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5 Ways Artificial Intelligence Can Improve Company’s Project Management

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October 22, 2021
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6 minutes
Lynn's diverse perspectives on business stem from her extensive experience as a management consultant - her role as a beloved wife, mother and grandmother adds further depth to her insights.

AI for project management and its potential to take over project management roles is becoming a subject of debate.

There can be little question that technology is helpful as projects become more complex. Moreover, globalization happens, companies digitize their systems, and teams become dispersed across geographic boundaries. But, will AI take over the role of a project manager?

The power of technology is not new for project managers. Project management tools introduced over the years may now be considered commonplace. At the time, they were revolutionary.

  • 1920: Gantt charts for scheduling
  • 1957: Critical Path Method (CPM) to predict project duration by analyzing the sequence of events with the least flexibility
  • 1958: Program Evaluation Review Technique (PERT) – analysis of the time needed to complete tasks
  • 1962: Work Breakdown Structure (WBS) – tree structure of deliverables and tasks to complete projects

Project managers and project management consultants still apply these techniques, although they have become automated over time. I remember the excitement of a manager of a new hotel construction demonstrating one of the first versions of Microsoft Project.

However, automation requires a fair degree of human intervention. Artificial Intelligence takes a step further – it seeks to exclude human help.

What is Artificial Intelligence (AI)

IBM has used the following description for AI: “Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.”

Basically, it is about applying technology to make the world more efficient and effective. The goal is generally also to improve customer experience.

AI systems include:

  • Robotic process automation (RPA)
  • Machine learning
  • Deep learning
  • Natural language processing
  • Digital assistants, and
  • Computer vision

Applying these technologies will lead to a more automated, integrated, and predictive environment for project management.

The benefit of AI is its ability to process massive amounts of data - including thousands of project records – make connections between them, reach conclusions, and make suggestions.

Phases of AI implementation

Firstly, Robotic process automation, represented by bots. Bots are software applications that complete automated, repetitive, predefined tasks.

Secondly, chatbots or digital assistants. They use natural language processing to conduct online chat conversations via text or text-to-speech and replace direct contact with a human.

Thirdly, With machine learning, bots adapt and adjust to the precepts they receive from the environment and take actions to maximize achieving objectives.

Fourthly, autonomous project management, may be possible but is probably years away.

“Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.” (IBM)

Project Management Roles

The PMBOK (Project Management Body of Knowledge) was first released in 1987, and its 7th version was published in 2021.

Basically, it identifies the ten subfields associated with project management (PM) and the phases in a project lifecycle.

Subfields and Project Life Cycles

Mostly, the subfields are relatively task-oriented and administrative:

  1. Integration
  2. Stakeholders
  3. Scope
  4. Resources
  5. Time
  6. Cost
  7. Quality
  8. Risk
  9. Procurement
  10. Communication

Moreover, the five phases of a project lifecycle integrates into subfields:

  1. Initiation
  2. Planning
  3. Implementing / Executing
  4. Monitoring and Controlling
  5. Closing

On the contrary, many of these elements can and will be taken over by AI.

However, project management roles go beyond the purely transactional.

The Human Side of Project Management

PMBOK describes a project manager as “the person assigned to lead the team that is responsible for achieving the project objectives.” This implies different roles and what PWC describes as the “human skills” associated with PM:

  • Leadership
  • People and stakeholder management (including conflict resolution)
  • Communication (verbal & non-verbal)
  • Storytelling
  • Empathy
  • Negotiation
  • Emotional intelligence

Moreover, these human skills are less likely to be taken over – certainly in the short term – by automation and AI.

How Can AI be Applied in Project Management Roles?

PM AI means that a project manager can use an AI-based assistant or bot to support and streamline his projects.

The main areas of application now include the following:

Increased Efficiency

Bots are best for handling repetitive, time-consuming, and often boring administrative tasks. For example, data entry and management, project schedules, resource allocation, invoicing, project documentation, workflows, and quality check activities.

Moreover, Chatbots can minimize the time needed to contact project team members by phone, email or in-person, check project status, set up meetings, or reallocate tasks. They can contact team members, remind them of jobs, and ask for information on progress.

According to Cognilytica research, project managers spend as much as 54% of their time on low-value task management. Using an AI assistant frees the manager for value add, strategic activities and team leadership. Adding on, 86% of project managers welcome AI technologies, and there was a 20% performance increase for those who adopt AI.

There is also a significant decrease in human error and, therefore, the potential for considerable cost savings.


Computers can process complex project data better than a human can. Basically, they identify trends and uncover patterns faster and pick up signals that people might miss.

Managers can only act on the information they have. Moreover, the delay of even a small task can impact project progress.

Accurate, real-time information identifies issues, gives early warnings of bottlenecks or of deadlines that will be missed – and managers can avoid them before they happen.


The goal for managers today is to make data-driven decisions. AI facilitates it.

There may be automated approval systems. For example, AI can develop an understanding of project performance based on KPIs, schedules and resources. It may recommend who is best placed to take the lead on a task, for the project manager to approve.

Basically, Guesswork is taken out of predictions of the time required to complete tasks. Value or risk analyses are faster and more accurate.

Planning and Forecasting

Predictive analytics give project managers confidence in planning and forecasting.

Moreover, Computers process enormous amounts of data and analyze it against multiple criteria and variables. These variables may include who is working on the project, their past performance, how quickly they are closing tasks, supply chain links, and the history of similar projects and managers.

AI tools then predict end dates and rate confidence in the prediction. Managers can change plans, scale up or down their resources, deal with issues, and potentially save thousands of dollars in project overruns, contractual penalties, and unhappy clients.

For example, The AI solution might find a trend that orders placed with a particular supplier take longer to process in the third and fourth weeks of the month. Managers can adjust their ordering schedules and avoid possible supply chain bottlenecks.

Agility and Flexibility

AI provides new intelligence and insights, and project managers can respond quickly and with confidence.

Moreover, Top project managers and management consultants will integrate AI into PMO, agile project management, Scrum mastery, and other approaches to improve project outcomes.

The Future of AI for Project Managers

At the moment, AI is taking over some project management roles through

  • Data integration, and
  • Process automation

As shown by, the number of AI-driven project management tools and platforms on the market. For example, Click-up, Trello, Basecamp, Asana, Wrike, Slack, and Project Insight.

Moreover, AI chatbots or project assistants are becoming a feature of these systems, and they often integrate with each other.

For example, one of my clients uses consultants and freelancers from several countries and various time zones. They combine Slack and Click-up:

  • processes conversations within Slack and recognizes tasks and assignments from them.
  • This information is integrated with project planning and workflow management in Click-Up.
  • And all of it is linked to emails and calendars.

Simply, This process means team members have the freedom to move ahead with their tasks with minimal check-ins. The project manager can handle multiple projects, is always up to date with progress, with follow-ups only to answer questions or deal with issues.

However, Integrating machine learning into project management is still at an early stage. When it becomes more established, it will be able to do such things as convert mind maps of professionals into tasks and relationships, or propose multiple schedules based on context and dependencies.

The final step will be autonomous project management.

The first three phases focus on technical project management processes. The fourth one will have to understand and master the project environment and stakeholders. The experts believe that this is unlikely to happen in the foreseeable future, only because company management will want to retain the power to make major financial decisions.

Key Takeaways

AI for project management is currently at phase two of a possible four stages.

Basically, It is not replacing project managers but is making them more aware, agile, and productive. Moreover, Project management roles can become more strategic, focusing on prediction, risk mitigation and stakeholder management. Bots and chatbots taking over task management of repetitive tasks.

The major benefits are increased efficiency, improved analytics, better decision making, confidence in planning and forecasting, and significant agility and flexibility in performance.

We are at an early stage of integrating machine learning into project management. Achieving total autonomous project management is still in the future.

In the meantime, managing projects still needs the human skills that project managers and project management consultants bring to the table.