How could we use Artificial Intelligence in Project and Program Management?

“AI (Artificial Intelligence) will reinvent project, program and portfolio management” (Taylor, 2021).

Summary

Artificial Intelligence (AI) in project and program management heralds a significant transformation, offering a means to enhance the historically low project success rates by acting as a companion and early warning system, identifying risks, and optimizing processes. AI’s ability to process vast amounts of data surpasses our human capability, allowing us as managers to leverage insights from past projects and make informed decisions during project selection and execution. To use AI effectively, we need to collect and keep up-to-date, high-quality data within a repository based on a strong project and program governance framework. This lets us use predictive modeling and real-time analytics to make smart decisions. As project and program management evolves with AI integration, the role of managers will shift, emphasizing the need for soft skills where AI does not suffice. AI promises a wide range of applications, from predictive analytics and resource optimization to improved stakeholder management, but the only restriction on its potential is our imagination in coming up with new ways of using AI.

AI in project and program management

Why?

Why should we use Artificial Intelligence (AI) in project and program management? “To improve the project success rate.” (Boudreau, 2019: 52).

Today, a lot of projects don’t stay within time, scope, or budget, or don’t deliver the anticipated value. Just like David Porter states, “It is an industry that needs disruption.” (Rogelberg, 2020: 8). Artificial Intelligence has the potential to bring about that disruption.

So what could we expect?

AI could be a companion on our project or program management journey. Like a GPS or a copilot helping us get to our destination. There may even be moments when it runs entirely on autopilot.

AI could also be an early warning system (Rogelberg, 2020: 7) for issues that might surface during our journey. It could also give us real-time insights via a dashboard and automatic status reports, flag potential issues, and provide alerts (Rogelberg, 2020: 21). And we could do some intelligent automation (Rogelberg, 2020: 21), to help us focus on what matters most in projects—people.

Along the way, AI can also be used as a gatekeeper. Consider the go-or-no-go choice. “Based on the available information, do we continue our journey or stop where we are right now?”.

As project and program managers, we need to have a complete holistic and bird-eye view of the project or projects within a program (Boudreau, 2019: 130). And that’s where AI can give us a helping hand.

As Susie Palmer states “A project manager’s ability to make good decisions is only as good as what he or she knows.” (Rogelberg, 2020: 17) and “Good project managers have a lot of experience and a wealth of hindsight that enable them to spot trends and see potential problems early.” (Rogelberg, 2020: 17).

But what if we could let AI help and guide us? AI could also take lots of data and different parameters into account. Way more than we, as humans, can do. In that way, we could stand on the shoulders of giant’s. Susie Palmer puts it as “[…] to bear a wealth of hindsight from other people’s experiences” (Rogelberg, 2020: 18).

How crazy would it be if we could predict the probability of success even before a project starts (Boudreau, 2019: 52)?

So AI can help us:

• To learn from the past.
• To help and guide us in the present.
• To shed light on the future.

But where do we start?

The role AI can play starts with data, good data. So we need better project and program management data! But before that, we need good processes within a governance framework that create good data.

To use AI effectively, we need to start with collecting “good” (consistent and clean) data and consistently maintain good data habits when creating and collecting new data (Boudreau, 2019). Because individual projects tend to exist in silo’s (Rogelberg, 2020: 12), we need to start collecting that data in a uniform way across projects in a data repository (Boudreau, 2019: 30). The data in the data repository will be used to build a knowledge base.

To have good data, start collecting project and program management data in a uniform way in a data repository.

So collecting and storing project and program data should be our top priority right now. But before that, we will have to take a look at our current project and program management processes. We need to assess what needs to be changed in our processes. So that the data we collect as a result of those processes is of good quality. We must establish an organization-wide project and program governance framework if one hasn’t already been put in place.

So we, as managers and as a company as a whole, can anticipate, adapt, and make as much use as possible of the merits that AI could bring to the table.

The role of a project manager is going to be different, and the way we deliver projects will change (Boudreau, 2019: 144).

But it will be a partnership between a project or program manager with the right skillset and an AI tool that can help and guide the manager to enhance project success.

As a manager, it has always been important, but now it becomes even more urgent to focus on people-oriented skills like communication, emotional intelligence, empathy, and self-reflection, to name just a few. Because this is the area where AI still falls short (Taylor, 2021).

A big picture

To put it all into perspective, I created the following visual representation:

So we have the “AI System or Tool” as a central hub.

This AI system uses data from a data repository and environmental data from other sources to generate information and knowledge.

It can help with project selection based on information from past projects, and it can help with understanding and evaluating requests for proposals.

It is fed with real-time information from projects that are being executed.

During the project execution:

• It acts as a companion.
• It has a dashboard.
• It creates alerts if needed.
• It constantly does an environmental (env.) and future scan. So it scans for possible future issues based on real-time decisions.

When the project is closed, the lessons learned and created artifacts are automatically added to the data repository.

What can AI do for you?

Below, I summarized a few possible use cases in project and program management, each with how it can help before the start of a project, when the project is being done, and how it can make some guesses about the future.

But beware:

The application of AI capability will only be limited by our imagination (Boudreau, 2019: 106).

Use Cases in Project Management

Before the start (past)

• Capture and analyze historical project data for project selection, project prioritization, and performance prediction (via a data repository).
• Identify and analyze industry trends to inform project conception.
• Define project metrics.
• Do predictive modeling for project feasibility and success rate.

In the moment (present)

• Help with processing requests for proposals (RFPs) (via natural language processing and a data repository of previous proposals).
• Help with decision-making via decision support (via a virtual assistant - chatbot).
• Help with real-time project tracking via dashboards.
• Create automatic status reports.
• Automatically calculate project metrics (budget metrics included).
• Do AI-driven autonomous project adjustments.
• Do predictive analytics for project risks.
• Do resource management and optimization.
• Do automatic quality control.
• Help with time management and tracking.
• Help with schedule management and tracking.
• Help with cost management and tracking.
• Help with change management and control. Do a cost-benefit analysis of the proposed change (via machine learning).
• Ensure compliance with regulations like GDPR.
• Enhance team performance (via machine learning).
• Use collaborative AI to improve team dynamics.
• Help with stakeholder management (via natural language processing and sentiment analysis).
• Help with project team communication.
• Help with requirements and user story generation and refinement (via natural language processing).
• Do automatic calculations of story points (via natural language processing on a data repository).
• Help with the integration of cross-functional team insights to optimize project outcomes.
• Facilitate virtual retrospectives and lessons learned sessions.

A glimpse into the future

• Do advanced forecasting and predictive analytics.
• Do resource availability forecasting
• Help with scenario planning using simulation models.
• Enhance project innovation through trend spotting and pattern recognition.

Use Cases in Program Management

Before the start (past)

• Analyze past program data to inform the current program strategy.
• Predict future challenges.
• Aid in project screening and selection (via a project screening tool and a probability of success calculation).
• Help with portfolio optimization using historical performance data.
• Do trend analysis for better alignment with organizational strategy.

In the moment (present)

• Help with real-time (projects in a) program tracking via dashboards.
• Create automatic status reports.
• Integrate multiple project data repositories for strategic decision-making.
• Help with the strategic alignment of multiple projects.
• Help with program risk mitigation strategies.
• Do automatic quality management and control.
• Help with cost management on the program level.
• Help with schedule management on the program level.
• Help with change management on the program level.
• Ensure compliance with regulations like GDPR.
• Do resource availability forecasting.
• Help with the dynamic reallocation of resources based on project performance and priority adjustments.
• Do program-level stakeholder engagement and sentiment analysis.
• Do value stream mapping for program efficiency.

A glimpse into the future

• Do AI-powered predictive governance.
• Do predictive analytics for strategic program pivoting.
• Help with resource availability allocation and forecasting for all the projects within the program.
• Help with cost scheduling for all the projects within the program.
• Do advanced forecasting for long-term program benefit realization.
• Do scenario planning for program contingency planning (via machine learning).

Glossary

What is a Project?

A project is a temporary endeavor undertaken to create a unique product, service, or result. The temporary nature of projects indicates that they have a defined beginning and end. Projects are separate from business-as-usual activities, requiring distinct technical skills and management strategies.

Project management, then, is the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements. It involves initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria at the specified time. The primary challenge of project management is to achieve all of the project goals within the given constraints, such as scope, time, quality, and budget.

The Project Management Institute (PMI) defines project management as “the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements” (Project Management Institute, 2017, A Guide to the Project Management Body of Knowledge (PMBOK? Guide) — Sixth Edition).

What is a Program?

A program is a group of related projects managed in a coordinated manner to obtain benefits and control not available from managing them individually. Programs may include elements of related work outside of the scope of the discrete projects in the program. Essentially, a program is about strategic alignment and the orchestration of various outputs from projects to achieve outcomes that reflect an organization’s objectives.

Program management, therefore, is the application of knowledge, skills, and principles to a program to achieve the program outcomes and to bring about desired benefits that are aligned with the organization’s strategic objectives. It involves the coordination of multiple project management processes and techniques. The primary challenge of program management is managing the interdependencies, resources, and stakeholders to realize benefits that would not be possible if the projects were managed in isolation.

The Project Management Institute (PMI) describes program management as “the application of knowledge, skills, tools, and techniques to a program to meet the program requirements and to obtain benefits and control not available by managing project individually” (Project Management Institute, 2017, The Standard for Program Management — Fourth Edition).

What is AI?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. This term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

AI is a broad field of study that includes many theories, methods, and technologies, as well as the following major subfields:

1. Machine learning (ML), where computers are given the ability to learn without being explicitly programmed.
2. Natural Language Processing (NLP), which is focused on the interaction between computers and human language.
3. Robotics, which involves designing, constructing, operating, and using robots.
4. Expert Systems, which are designed to solve complex problems by reasoning through bodies of knowledge.
5. Computer Vision, which is concerned with giving machines the ability to visually interpret the world.

What is a Large Language Model (LLM)?

Large Language Models (LLMs), such as OpenAI’s GPT (Generative Pre-trained Transformer), is a type of AI that tries to make text that sounds like it was written by a person by guessing what will happen next in the text. They are based on deep learning techniques and are trained on vast datasets, enabling them to produce coherent and contextually relevant text over extended passages.

What is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. It concerns itself with the interaction between computers and human (natural) languages, aiming to enable computers to understand, interpret, and generate human language in a valuable way.

Key areas within NLP include:

1. Speech Recognition: Transcribing spoken language into text.
2. Natural Language Understanding (NLU): Comprehending the meaning, intent, and sentiment behind the text.
3. Natural Language Generation (NLG): Producing text that appears natural to human readers from a computational data source.

NLP applications are diverse and ubiquitous, encompassing:

• Language Translation: Translating text or speech from one language to another.
• Sentiment Analysis: Identifying and categorizing opinions expressed in a piece of text.
• Chatbots and Virtual Assistants: Interacting with users in a natural, conversational manner.
• Information Extraction: Automatically extracting structured information from unstructured text.
• Text Summarization: Creating a shortened version of a text, capturing only the most essential information.

Disclaimer

?? All the views and opinions expressed in this article are my own personal views and opinions.

References

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Boudreau, P. (2019). Applying artificial intelligence to project management. Independently published.

Rogelberg, D. (2020). Will AI change the way you manage change? Seven project management experts on how people and data can work together for better outcomes. Mighty Guides by Sharktower. Retrieved November 3, 2023, from https://uk.sharktower.com/download-7-experts-ebook

Taylor, P. (2021). AI and the project manager: How the rise of artificial intelligence will change your world (1st ed.). Routledge.