For years now, the PMP certification introduced by Project Management Institute (PMI) has seen high acceptance and recognition by the project-program-portfolio (PPP) management community. Various agile approaches have seen wide acceptance in the industry. Since the beginning of this decade, practical hybrid-agile certifications such as CHAMP, and practical scaled agile certification such as CIPSA from ManagementYogi, are used by industry professionals and practitioners.
Enter Artificial Intelligence (AI). It’s going to impact us all. In fact, it’s already impacting us. Specifically, Generative AI, or simply, GenAI has a significant impact.
As project management is industry-agnostic and value-focused, AI will naturally play a big role. AI is also no longer specific to a few large organizations supplying AI “electricity” to all, but many small or mid-sized ones, who provide real value with their own “electricity supply.”
Organizations such as MPUG have already taken AI into their operations and are using AI assistants to help members, customers and keen learners.
In this article, we will focus on the new and upcoming PMBOK Guide, 8th edition, a draft edition available in December 2024. AI contents are now deeply involved. This required me to do a complete revisit from the earlier PMBOK Guide and PMP certification. The PMP exam is not yet impacted with new AI content, but it will be, post the final release of the new PMBOK guide.
Let’s start with understanding the fundamentals of AI.
The AI Onion *** UPDATED ***
Like the Scaled Agile Onion which I’ve explained in this article of Scaled Kanban using CIPSA (see here and here), I visualize the layers of AI as a part of the AI Onion. This is shown below.
Understanding these layers briefly will help while going forward in this article.
At a higher level, we have Artificial Intelligence (AI), which has the abilities of machines to carry out functions and demonstrate behavior that usually need the cognitive power and capabilities of humans.
There are many definitions of AI, which are quite convoluted and complex.
I’ll define AI in simple terms as:
“The ability of a machine to demonstrate human-like intelligence.”
Within AI, we have Machine Learning (ML). It’s a subset of AI. The ability to learn, as shown by humans, is key here, as that demonstrates one aspect of intelligence. It involves machine learning with algorithms. The algorithms take a large amount of trained data into ML models.
Then we have Deep Learning (DL), which is a subset of ML. DL uses neutral network layers and structures to recognize images and to have natural language processing (NLP). Deep Learning’s name comes from the layers of neural networks; the more the neural layers, the more powerful the learning is. For example, it has been successful in recognizing images.
Next, we have Generative AI (GenAI), which is part of Large Language Models (LLMs). It’s called “generative”, because it generates things such as text, audio, image, code, and more. The LLMs are trained with vast and diverse data, usually from the internet. GenAI, a subset of LLM, has practical, real-world examples. For example, taking prompts given by humans to generate human understandable text.
GenAI – An Example
MPUG’s AI assistant on their site is a simple, yet very good example of GenAI. If you are reading this article intently, you can try it right now! I have.
I’ve used it many times to get the needed information. Here is one real use case.
I asked MPUG’s AI Assistant a few questions:
“What is Scrum? Any course available?”
It gave me a really detailed response and also contact information for more help. Then I asked it to restrict it to 50 words, which it did. This is shown in the above figure.
You can ask this generative pre-trained text transformer (GPT) with a few prompts, and it’ll give you a response with explanations and links, as well as courses offered, and articles published on the topic. To get the right responses from the LLM, you need to have the proper prompts.
The new PMBOK Guide has information on various layers of the AI Onion, but before that, let’s get into the guide at a high-level by understanding the building blocks such as process groups, performance domains and processes.
As we proceed with this article, I’ll elaborate on the integration of AI with some of these elements of the new PMBOK Guide.
Process Groups *** UPDATED ***
In the earlier edition of the PMBOK Guide, the process groups were completely removed, but there was a channel for process-group-based implementation.
As I interact with many aspiring PMPs (see here) and RMPs (see here) frequently, the clear re-introduction of process groups will benefit them all. There was massive confusion with the previous edition of the PMBOK guide. Such confusion will be reduced now; learning will be enhanced to get certified, and application, post certification, will be better.
PMI defines a process group as:
“A logical grouping of project management processes to achieve specific project objectives.”
Simply put, a process group is a group of processes. Yes, as simple as that! For example, when we say Planning Process Group, it will have a group of planning processes.
To understand more clearly, you can refer to the below table.
As you can you see – there was no process group at all in the earlier edition!
In the new PMBOK Guide 8th edition, the number of processes in every process group is listed in the below table. I expect the number of process groups to remain the same in the final released version, though there might be some changes with respect to the naming and number of processes.
As you can see, we have 40 processes, spread across and grouped into five project management process groups.
Performance Domains
Performance domain, as the name suggests, is the domain of performance for the project, e.g., Project Finance Management.
PMI defines a performance domain as follows:
“A performance domain is a group of related activities that are critical for the effective delivery of project outcomes.”
Performance Domains are interdependent and interact with each other. They are also interrelated and will have outcome-focused measures (see here and here). These performance domains will have various associated processes, which in turn will have inputs, tools and techniques, and outputs (ITTOs).
The new PMBOK now has seven performance domains (PD). Each PD is a specific one and relates to an identified domain of project management.
Taking another example, now we have a PD called Governance. It’s a fresh introduction when compared to earlier editions of the PMBOK Guide.
To have more clarity, you can refer to the below table for a comparison of performance domains between PMBOK7 and PMBOK8.
New Process Map *** UPDATED ***
The process map in the PMBOK Guide, 8th draft edition, is quite different compared to the earlier editions.
Following are the key points for the new process map:
- Total number of processes = 40
- Every process is part of a process group and performance domain.
- The earlier processes of Project Stakeholder Management and Project Communication Management from PMBOK6 have been combined. It’s called Project Stakeholders PD.
- Certain processes and activities of Project Procurement Management from PMBOK6 have been pulled into the new Project Finance PD.
The process map is very important to know, along with flow of processes, sequencing among processes, and the clear logic of having them in that way.
To be a PMP, these processes, their sequencing, the flow, and interactions should be on your fingertips. In my sessions and courses, I always emphasize it and aspirants remember the logical sequencing with key ITTOs, in just 1 or 2 hours.
An Example
Let’s take an example of Project Governance PD to understand. The processes in the process map, when considered for this PD, are shown below.
As shown above, for the Governance PD:
- There are 7 processes in total.
- Each process belongs to a process group and of course, the PD as well.
- Each process serves a purpose. For example, the process of Authorize Project Initiation will indeed authorize the initiation of the project, formally assign the project manager, and allocate the resources for the project work.
- Each will have a set of inputs, tools and techniques, and outputs (ITTOs). For example, the Integrate and Align Project Plans process will have the Project Management Plan in the output.
- Each process will have tools and/or techniques for predictive, adaptive and hybrid approaches. For example, the Direct and Manage Project Execution process has a T&T of Daily Coordination Meetings. In a big team, this is equivalent to CIPSA Daily Scrum while using the CIPSA Scrum Framework for a large team.
The process map is crucial not only for your preparation of the PMP exam, but also to understand the vast content of the PMBOK Guide, in a relatively short time.
PMBOK Guide and Artificial Intelligence *** UPDATED ***
With the above understanding of AI, its layers, and the new PMBOK Guide, now let us understand how they are shown in the guide to be working together.
Application # 1: Direct Use of AI as a T&T
First and foremost, many AI-related tools and techniques will be used across the processes elaborated in the PMBOK Guide. Taking an example, there is an explicit T&T called Artificial Intelligence in the Estimate Activity Resources process of Resources performance domain.
Taking another example, Process Automations is now a T&T in the Scope performance domain. Automation is a usage of AI. As the PMBOK guide notes, AI can go further for assistance such as risk analysis and augmentation such as project business case preparation.
Application # 2: Indirect Use of AI in T&Ts
Usage can also be indirect, yet widely applied. For example, the T&T called Project Management Information System or PMIS, can have vast amounts of data on which AI can be applied.
PMIS is used frequently as a T&T in multiple processes across the performance domains. The initial practical example I gave for MPUG-AI, in fact, sources its response from MPUG’s organizational information system.
To reaffirm, PMIS is used in projects as well as both program management and portfolio management (see here).
Application # 3: Real-time Monitoring of Projects
Monitoring is a very important aspect of project management. AI tools can monitor the project progress in real-time against the planned baselines and give alerts for deviations from the baselines. These can help in corrective actions. AI tools can also generate Change Requests for such corrective actions. There are AI tools which provide such capabilities.
Usage # 4: AI in Risk Management
Risk management goes through multiple processes as this practical, hands-on article explains (see here). Post risk identification, AI tools can be used to suggest and/or automate some risk mitigation actions based on programmed rules.
Conclusion and Three Laws
For years, organizations such as MPUG, ManagementYogi, and PMI have played a big role in imparting project management knowledge and skills. These organizations have made enormous contributions to the field through high-quality articles, practical training using tools like Microsoft Project, webinars, newsletters, and a wide range of certification courses. Experts from around the world have contributed to these efforts.
Now, they are bringing AI learning and content to you. Because right now, and in the future, you will have to manage AI projects.
When done rightly and ethically, AI can be a great companion, bringing to mind Asimov’s Three Laws of Robotics, written in 1940s. I first learned about them while watching I, Robot at Kino Cinema in Yokohama Minato Mirai, Japan, back in 2004:
- One, a robot may not injure a human being, or, through inaction, allow a human being to come to harm.
- Two, a robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
- And three, a robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
Going forward, for every aspiring PMP and project management practitioner, understanding AI won’t just be important – it will be pivotal. A paradigm shift is on the horizon with AI.
To stay ahead of the curve, you need to familiarize yourself with various AI terminologies, ML models, NLP, LLMs and a variety of AI tools and techniques that will shape the future of project management.
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This article was first published by MPUG on April 29, 2025. This an updated version.
References
[1] Project Management Body of Knowledge (PMBOK) Guide, 8th edition draft, by Project Management Institute (PMI)
[2] PMP Live Lessons – Guaranteed Pass or Your Full Money Back, by ManagementYogi.com
[3] PMP 35 Contact Hours Online – With Money-Back Guarantee, by ManagementYogi.com
