AI IQ

The development of virtual assistants and large language models (LLMs) provides the foundation for ubiquitous project management. I define this term as the ability of a project manager to manage a project from anywhere at any time. The technical components are available, but the functional aspects need some explanation and guidelines.

The purpose of ubiquitous project management is to improve productivity by allowing instant access to project data and decision-making. The premise is that the project team or various parts of the project are performed in different regions, such as a globally distributed project or one where contractors complete tasks at different sites.

LLMs, such as ChatGPT, can be accessed from smartphone apps or even smartwatches. They are linked to two main data sources.

1. The project data. This consists of all project management plans, such as the deliverables, budget, schedule, resources, and risks, as well as the project's current status and past performance.

2. The logic, performance, and decisions of previous similar projects. This might be within the organization, assuming there are enough projects or a general database containing similar projects.

Augmented with machine learning algorithms, the virtual project assistant proactively predicts potential issues and highlights current project issues to be solved. The project manager can access solutions that work (supervised learning) and decisions to avoid (reinforcement learning). Project managers who provide prompts in ChatGPT are currently using a form of this capability. Over time, the content and logic will become more focused and more effective.

How does a project manager make ubiquitous project management successful?

• Continue to involve and communicate with the project team and stakeholders
• Take responsibility for the data used by the virtual assistant and the actions taken
• Abide by ethical and cultural considerations for a more diverse project environment
• Allow project team members to gain insight into the process

Ubiquitous project management offers a new opportunity to be more productive and manage projects more effectively. There will be missteps, misinformation, and misunderstandings, but significant gains are possible. Knowledge of AI and how the process can be applied helps project managers as this capability is deployed.

Based on discussions, AI is being used by many project managers and many project-based organizations. Here are some practical examples of how AI is being used to improve project performance.

Generative AI

1. Generate sample templates. Project managers use tools such as ChatGPT to create a basic structure for scope, schedule, and risk documents that can be adapted to their projects.

2. Check for a comprehensive capture of the project plan. This consists of informing AI about the project and asking AI to review documents such as the scope requirements or risk details to check if the project documents have missed any items that should be included.

3. Check for solutions. For specific risks, project managers ask for mitigation strategies, select an appropriate one, and make adjustments as needed to use for their project.

Machine Learning

1. Prioritization. For organizations with many concurrent projects, machine learning is used to predict which ones have a high probability of success or provide the highest value. Decisions are made to prioritize or terminate projects in consideration of limited funding and organizational focus.  

2. Early warning. Organizations use machine learning to receive early warning of deterioration of budget or schedule performance before a human can detect the impending variance.  This allows more time to develop mitigation or action plans to avoid or recover the variance.

Natural Language Processing (NLP)

1. Procurement bids. Project managers use NLP tools to compare bid submissions and identify inconsistencies.

2. Requirements issues. Project managers use NLP tools to find errors and omissions in requirements documents. I recently worked with two government organizations to implement this capability.

AI in project management is rapidly becoming a standard process and valued methodology. Knowledge of how to use the technology is increasing, and project managers are seeing productivity gains from using AI. Finding additional practical applications and use cases will improve the value of this technology in project management.     

Generative AI models such as ChatGPT are part of an ongoing evolution of AI capability. The development of natural language processing (NLP) began simply with what is known as a “bag of words.” A paragraph or block of text is tokenized, which means breaking the content into linguistic units. A count is calculated for each word. If the word “sad” appears ten times and the word “happy” appears once, then the content has a negative sentiment. A subsequent development was the ability to identify parts of speech, known as parts of speech tagging. This became essential for language translation. Understanding nouns, verbs, adjectives, and adverbs provides a way to interpret a sentence.

Recurrent neural network (RNN) algorithms allow software to evaluate a sentence from right to left and left to right and remember sequential dependencies. A recent software development is transformers. A generative pre-trained transformer (GPT) adds self-attention capability, which is identifying keywords or phrases. Parallel processing is also included and performs faster analysis. The pretraining part of GPT means it accesses a volume of content that has already been analyzed. At the core of a GPT model, such as ChatGPT, are machine learning models that predict the next word or phrase and determine how to respond to a query.

Many people may find AI capability a mystery, but it is based on statistical models and highly evolving algorithms that take advantage of logical sequences. ChatGPT does not provide perfect answers. Understanding how to interact with the software improves the accuracy and relevance of responses, which is why prompt engineering is becoming an important skill for project managers.

Last week, I was in Bucharest to deliver a workshop at the PMI Romania Chapter Conference. I enjoy sharing my knowledge and experience of applying AI in project management with participants who are eager to learn and open to embracing AI-based solutions. This was an extremely well-organized event, and every member of the volunteer staff made me feel welcome. They exuded professionalism along with their excitement to host such a well-attended (sold-out) event.

I noticed the engaged attention of participants throughout my interactive presentation and was pleased that my 90-minute workshop received positive feedback. These project management practitioners have a thirst for knowledge and a desire to improve the projects they manage. The additional value was networking with like-minded individuals from many distinct project areas.  

This event may not be as large as a PMI Global Congress, but I encourage all project subject matter experts to accept the invitation to speak at local chapter events when an opportunity arises. It is very rewarding.

I want to extend my heartfelt thanks to all the outstanding individuals I had the pleasure of meeting at the PMI Romania Chapter. Your warm welcome and enthusiasm made my workshop experience truly memorable.

Project managers have a significant opportunity to increase the value they bring to an organization by embracing AI. This is especially relevant to data management. Making a machine learning algorithm effective requires determining the best set of project features to use as input. Data scientists are now a scarce resource. Yet, it is doubtful that a data scientist is prepared to understand the nuances of project data.

Here are four practical steps for successful data management when using AI. These steps are designed to be straightforward and manageable, empowering project managers to take control of their data.

1. Determine what project data is available, how it can be accessed, and the format for the files being accessed.
2. Understand how to perform data wrangling. Ensure there is structured project data. This means consistency in the format, and resolving any anomalies, such as blank data fields, multiple entries for a single field, and inconsistent meaning for entries within a field.
3. Become familiar with feature engineering. Determine how to manage data fields that have the same meaning, add data fields that are missing, and improve the details of data that is captured and used by AI-based software
4. Improve your knowledge of basic statistics. Be able to analyze outliers and decide on appropriate action. Understand probability, sample distributions, and causal correlation. You don’t need to be an expert, but basic knowledge is essential.

With increased skills in basic data management, the project manager becomes an integral part of the organization's successful adoption of AI-based tools. Their role becomes significantly more valuable, and their contributions will be recognized as they help navigate the complexities of AI.