AI IQ

Artificial intelligence is no longer a distant trend—it's actively reshaping how project managers plan, monitor, and deliver results. From forecasting project risks to generating reports through natural language processing, AI is unlocking new efficiencies. For PMOs and project professionals, this isn’t just an evolution—it’s a transformation of the project landscape.

Yet, the most significant barrier to AI adoption isn’t the technology itself. It’s the readiness of the people expected to use it.

Project managers are uniquely positioned at the intersection of strategic oversight and operational detail. To lead AI-integrated projects, they must understand not only how AI works but also how to collaborate with it effectively. This means gaining literacy in tools like machine learning, process automation, and predictive analytics—not to become data scientists, but to confidently interpret results, assess model performance, and apply AI outputs to decision-making.

PMOs have a key role in fostering this shift. Developing structured AI learning pathways ensures that project teams are equipped for what’s ahead. These pathways should be role-based, scalable, and practical, covering everything from ethical AI usage and data management to real-world use cases in project environments. Importantly, they must recognize that AI does not replace the core competencies of project management—it enhances them.

A supportive community of practice and mentorship model can further accelerate adoption, turning training into shared experience. By embedding AI into the PMO learning culture, organizations can create a workforce that is agile, informed, and capable of leveraging AI as a strategic advantage.

AI skills development isn’t a side initiative. It’s essential for the future of project leadership.

Although AI is a powerful new technology, there will likely be missteps as we learn to use it. Technology users have been lulled into expectations that software will deliver results with minimal effort. Checking local weather on a smartphone, shopping online, or ordering a ride from Uber are easy tasks. For project management, software can schedule tasks and manage complex requirements. This can be accomplished without insight into how the software logic works. However, for AI, we need to step back and understand the process, sometimes questioning the results. AI is not a turnkey solution, yet we treat it like it is. Think about using a large language model (LLM) like ChatGPT. The user asks a question and receives a response. Most users do not think about how the response is generated or what data was used to produce the result.  To clarify a vague answer, a new prompt can be entered requesting more details.

As project managers, we need to know more. AI methods can include supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, self-supervised learning, and genetic algorithms. Each algorithm has a different process for calculating results and has different data requirements. AI prediction is a probability. When you receive a response from an LLM, do you understand the probability that the answer is correct? You can ask for the source to help validate the answer. 

The way out of this forest of possibilities is education and training. We don’t need to be data scientists or software engineers, but we have a responsibility to investigate and understand how the algorithm provides answers. There is a learning curve with AI technology, and business users can be trained to enhance their knowledge so they know how to acquire optimal results from AI technology.     

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.