. AI also automates most redundant tasks, thereby enhancing employee engagement and productivity. As per Gartner, AI will eliminate 80% of today’s manual project management tasks by 2030. AI machines will take over everything from planning to data collection, tracking to reporting, and so on.
The data-interpretation capability of AI can provide real-time insights into project metrics. It can enable project managers to make data-driven decisions based on past experience. For instance, Cap Gemini uses the cognitive computing system IBM Watson to improve resource deployment in projects through efficient resource planning.
AI can improve the accuracy of the project planning and supports the project manager to monitor the project’s progression. This is especially valuable when dealing with large and complex projects. AI-enhanced project management tools can help in making the right decision on the best allocation of resources for your project. Machine learning algorithms can be used to provide estimates of duration, resource and budget requirements for project activities based on historical information from previous projects.
AI can provide real-time data and project status updates through data visualization. This can help the team and management to discuss the status of a project and allows informed decision-making on the project duration, cost, and strategy The data-interpretation capability of AI can provide real-time insights into project metrics. It can enable project managers to make data-driven decisions based on past experience.
AI software enhances visibility for projects across the spectrum, which enables detection of risks early on so they can be handled before they pose a threat to the completion or quality of the project. For instance, the combination of machine learning with Monte Carlo simulation can help project managers to improve the evaluation and simulation of risks and opportunities of the overall project or specific tasks.
A knowledge-based expert system is a computer program that exhibits the knowledge and analytical skills of one or more human experts regarding a specific problem. The system captures the human expert’s experience and codes this in a computer so that any user can understand it. The knowledge engineer/human expert feeds information into the KBES. Often this information is declarative, i.e., the expert would state some facts, rules, or relationships into the knowledge base. The inference engine would then use the knowledge base as a data file to determine the knowledge and provide the output.