AI IQ

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.

Fear of new technology is often based on the belief that it results in a loss of jobs. As technology such as artificial intelligence (AI) becomes more prevalent in updating project methodologies, project managers ask the same question. Will we still need project managers?  Using AI in projects is growing because it improves project performance and increases project success rates. Below is my opinion on why AI will not be able to replace the project manager role.     

1. Managing data.  AI, especially machine learning algorithms, requires structured and relevant data.  From my experience working with organizations, they have a lot of project data, but it is unstructured, not easily accessible, and often misses important data points.  Project managers need to define a project data strategy, provide constant updates for data used as input to AI tools, and ensure the data being collected is the most relevant to the project type or organization. This is not a function that an IT person or a business specialist can provide. A project manager knows project management language and concepts such as the critical path and earned value.
2. Interpreting results and taking appropriate action.  AI is based on math, not myth. Project managers need to interpret machine learning output and determine what actions are required.  AI algorithms produce a prediction or perform classification.  Prediction is unlikely to be a 100% probability, and a classification result may include pointless outliers. A project manager with knowledge of statistics can determine the proper evaluation and next steps toward a decision.
3. Collaborating.  Studies show that when people collaborate with AI tools, the results are better than either could achieve on their own.  As shown in reasons 1 and 2 above, project managers have a critical role in optimizing this technology's effectiveness.

The knowledge required to be a great project manager will change, and the role will be slightly different.  As mundane tasks, such as creating a project status report and organizing a team meeting, are automated, there will be other more interesting and challenging tasks for project managers to perform that will improve project performance.

My next blog outlines how AI-based tools can replace project managers.

Fear of new technology is often based on the belief that it results in a loss of jobs. As technology such as artificial intelligence (AI) becomes more prevalent in updating project methodologies, project managers ask the same question. Will we still need project managers?  Using AI in projects is growing because it improves project performance and increases project success rates. Below is my opinion on why AI will not be able to replace the project manager role.     

1. Managing data.  AI, especially machine learning algorithms, requires structured and relevant data.  From my experience working with organizations, they have a lot of project data, but it is unstructured, not easily accessible, and often misses important data points.  Project managers need to define a project data strategy, provide constant updates for data used as input to AI tools, and ensure the data being collected is the most relevant to the project type or organization. This is not a function that an IT person or a business specialist can provide. A project manager knows project management language and concepts such as the critical path and earned value.
2. Interpreting results and taking appropriate action.  AI is based on math, not myth. Project managers need to interpret machine learning output and determine what actions are required.  AI algorithms produce a prediction or perform classification.  Prediction is unlikely to be a 100% probability, and a classification result may include pointless outliers. A project manager with knowledge of statistics can determine the proper evaluation and next steps toward a decision.
3. Collaborating.  Studies show that when people collaborate with AI tools, the results are better than either could achieve on their own.  As shown in reasons 1 and 2 above, project managers have a critical role in optimizing this technology's effectiveness.

The knowledge required to be a great project manager will change, and the role will be slightly different.  As mundane tasks, such as creating a project status report and organizing a team meeting, are automated, there will be other more interesting and challenging tasks for project managers to perform that will improve project performance.

My next blog outlines the areas where AI-based tools will replace project managers.

     There are two options for obtaining AI software: build your own or procure them from a vendor. Developing AI tools should be based on a well-conceived strategy. The first step is to understand existing project problems, such as the inability to achieve budget goals, constant change requests, or the inability to identify and manage risks. The next step is to create the objectives for the new AI-based process. The organization must consider a change that disrupts the project methodology instead of simply automating existing tasks and roles. It should also be evident that applying AI to the project methodology requires an effective change management process.

            There are advantages and disadvantages to building or buying AI tools for project management. Building tools internally brings increased knowledge to the organization, maintains a higher level of data security, and allows a faster, more flexible response to feedback. Buying tools allows the organization to take advantage of vendor experience in the market, avoids the search for highly skilled AI resources, and set more attention on solving the project problem.

Build Advantages                                            

• Increase internal knowledge            
• Maintain data security
• Provide flexibility for changes
• Offers instant feedback and adjustments

Buy Advantages

• Capitalize on vendor experience
• Utilize industry solution
• Reduce resource acquisition concerns
• Focus on data, not algorithms

Additional considerations in either scenario include responsibility for managing data, providing support for the new AI-based process, proper interpretation of results, and the strategy for testing and validation. There are numerous vendor offerings that use AI as a core algorithm in their software. Some organizations take advantage of this opportunity, while others use internal resources to create their own machine learning and natural language processing (NLP) algorithms to apply to their project methodology.