AI IQ

Reinforcement learning is a process of making decisions based on avoiding previous mistakes.  As humans, we interact with the world, learning the actions we need to take to achieve our goals. When we learn to ride a bicycle, we learn balance and steering to avoid falling over. In machine learning, reinforcement learning is an algorithm that learns to make the correct decision through trial and error. In the world of project management, we call this experience. Similar to gaining experience, the AI-based algorithm needs historical data. Reinforcement learning algorithms can start with no data and gradually become an expert by learning from mistakes in a game such as chess. However, this may not be the best strategy for managing a project.

Computers can retain a lot of data and have excellent recall. Think of an issue that is captured for a project in progress. What is the problem, and how do we plan to solve it? Project managers gather data and think about possible solutions. We use reinforcement learning in this situation because we avoid a solution that we know failed in the past. Now, think about having a database that contains all the decisions for a similar issue in numerous previous projects. The project manager avoids decisions that do not work and tries a new solution. If the new solution is successful, the reward is feeling good about making the correct decision.

I suggest to my project management students that they start their own project issues database as soon as they are employed in a project role. They can capture the project problem details, the project conditions or environment, the decision made, and if it was successful or not.

Capturing project decisions is a simple way to create data that an AI algorithm can use to improve project performance. Algorithms use this process by being able to access previous project information to help project managers make better decisions. Imagine if a project manager never made the same mistake twice!

Reinforcement learning is not at the top of the list for AI in project management because supervised and unsupervised learning are easier to work with and provide statistical results. However, this type of algorithm can be a powerful tool for helping project managers make good decisions.   

Project managers are inundated with training and education opportunities. As AI becomes more common in how projects are managed, project managers must ensure they have the right skills to be successful.      

1) Fundamentals of machine learning (ML) and natural language processing (NLP). Whether the AI-based solution is provided by a vendor or created internally by programmers, the project manager should understand the fundamental capability of these two components. Machine learning is the engine that drives most but not all AI solutions. A software program uses loops or iterations to refine the correlation performed by regression analysis. Hyperparameters are set before the program begins and include items such as the number of iterations and the number of layers in the neural network. It is essential to be able to ask relevant questions, such as the number of datasets used to make the prediction.

NLP uses numerous techniques to interpret language and generate a response. Generative AI, such as ChatGPT, depends on a corpus or body of work to provide useful responses since the data that is accessed significantly impacts the output. A project manager understands project information and needs to be involved in how project data is applied to Generative AI tools.

2) Data management. AI requires data, and, in most situations, relevant data will be more important than volumes of data. Data wrangling and feature engineering are necessary for proper input. An information technology (IT) person and a data scientist will not understand the nuances of project data. The project manager is the best person to identify the correct data, relevant data fields, and amount of data required for AI tools.

3) Math and statistics. AI is based on math. The algorithms use regression analysis to produce results. Statistics is an important component for understanding and interpreting the output. A high-level degree in math is not required. However, project managers must become familiar with managing different aspects of statistical analysis. What is an outlier? Can the outlier be ignored, or is it the start of a new trend?

Project managers have a critical role in the adoption and successful application of AI to improve project performance. There are new areas where training and additional knowledge are essential for this process. Collaborating with AI tools is an opportunity for project managers to demonstrate their value to project stakeholders. Improving these three skills should help you improve your value to any organization. 

In my previous blog, I stated three reasons why AI will not be able to replace project managers.  That should not make anyone complacent.  AI will change how projects are managed, and in this blog, I explain three areas where AI will replace the project manager functions and deliver improved project performance.

1. Administrative tasks.  Tools already exist to perform functions such as organizing a meeting and creating a project status report.  Known as Robotic Process Automation (RPA), this clever software captures a standard sequence of events familiar to project managers but adds the ability to handle more complexity.  When searching for a meeting time, RPA checks calendar availability for all participants books the time, and sends out meeting invitations to reserve the time.  
2. Decision making.  The first step for better decision-making is to build a knowledge repository.  This requires a well-conceived data strategy and comprehensive data capture on the selected issues.  For example, once the actions are captured that successfully resolve an issue, that action can be used again when faced with similar conditions.  Imagine capturing issues and risk responses for every project in the organization over ten years.  That becomes a valuable resource for decision-making that can be successfully utilized to avoid the bias of a human project manager making a decision. AI can review and analyze vast amounts of data faster and more effectively than humans.
3. Team building.  Pundits continue to suggest that AI can never handle the people side of managing projects without providing evidence to support their position.  One survey revealed that a majority of employees would rather work for a robot than their current manager.  How does a robot build a strong team?  AI tools capture sentiment analysis to better understand the performance and feelings of project team members.  This analysis is used for actions such as providing additional communication, clarification, or customized individual communication.  Next, based on task performance, an AI software agent provides unemotional, unbiased feedback to project team members on an individual basis.  Each team member now has a private coach to enhance their work habits, skills, and personal development. 

An AI-based project agent will eventually be much better than a project manager in these areas.  I will not predict when this will happen since it depends on a comprehensive change management process as AI-based tools are deployed in the project methodology.  Achieving the result also requires collaboration between a project manager and AI tools to make this vision a reality.