Project Management & Artificial Intelligence (AI)

Artificial Intelligence (AI) accentuates the development of intelligent machines, thinking, and working like humans. Theoretical AI says that there are three types of Intelligence:

1.    Artificial Narrow Intelligence ( ANI ) – AI is programmed to perform a single task. Google Assistant, Google Translate, Siri and other natural language processing tools are examples of Narrow AI

2.    Artificial General Intelligence ( AGI ) – Also known as Strong intelligence, refers to machines that exhibit human intelligence. AGI is expected to be able to reason, solve problems, make judgments under uncertainty, plan, learn, integrate prior knowledge in decision-making, and be innovative, imaginative and creative

3.    Artificial Super Intelligence ( ASI )AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data. ... Cognitive computing is a subfield of AI that strives for natural, human-like interaction with machines. AI often revolves around the use of algorithms. 

Project Management involves numerous data analysis, Risk management, Estimations, Knowledge management, Continuous improvement, Decision making and a lot more where AI can support….

• Predictive Analytics: Branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the future.

Predictive analytics uses historical data to predict future events. Typically, historical data is used to build a mathematical model that captures important trends. That predictive model is then used on current data to predict what will happen next, or to suggest actions to take for optimal outcomes.

Data Analysis Types:

• Simple summation and statistics.
• Predictive (forecasting)
• Descriptive (business intelligence and data mining)
• Prescriptive (optimization and simulation)

Predictive analytics is very helpful during planning and decision making, and as we all know proper planning and decision making will always reduce project failure and increase the project’s success rate. 

• Risk Management Project risk management is an important aspect of project management. Project risk is defined by PMI as, "an uncertain event or condition that, if it occurs, has a positive or negative effect on a project’s objectives. As we all know every project has risks that are interdependent and have uncertainties associated with it. AI system helps in the identification of potential risks by analyzing real-time and historical project data. More importantly, it helps with better visibility in projects. For example, It helps in identification of the possibility of the completion of a task before the deadline or not by analyzing how much time is being spent on a task, based on the calculations of the completed/pending/WIP activities.
•  3- Project Estimations is a critical part of project planning, involving a quantitative estimate of project costs, resources, or duration. There are instances where an individual is not sure how much effort or the time a project might take to complete or how much money is required. That is where historic business data and project estimation comes into play. AI and machine learning are great at analyzing a large amount of data and find patterns so they can deliver useful information that will help you with project estimation.
•  4- Knowledge-Based Systems (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. . A knowledgebase is a centralized repository where information is stored, organized, and then shared. When used externally, a knowledge base is where one can go to learn any and everything they'd ever need to know about a company's products or services, organization or industry. Knowledge-based systems use machine learning and natural language generation to create the documentation for the individual.
• 5- Machine Learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Machine learning can be leveraged to study patterns in your project schedule and it will highlight areas where you can accelerate the project process. You can also use machine learning to assess risks and allows finance managers to give a better offer to customers. This can increase company revenue and profits. It can also be used to automate approval workflows and remove friction.
• 6- Decision Support Systems (DSS) is a computerized program used to support determinations, judgments, and courses of action in an organization or a business. Decision support systems create processes by using rules and logic and helps to automate the decision-making process.  A decision support system helps in decision-making but does not necessarily give a decision itself. The decision-makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions

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