“The market for AI in project management is projected to grow from USD 2.5 billion in 2023 to USD 5.7 billion by 2028, at a CAGR of 17.3% during the forecast period 2023 – 2028 “

Being that as the forecast, it is imperative that all project stakeholders must proactively prepare themselves to embrace the power of AI for improving project success.  The objective of this article is to introduce the potential and practicality of applying Artificial Intelligence (AI) for achieving better project outcomes. This article marks the beginning of a series that will explore the successful application of Narrow AI in various project scenarios. By reading this article, you will gain a clear understanding of the following key points:

1. Attributes of Artificial Intelligence: We will discuss the fundamental characteristics and capabilities of AI, providing insights into how it can enhance project management.

2. General AI vs. Narrow AI: We will delve into the distinction between General AI and Narrow AI, clarifying the specific focus and benefits of Narrow AI for project success.

3. Disruptive Potential of AI in Project Management: We will explore the ways in which AI can revolutionize project management practices, highlighting the potential disruptions and improvements it brings to the field.

4. Getting Started: This article will provide guidance on where to begin when incorporating AI into project management processes. We will discuss key considerations and steps to initiate AI adoption effectively.

5. Taking Initiative: We will address the question of who should take the lead in implementing AI for project success. You will gain insights into the roles and responsibilities of various stakeholders in driving AI initiatives within organizations.

This article serves as the foundation for the upcoming articles in this series, which will provide a further in-depth exploration of AI’s practical applications in project management.

Driverless cars and Self driven projects 

The buzz surrounding AI in project management is widespread, yet the majority of people have not truly delved into its potential. Many still view “self-driven projects” as unattainable goal or a mere fantasy. However, this scepticism resembles the initial reception of driverless cars. Just a decade ago, autonomous vehicles were considered a fascinating concept or a distant dream that most people didn’t believe in. Fast forward to today, and driverless cars are a reality. Imagine stepping into a rideshare car with no driver in the front seat. This scenario is currently unfolding on the streets of San Francisco and Phoenix, where Waymo, a company born out of Google, operates a fully autonomous taxi fleet. And it won’t stop there; Waymo plans to bring its service to Los Angeles soon. These autonomous vehicles are here to stay, supported by statistics that demonstrate their superior reliability compared to human-driven cars.

Usage Chat GPT – Just scratching the surface

As of today, the perception of AI in project management among professionals is primarily limited to the usage of Chat GPT. One of my colleagues in sales recently experienced this firsthand when he asked Chat GPT to enhance a draft email, and he was ecstatic with the results. Witnessing this initial success, I couldn’t help but feel happy for him. However, it’s crucial to recognize that utilizing Chat GPT for project correspondence is merely scratching the surface of AI’s potential in project management.

What is really Artificial Intelligence?

Artificial Intelligence is a broad field with various definitions. Here are a couple of perspectives:

• Cool things that computers can’t do
• Systems that are autonomous and adaptable

Personally, I find the second definition more compelling. For a system to be considered as employing artificial intelligence, it should possess two key attributes: autonomy and adaptability. Autonomy refers to the system’s ability to function with minimal human intervention, while adaptability involves the capacity to learn from and adjust to changing environments. If a system incorporates these characteristics, then it can be regarded as having a fundamental aspect of artificial intelligence. Otherwise, it falls outside the realm of AI. There are different kinds of AI, and each one has its own traits and skills. Here are some examples of common AI.

1. Narrow or Weak AI: This type of artificial intelligence is designed to conduct specific tasks or functions and is restricted to a limited domain. Examples include virtual personal assistants (e.g., Siri, Alexa), recommendation systems (e.g., Netflix’s movie recommendations), and autonomous vehicles.

2. General or Strong AI: General AI refers to a system that possesses the ability to understand, learn, and perform any intellectual task that a human being can do. This level of AI is still largely theoretical and has not been achieved yet.

3. Machine Learning (ML): ML is a branch of AI that focuses on making algorithms and models that let systems learn and get better without being directly programmed. It involves It includes using a lot of data to train models that can be used to make predictions or decisions. Examples include image recognition, natural language processing, and fraud detection.

4. Deep Learning: Deep learning is a subfield of ML that uses neural networks with multiple layers to process and understand complex patterns and data. It has been particularly successful in areas such as image and speech recognition, natural language processing, and autonomous driving.

5. Reinforcement Learning: In this type of AI, an agent is taught to make choices or act in an environment so that it can get the most rewards. The agent learns by making mistakes and seeing what happens in the form of awards or punishments. Reinforcement learning has been used in areas such as game playing (e.g., AlphaGo) and robotics.

6. Natural Language Processing (NLP): NLP is all about making it possible for machines to understand, interpret, and make up human language. It includes jobs like translating languages, figuring out how people feel about things (sentiment analysis), and making chatbots.

7. Computer Vision: Computer vision involves teaching computers to interpret and understand visual information from images or videos. It can be used for things like recognizing objects, sorting images, and autonomous vehicles.

8. Expert Systems: Expert systems are artificial intelligence programs that attempt to simulate the judgment of human experts in a given field. They use a knowledge base and a set of rules to provide expert-level advice or solutions.

These are some of the main types of AI, but it’s worth noting that AI is a rapidly evolving field, and new approaches and techniques continue to emerge.

Will AI replace the need for Project Managers?

AI has already made both positive and negative impacts on our lives, often without us even realizing it. For instance, numerous content creators have unfortunately lost their jobs to Chat GPT. Simultaneously, there are individuals with extensive domain knowledge who are leveraging AI to their advantage. I wholeheartedly agree with the statement that “AI will not take over everything, but those who leverage AI will definitely outperform those who do not.”

AI can replace project managers who do only mundane repetitive tasks. AI is a boon for project managers who want to free their time from mundane repetitive tasks so that they can focus on more value-adding activities.

Projects and project managers that embrace AI will undoubtedly yield superior results compared to those unwilling to step out of their comfort zones and rely solely on traditional project management practices. It’s important to acknowledge that AI offers transformative opportunities that can optimize various aspects of project management, pushing boundaries and unlocking new possibilities for success.

While Artificial intelligence for project success is a vast topic, creating pinches of AI or ‘narrow AI’ on top of traditional project management principles seems more viable.

The field of Project Management is poised for disruption with the advent of AI, which is expected to impact the following key areas:

1. Enhanced Project Portfolio Management project: AI can offer valuable insights by analyzing vast amounts of data, allowing for more informed decisions when selecting and prioritizing projects.

2. Digital PMO Support: AI-powered tools can assist PMOs in streamlining their operations, automating administrative tasks, and providing real-time analytics for more effective decision-making.

3. Improved project definition, planning, and reporting: AI can facilitate faster and more accurate project definition, planning, and reporting by leveraging historical data, predictive analytics, and intelligent algorithms.

4. Enhanced project scoping: AI can assist in identifying and defining project scopes by analyzing relevant data and providing recommendations based on historical project outcomes and industry best practices. In agile project management, AI can assist in prioritizing features for releases by analyzing market dynamics like competitor activity, alternate technology, etc.

5. Scheduling and proactive re-scheduling: AI algorithms can optimize project schedules, taking into account various constraints, dependencies, and potential risks. AI can also proactively adjust schedules in real-time, considering unforeseen circumstances or changes in project parameters.AI opens additional avenues for scheduling beyond the traditional fast tracking and crashing like appropriate manpower, material, and equipment allocation/reallocation. Based on historical data AI-based systems can suggest which person to be deployed, which piece of equipment to be used, and which material from which supplier to be used.

6. Automated reporting with drill-down and drill-up capabilities: AI-powered reporting systems can automatically generate comprehensive project reports with the ability to drill down into specific details or drill forward to future projections, providing stakeholders with actionable insights.

7. Virtual project assistants with NLP capabilities: AI-powered virtual assistants can provide project managers with real-time support, offering task reminders, and data analysis, and facilitating communication and collaboration among team members. Virtual project assistants with natural language processing capabilities will really disrupt the way we monitor and control projects.

8. Evolution of the project manager role: With the introduction of AI, the role of a project manager is expected to evolve. Project managers may need to acquire new skills, such as data analysis and interpretation, to effectively leverage AI tools and technologies in their projects.

9. New role for PMO: As AI transforms project management practices, PMOs may take on new responsibilities, including managing AI-based tools and technologies, driving organizational adoption of AI, and overseeing the ethical use of AI in project management.

Do not underestimate the potential for disruptions across various streams of projects. Both technology and infrastructure projects are susceptible to their effects. Incorporating artificial intelligence (AI) into engineering, procurement, and construction (EPC) projects can yield a multitude of advantages, such as heightened efficiency, improved decision-making, and significant cost savings.

First things first

By now, you might have had a fair understanding of the potential of AI in project management and the importance of clean and accurate data. AI systems rely on vast amounts of quality data to train and improve their models. AI systems rely on a single source of data, where a centralized and comprehensive repository of high-quality data is crucial for training and enhancing their models effectively. However, in EPC projects, data is often fragmented, coming from various sources such as design specifications, construction plans, cost estimates, and historical project data. This fragmentation can lead to challenges in data management, including the presence of wrong or inaccurate data. To address these issues, it is essential to establish a common data environment (CDE) that facilitates the collection, cleaning, organization, and integration of relevant data for AI applications. A CDE acts as a centralized platform where different data sources can be harmonized, ensuring consistency and accuracy.

By implementing robust data management practices within the CDE, organizations can overcome the hurdles associated with fragmented and incorrect data, ultimately enabling more reliable and efficient AI-driven decision-making processes in EPC projects. Ideally, the PMOs should take the lead. They must define the roadmap for the adoption of AI in their organization. 

In my next article, we will delve more into Common Data Environment (CDE) more, because everything starts with accurate data in AI.