How to Prepare best for the Future of Projects & A.I.

The evolution of technological progress, particularly in the last two decades in computing power (CPU), memory, and network capacity, has ultimately led to artificial intelligence (AI) taking the world by storm since the End of 2022. And in perspective, we are still at the beginning of these developments ...

AI has revolutionized our lives and work, from self-driving cars to virtual assistants. In project management, too, AI is increasingly becoming an indispensable tool for companies looking to boost productivity and efficiency.

But is AI the future of project management?

In this article, we will explore how AI is being used in project management, the benefits it brings, what aspects of project management are impacted, and how project managers can adapt to harness the potential of AI.

In today's rapidly evolving business landscape, project management professionals face the constant challenge of delivering projects on time and within budget. Project management is undergoing a transformation that promises to revolutionize traditional practices with the advent of artificial intelligence (AI).

According to a Project Management Institute (PMI) survey, 81% of project staff say AI technologies are impacting their organizations.

As artificial intelligence (AI) continues to advance, project management (PM) is predicted to see a reduction of approximately 80% in its current workload by 2030.

This transformation will occur as AI takes over traditional PM tasks, including data collection, tracking, and reporting.

Understanding the role of AI in project management is critical for professionals and experts in the field to remain competitive and effectively leverage new technologies.

A.I. in Project Management - Survey Results

Learn more aboutthe Impact of A.I. to Project Management and how your peers see it in this Results Paper released

What Is Artificial Intelligence?

For a better understanding of this article and, of course, to have a general say in it, it is crucial to understand, at least in principle, what AI is in the first place.

Therefore, here is a short, basic explanation:
Artificial Intelligence (AI) is a branch of computer science that creates intelligent agents, i.e., systems that can "think" logically, learn, and act independently. AI research is concerned with how to create computers that are capable of intelligent behavior.

In practice, AI applications can be used in a variety of ways, including:

  • Machine learning: in this method, computers learn from data without being explicitly programmed.
  • Natural language processing: this involves teaching computers to understand human language and respond in ways that are natural to humans.
  • Robotics: This involves using robots to perform tasks that would otherwise be difficult or impossible for humans.
  • Predictive analytics: This method uses artificial intelligence to predict future events, trends, and behaviors.
  • Expert systems: This type of AI application attempts to mimic the decision-making processes of a human expert.Amy Ruddell

A detailed Introduction to AI can be found in our dedicated Roadmap: 

Which areas of project management are primarily affected by AI?

Project management is affected on many fronts by disruptions caused by technological advances and changing requirements. Automating repetitive tasks allows project managers to focus on critical decisions and value-added activities. Shifting to adaptive methods promotes adaptability and improves project outcomes. In addition, the evolving role of project managers underscores the need for strategic leadership and effective communication.

It is important to note that while AI can impact these aspects of project management, it is not a replacement for human project managers.

AI technologies are tools that improve decision-making and support project managers in their role, creating capacity that the project manager of the future can use for the aspects that matter, such as leadership and communication.

If project managers understand and adapt to these changes, they can take advantage of the opportunities that arise and achieve successful results even in a rapidly changing environment. 

Currently, it is foreseeable that the following areas of the PM will be affected by AI or are already affected:

  • Task and Resource Allocation: AI can automatically assign tasks to team members based on their skills, availability, and workload. It can also optimize resource allocation by analyzing historical data, project requirements, and individual skills.
  • Risk Management: AI can improve risk management processes by analyzing large amounts of data to identify potential risks, predict their likelihood, and suggest mitigation strategies. It can also provide project managers with real-time monitoring and early warning systems.
  • Scheduling and Planning: AI-powered algorithms can optimize project schedules, considering various factors such as task dependency, resource availability, and constraints. AI can help identify potential schedule conflicts and suggest alternative plans.
  • Data Analysis and Decision Making: AI can analyze project-related data, such as historical performance, financial metrics, and stakeholder feedback, to provide insights and support decision making. This can help project managers make informed decisions and predict project outcomes more accurately.
  • Stakeholder Engagement: AI-driven chatbots or virtual assistants can interact with project stakeholders, provide them with information, answer queries, and help solve everyday problems. This can improve stakeholder engagement and satisfaction.
  • Communication and Collaboration: AI can facilitate communication and collaboration between project team members by providing intelligent chatbots or virtual assistants to answer common questions, provide project updates, and help share information.
  • Quality Control and Testing: AI can automate quality control processes by using machine learning algorithms to detect errors or anomalies in project deliverables. It can also help test software applications by creating test cases and executing automated tests.
  • Project Monitoring and Reporting: AI can monitor project progress in real time by analyzing data from multiple sources and generating status reports. It can identify performance variances, highlight critical issues, and recommend corrective actions.

Benefits Of Using A.I. In Project Management

By leveraging AI technologies, project managers can unlock new possibilities and overcome traditional limitations. Let's delve into the key advantages that AI brings to the realm of project management:

Enhancing Productivity and Efficiency through Automation and Optimisation: AI automates repetitive and time-consuming tasks, freeing project managers' time to focus on strategic activities. Tasks such as data entry, status reporting, and documentation can be automated, reducing manual effort and minimising the chance of errors. In addition, AI algorithms can optimize resource allocation, ensuring that the right resources are assigned to the right tasks at the right time. This optimization increases productivity, streamlined workflows, and improved project efficiency.

Improving Decision-making Processes with Data-driven Insights: AI can quickly analyze vast amounts of data and extract meaningful insights. By leveraging AI-driven analytics, project managers can access real-time data and actionable insights that support informed decision-making. AI algorithms can identify trends, patterns, and correlations within project data, enabling project managers to make proactive decisions based on accurate and up-to-date information. This data-driven approach minimizes the reliance on gut instincts and intuition, leading to more precise and effective decision-making.

Streamlining Resource Allocation and Scheduling through AI Algorithms: Resource allocation and scheduling are critical factors in project management. AI algorithms can analyze historical data, project requirements, and resource availability to optimize resource allocation and scheduling. By considering resource skills, availability, and task dependencies, AI can create balanced and realistic project schedules that maximize resource utilization and minimize bottlenecks. This optimization ensures that projects are completed efficiently, with optimal resource allocation, and within the defined timelines.

Enhancing Project Risk Management and Mitigation Strategies: Risk management is a core competency in project management. AI improves project risk management by identifying potential risks, assessing their impact, and suggesting mitigation strategies. By analyzing historical project data, market trends, and external factors, AI algorithms can identify patterns and predict potential risks before they escalate. This early warning system enables project managers to proactively develop risk mitigation strategies and allocate appropriate resources to manage potential threats effectively

Better selection and prioritization:
Selection and prioritization are a prediction: which projects will bring the most value to the organization? When the correct data is available, ML can detect patterns that can’t be discerned by other means and can vastly exceed human accuracy in making predictions. ML-driven prioritization will soon result in:
- Faster identification of launch-ready projects that have the proper fundamentals in place
- Selection of projects that have higher chances of success and deliver the highest benefits
- A better balance in the project portfolio and overview of risk in the organization
- Removal of human biases from decision-making

Support for the project management office
Data analytics and automation startups are now helping organizations streamline and optimize the project management office's (PMO) role.  These new intelligent tools will radically transform the way PMOs operate and perform with:
- Better monitoring of project progress
- The capability to anticipate potential problems and to address some simple ones automatically
- Automated preparation and distribution of project reports and gathering of feedback
- Greater sophistication in selecting the best project management methodology for each project
- Compliance monitoring for processes and policies
- Automation, via virtual assistants, of support functions such as status updates, risk assessment, and stakeholder analysis

Improved, faster project definition, planning, and reporting
One of the most developed areas in project management automation is risk management. New applications use big data and ML to help leaders and project managers anticipate risks that might go unnoticed. These tools can already propose mitigating actions, and soon, they can adjust the plans automatically to avoid certain risks.

Similar approaches will soon facilitate project definition, planning, and reporting. These exercises are now time-consuming, repetitive, and mostly manual. ML, natural language processing, and plain text output will lead to:
- Improved project scoping by automating the time-consuming collection and analysis of user stories. These tools will reveal potential problems such as ambiguities, duplicates, omissions, inconsistencies, and complexities.
- Tools to facilitate scheduling processes and draft detailed plans and resource demands
- Automated reporting that is not only produced with less labor but will replace today’s reports — often weeks old — with real-time data. These tools will also drill deeper than is currently possible, displaying project status, benefits achieved, potential slippage, and team sentiment clearly and objectively.

Virtual project assistants
Practically overnight, ChatGPT changed the world’s understanding of how AI can analyze massive data sets and generate novel and immediate insights in plain text. In project management, tools like these will power “bots” or “virtual assistants.” Oracle recently announced a new project management digital assistant, which provides instant status updates and helps users update time and task progress via text, voice, or chat.

The digital assistant learns from past time entries, project planning data, and the context to tailor interactions and smartly capture critical project information. PMOtto is an ML-enabled virtual project assistant that is already in use. A user can ask PMOtto to “Schedule John to paint the wall next week and allocate him full time to the task.” The assistant might reply, “Based on previous similar tasks allocated to John, it seems he will need two weeks to do the work, not one week as you requested. Should I adjust it?”

Advanced testing systems and software
Testing is another essential task in most projects, and project managers need to test early and often. It’s rare today to find a major project without multiple systems and types of software that must be tested before the project goes live. Soon, advanced testing systems only feasible for certain megaprojects will become widely available.

The Elizabeth Line, part of the Crossrail project in the United Kingdom, is a complex railway with new stations, new infrastructure, new tracks, and new trains; it was, therefore, important that every element of the project went through a rigorous testing and commissioning process to ensure safety and reliability. It required a never-before-seen combination of hardware and software, and after initial challenges, the project team developed the Crossrail Integration Facility. This fully automated off-site testing facility has proven invaluable in increasing systems’ efficiency, cost-effectiveness, and resilience. Systems engineer Alessandra Scholl-Sternberg describes some of its features: “An extensive system automation library has been written, which enables complex set-ups to be achieved, health checks to be accurately performed, endurance testing to occur over extended periods, and the implementation of tests of repetitive nature.” Rigorous audits can be run at the facility 24-7, free from the risk of operator bias.

Advanced and automated system testing solutions for software projects will soon allow early detection of defects and self-correcting processes. This will significantly reduce time spent on cumbersome testing activities, reduce the number of reworks, and ultimately, deliver easy-to-use and bug-free solutions.

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What Are The Challenges Of Using Artificial Intelligence In Project Management?

Implementing AI in project management comes with certain risks and challenges. To mitigate these risks, organizations should approach AI implementation in project management with careful planning, involve relevant stakeholders, and maintain a balance between AI-driven insights and human judgment. Continuous monitoring, transparency, and accountability are essential to ensure AI's ethical and responsible use in project management.

  1. Data Quality and Availability: AI relies heavily on data. If project management data is incomplete, inaccurate, or of poor quality, it can impact the effectiveness and reliability of AI algorithms. The availability of high-quality data is critical to accurate AI-powered decision-making.
  2. Bias and Fairness: AI algorithms can inherit biases from the data they are trained on, leading to biased decision-making. Project management can lead to unfair resource allocation, biased risk assessments, or biased project evaluations. Regularly monitoring and evaluating AI algorithms is necessary to identify and mitigate biases.
  3. Lack of Human Judgement: While AI can provide insights and recommendations, it requires the human judgment and contextual understanding that project managers possess. Relying too much on AI algorithms without considering human expertise and intuition can lead to suboptimal decisions or missed opportunities.
  4. Implementation Complexity: Implementing AI in project management requires specialized knowledge and expertise. Companies may need to invest in AI infrastructure, data integration, and staff training. Poorly planned or executed implementation can lead to cost overruns, delays, or project failure.
  5. Change Management and Resistance: The introduction of AI in project management may face resistance from employees who fear a job change or perceive AI as a threat. Effective change management strategies, clear communication, and training programs are important to address concerns, build trust and ensure a smooth introduction.
  6. Ethical and Legal Considerations: AI in project management raises ethical and legal issues, such as data privacy, data security, and regulatory compliance. Companies must ensure that AI systems adhere to ethical guidelines, protect sensitive project data, and comply with relevant laws and regulations.
  7. Overreliance and Misinterpretation: Although AI can provide valuable insights, it is essential to recognize its limitations. Relying too heavily on AI recommendations without critical analysis or validation can lead to poor decisions. Project managers should understand AI algorithms' underlying assumptions and limitations and use them as tools for informed decision-making rather than blindly accepting them.
  8. Maintenance and Adaptability: To remain effective, AI algorithms must be regularly maintained, updated, and fine-tuned. Project management practices and requirements evolve, and AI systems must adapt accordingly. Failure to maintain and update AI solutions can lead to outdated or inefficient performance.

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Pros And Cons Of Using AI In Project Management

There are both pros and cons to using artificial intelligence (AI) in project management. Some of the pros include:


  1. Increased Efficiency: AI can help automate repetitive tasks, freeing up time for project managers to focus on more strategic tasks.
  2. Improved Accuracy: Automating tasks leaves less room for human error. This can improve accuracy and quality control throughout the project management process.
  3. Greater Insights: AI can provide insights that humans may not be able to see, such as patterns or trends in data. This can help project managers make better decisions about the direction of their projects.


However, there are also some potential drawbacks to using AI in project management, including:


  1. High Costs: Implementing AI into a project management system can be costly in terms of money and time investment.
  2. Job Losses: As AI automates more tasks, there is a risk that some jobs will be made obsolete. This could lead to layoffs or other negative consequences for employees whose roles are impacted by AI automation.
  3. Ethical Concerns: As AI becomes more sophisticated, ethical concerns exist about its use. For example, if AI is used to automate decision-making, who is ultimately responsible for the decisions made? There are also concerns about data privacy and security when using AI-powered systems.


Overall, AI is a valuable tool in project management. However, it is crucial to consider both the pros and cons before implementing an AI system.

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How to Get Started Using AI for Project Management

AI is implemented through innovative project management tool capabilities to improve project management. With the help of AI, companies can manage projects and programs more efficiently and make the PMO an even stronger value creator and implementer of the company's strategy. The benefits of these capabilities are currently focused on reducing administrative tasks through automation, auditing project management artifacts against best practice standards, and leveraging enhanced collaboration capabilities to manage risk better. To unlock the potential of AI for Project Management, you need to explore these tools in small steps that your organization can take today.

To get the most value from AI in project management, guidance on navigating the early stages of adoption is critical. Whether you take an agile, waterfall, or hybrid approach, you should identify, prioritize, and implement your AI efforts based on the opportunities for the greatest efficiency. You can engage partners to build custom tools into existing processes based on specific business needs or use off-the-shelf custom AI tools.

A few critical fundamental principles in getting started:

  • Test features based on methodology. For agile projects, test the tool's features (for instance, Stratejos ) that help you with estimates, budget, and sprint management in Jira. For waterfall projects, test the features of the Steelray project artifact review tool to compare your plans against best practices.
  • Focus on collaboration capabilities. For both project types, explore collaboration opportunities using Asana, Workfront, Monday, Memo, and Advanced Management Insight tools to determine the benefits of sharing project activities more broadly and involving stakeholders more in decision-making.     
  • Double down on data. To prepare for the future of AI for project management, clean up your task and resource management data. Machine learning will allow tools to help with estimation in the future, but they will only be as good as the data provided. Today's focus on good knowledge management and accurately capturing the correct data will provide the foundation for predictive analytics that help estimate schedules and resource needs.

By applying the power of AI to industry-proven best practices within PMO, organizations can achieve greater business value while developing PMO tools tailored to organizational culture, strategy, and needs.

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How Project Managers Should Adapt to AI to Stay Competitive

Automating a large part of their current tasks may frighten many project managers, but future possibilities should be seen as opportunities.
Successful project managers will learn to use the new tools to their advantage. Project managers will not disappear due to AI, but the project manager's job description will change massively. They must embrace these changes, adapt them, and take advantage of the new technologies. Currently, we think of cross-functional project teams as individuals, but soon we will think of them as a group of people and robots.

As their capacity shifts away from administrative work, future project managers must cultivate strong, soft skills, leadership, strategic thinking, and business acumen. They must focus on delivering the expected benefits and aligning with strategic objectives. They also need a good understanding of these technologies. Some organizations already integrate AI into their project management training and certification programs.

Project managers can adapt their practices to effectively leverage AI technology and remain competitive in an era of increasing AI adoption. Here are some ways project managers can adapt to AI:

Continuous Learning and Upskilling

Project managers should invest in ongoing education to stay abreast of AI advances, opportunities, and limitations. They should educate themselves on AI principles, terminology, and best practices to effectively communicate with AI experts, evaluate AI solutions, and manage AI-enabled projects. This will allow them to identify areas within projects where AI can add value proactively.

Collaborate with AI Systems

Instead of seeing AI as a threat, project managers should see AI as a collaborative partner. AI systems can provide valuable support in decision-making, risk management, and resource allocation. When project managers understand the capabilities and limitations of AI systems, they can leverage their strengths and expertise to ensure project success.

Address Ethical Considerations

AI adoption raises ethical considerations related to privacy, bias, and fairness. Project managers should be aware of these concerns and ensure that AI systems are designed and implemented ethically. They should also be prepared to address potential risks and challenges associated with AI implementation, such as algorithmic bias or unintended consequences.

Emphasise Soft Skills

While AI can automate specific technical tasks, project managers should focus on developing and improving their soft skills. For example, effective communication, leadership, stakeholder management, and adaptability are critical skills that project managers can use to add value in an AI-driven project environment. These skills complement the technical capabilities of AI and ensure successful project outcomes.

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Artificial intelligence can revolutionize project management in more ways than one. The growth of artificial intelligence will change the world and take us beyond our processing skills and biases. Many professions will change, including project management. Our success as PMs will depend on how well we can adapt and use these changes to our advantage.

The most important aspects of our jobs are leadership, collaboration and communication; these functions will not be automated in the foreseeable future, or at least not entirely. All those who fear their role as PMs will become obsolete must view this as another revolution in the age of technology. Banks have introduced ATMs, but that has not made the work of bank employees obsolete. ATMs gave customers convenient access to their accounts. They allowed bankers to use their skills to market other banking products, such as home loans and personalized banking, to a portion of customers. 

They promoted an opportunity for bank employees to use their skills to market other banking products, such as home loans and personalized banking, to a portion of their customers. A similar example: Sewing machines enabled faster production of clothes, but this did not eliminate the need for humans in the garment industry. On the contrary, it created a new way in the fashion industry, where the latest trends are implemented as quickly as possible through the mass production of clothes. Similar to these examples, project management will evolve to a new level with the advent of artificial intelligence. PMs can use the advanced insights of artificial intelligence to plan, execute and manage projects better than in today's world.

Start now to gain the knowledge and skills necessary to compete and succeed in the new world of project management.

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