Artificial Intelligence (AI) has the potential to revolutionize project management by automating routine tasks, optimizing workflows, and providing insights that can help project managers make informed decisions.

Projects are not Operations, but...

Projects differ from operations, but there are often elements akin to operations, namely processes. Organizations often have project themes. For example, an organization may develop embedded components for the automotive industry. Over time, the organization develops embedded products for a variety of customers. A forward-thinking company would record data from these projects, for example, budget and schedule performance, baseline changes to budget and schedule throughout the project's life, and more.

Available Talent

This same recurring element of the organization’s projects applies to the people and talents required to meet the project's scope. Regarding our embedded products for vehicle companies, list the skills available.

From experience, one of the project difficulties is understanding which team members were working on what project elements and when. A Gantt chart only tells us an estimate of when a team member is working on a project. Schedules drift or slip.
Understanding the aggregation of the projects' impact on available talent is complex and is a source of project failures. It is possible to do Monte Carlo Analysis on project schedules. Doing this on all projects, the amount of overlap of the individual projects and key talents helps identify schedule risks. Too much overlap or demands for the same talent base employed over many projects will disperse the competence.
AI can analyze historical data and predict the availability of resources for upcoming projects. It can also identify the best-suited resources for a particular project based on skills, experience, and availability. 

Risk Management

AI can analyze data from past projects and identify potential risks and opportunities to help project managers mitigate risks and take advantage of opportunities.  
AI can be used to predict project risks. Various AI-based techniques and tools can be used to identify and assess potential risks in a project. For instance, machine learning algorithms can analyze historical project data to identify patterns and predict future risks based on these patterns. 
AI can also perform real-time risk analysis by continuously monitoring project activities and data to identify emerging risks and issues. This can help project managers to proactively manage risks and take corrective action before they become major problems. 
Moreover, natural language processing (NLP) can analyze unstructured data, such as project reports, emails, and other project-related documents, to identify potential risks that traditional risk management techniques may have missed. 

AI and Quality

AI techniques and tools can help ensure product quality, reduce defects, and improve overall customer satisfaction.
One such application of AI in quality control is machine vision, which involves using computer vision algorithms to analyze product images and detect defects or anomalies. For instance, machine vision can detect defects in products such as electronic components, automotive parts, or food products in manufacturing. Cameras are already in place in many of these lines to view interim product quality and at the end of the manufacturing line as quality control measures.
Another example is using predictive analytics and machine learning to identify potential quality issues before they occur. For instance, machine learning algorithms in software development can analyze historical data on software defects and help developers identify code patterns or configurations likely to lead to defects.
AI can analyze patient data and identify potential risks or adverse events in healthcare, helping healthcare providers provide better quality care.  This is helpful for projects that are exploring new process approaches to healthcare.
AI-based quality control and quality assurance techniques can help businesses improve their processes, reduce costs, and improve customer satisfaction. However, it's important to note that AI should not replace human judgment entirely. Human oversight and intervention should always be present to ensure the accuracy and effectiveness of AI-based systems.

AI Predictions, Cost, and Suppliers

AI can support project and product cost management in various ways. Here are a few examples:

  1. Predictive analytics: AI can use historical data to predict future project or product costs. Machine learning algorithms can analyze past project costs and performance metrics to identify patterns and make predictions. This can help project managers and product teams to estimate costs more accurately and plan budgets accordingly. 
  2. Cost optimization: AI can optimize costs by identifying areas where costs can be reduced without sacrificing quality or performance. For example, in manufacturing, AI can identify ways to optimize production processes to reduce costs while maintaining quality standards.
  3. Supplier management: AI can help manage suppliers’ costs. For instance, AI can help identify the best suppliers based on factors such as price, quality, and delivery time and also help track supplier performance to ensure they meet agreed-upon terms.


AI can help reduce project and product costs, improve cost management processes, and increase efficiency. Many alternatives can be explored effectively without the exertion to do so. However, it is important to note that AI should not replace human judgment entirely. Human oversight and intervention should always be present to ensure the accuracy and effectiveness of AI-based systems.
The critical key for AI to support those items mentioned above, the historical data must be digitized and stored. A procedure or standard for inputting information may need to be set up. Poor input will lead to poor outputs, no matter the algorithm. AI is not a substitute for human thinking, which includes much more than algorithmic execution, such as inductive, deductive, abductive, and analogical.