The use of artificial intelligence (AI) in project management presents novel opportunities to manage projects more efficiently. For business owners, project managers, and project management offices (PMOs) alike, harnessing the power of AI is the key to greater productivity, resource optimization, and higher project success rates.

Here at Business2Community, we understand how challenging it is to find relevant, up-to-date information to help drive project decisions. That’s why we’ve gathered over 50 statistics about AI applications in project management from various reports and sources. Keep reading to discover all the latest trends you need to know, including how AI is used, its key benefits, challenges, and more.

AI in Project Management Statistics Highlights

  • 25% of project management offices (PMOs) globally have been impacted by AI.
  • 93% of companies that invest in AI in project management report a positive return on investment.
  • 66% of project managers consider saved time as the most beneficial impact of AI process automation.
  • AI could improve the success rate of projects by around 25%.
  • 80% of project management tasks will be eliminated by 2023 as AI takes on traditional project management functions such as data collection, tracking, and reporting.

How Is AI Used in Project Management? 

The key artificial intelligence technologies that project leaders in a PMI report attribute to higher quality work are:

  • Anti-bias solutions (68%)
  • Expert systems (61%)
  • Knowledge-based systems (59%)


AI Potential

The key AI technologies that project leaders in a PMI report attribute to higher productivity include:

  • Robotic process automation (74%)
  • Reinforcement learning (64%)
  • Machine learning (61%)

Machine Learning in Project Management

Machine learning (ML) refers to a branch of artificial intelligence that allows computers to learn from data and make better decisions over time.

According to PMI, 69% of organizations state that ML will impact them in the near future, while 31% are already being impacted.

Key ML use cases include:

  • Project planning: Machine learning powers faster project definition, planning, and execution.
  • Risk management: Machine learning is used to anticipate risks, propose mitigating actions, and automatically adjust plans to avoid certain types of risks.
  • Resource allocation: Machine learning helps a project manager optimize resource allocation by analyzing data on past projects, team member skills, resource availability, and workload.
  • Task planning: Machine learning is used to analyze the complexity of tasks, their estimated duration, resource availability, and other factors to help project managers make informed decisions about the order of tasks.
  • Analytics: Machine learning is used to guide accurate predictions about project completion, project costs, and more.
  • Quality control: Using machine learning, project teams can analyze data from previous projects, identify patterns that indicate potential problems, and take measures to mitigate quality issues.
  • Process improvement: Machine learning helps a project manager identify improvement areas by analyzing data on past projects for inefficiencies or bottlenecks.

Automation in Project Management

36% of AI innovators and 17% of AI laggards in a PMI study have experienced the benefits of automation.

In fact, 66% of project managers (PMs) consider saved time as the most beneficial impact of artificial intelligence process automation. This is followed by quality (56%) and change or transformation (53%).


Project Management Automation

Key examples of automation in project management include:

  • Project planning: Automation can create and update project schedules and resource plans based on predefined parameters, even as those parameters change.
  • Automating repetitive tasks: Automation can automate routine tasks, such as data entry, invoicing, and report generation.
  • Streamlining processes: Automation can monitor project progress, streamlining data collection, reporting, project scheduling, and more.
  • Improving efficiency: Automation can help projects run more efficiently by ensuring deliverables are tracked in real-time across the initiation, planning, and execution stages.
  • Reducing human error: Automation helps eliminate human error in project planning, scheduling, and execution, allowing projects to run more efficiently.

Natural Language Processing in Project Management

Natural language processing (NLP) allows machines to understand and process spoken and written language, including its grammar, meaning, and context. It is used for a variety of project management tasks, including:

  • Planning
  • Forecasting
  • Task automation
  • Risk management
  • Monitoring
  • Control
  • Coordination


NLP chart

Key use cases of NLP in project management include:

  • Project scoping: NLP simplifies the time-consuming collection and analysis of data, allowing PMs to scope for projects more accurately.
  • Generating project reports: NLP can extract relevant data from the project and prepare stakeholder reports.
  • Methodology selection: NLP can analyze project data and recommend the best project management methodology for each project.
  • Compliance monitoring: NLP can monitor project processes and policies to ensure compliance with regulations and standards, reducing the risk of non-compliance.
  • Automation of support functions: NLP-powered virtual assistants automate support functions such as status updates, risk assessment, and stakeholder analysis.

Generative AI in Project Management

Generative AI is a type of artificial intelligence that can create new content or data based on existing data or rules. Accenture found 40% of all working hours can be impacted by this technology.

This is because language tasks account for 62% of employees’ work time, and 65% of that time can be automated to increase productivity.


Generative AI impact

Key generative AI use cases include:

  • Project planning and scheduling: Generative AI can generate project plans and schedules by analyzing project data, project requirements, and resource availability. It can also create optimized project timelines and suggest task dependencies to ensure efficient project execution.
  • Risk management: Generative AI can identify potential risks and suggest mitigation strategies. It can also simulate various scenarios to assess the impact of different risk factors on the project timeline and budget.
  • Resource allocation: Generative AI helps a project manager allocate resources more effectively by making cost estimations and analyzing team dynamics. It can also make recommendations and suggest adjustments as project priorities change.
  • Task and workflow automation: AI-powered chatbots and virtual assistants can automate routine project tasks, such as sending status updates, scheduling meetings, and assigning tasks to team members.
  • Document generation: Generative AI can generate project-related documents, such as reports, proposals, and documentation for stakeholders.
  • Knowledge management: Generative AI can assist in capturing and organizing project-related knowledge, making it easier for team members to access relevant information and best practices from previous projects.
  • Automated reporting: AI can automate the generation of project status reports and dashboards, providing real-time insights into project performance.

Predictive Analytics in Project Management

Predictive analytics uses data, algorithms, and ML to forecast future project outcomes. It monitors project forecasts against historical performance to identify variances and helps PMs make better decisions about resources, risks, costs, and more.

In fact, 47% of PMs say that predictive analytics saves them time and makes them more productive.


AI impact

Key use cases include:

  • Uncovering insights: AI-powered data analytics tools are used to process vast amounts of data, and uncover patterns, trends, and project insights to help PMs identify areas for improvement. 
  • Resource allocation: Predictive analytics helps a project manager to allocate resources more effectively by analyzing past projects to identify patterns and trends.
  • Risk management: By analyzing past project data, predictive analytics helps PMs assess potential risks and develop proactive measures to address them.
  • Schedule optimization: Predictive analytics helps project managers optimize project schedules by analyzing past project data for patterns and trends.
  • Cost management: Predictive analytics analyzes past project data and identifies patterns, helping PMs estimate project costs and budget for future projects.

Who Is Using AI in Project Management? 

According to IBM, 35% of companies have adopted AI, while an additional 42% are exploring AI adoption.

More specifically, a Deloitte survey found that 25% of project management offices (PMOs) globally have been impacted by AI.

In a Capterra report, it was noted that AI was being well-received in project management with 80% of companies rating their level of acceptance levels as somewhat (57%) or extremely high (23%).

Additionally, 71% of companies are moderately (42%) or extremely (29%) familiar with the use of artificial intelligence in project management tools or software.


Project Management adoption

Click Up

Click Up is a cloud-based collaboration and project management tool packed with various AI-powered features. As one of the most versatile AI tools available in the field, it is designed to help project teams from all industries save time and manage their projects more efficiently.


Clickup logo

Click Up uses AI to provide the following capabilities to project teams:

  • AI-powered summaries for team tasks and email inboxes
  • Document summarization
  • AI writing and editing
  • Customer support
  • Translation
Company Click Up
Location California, US
Value $4.0 billion
Types of AI Used
  • Natural language processing
  • Generative AI


The world’s largest retailer, Amazon uses AI and machine learning across multiple business areas. From product development to service delivery, Amazon leverages AI across its many divisions to streamline internal processes and increase customer satisfaction.


Amazon logo

Key examples of how Amazon projects deploy AI include:

  • Chatbots for enhanced customer service
  • Machine learning for product recommendations
  • Product forecasting
  • Warehouse and delivery optimization
  • Detecting damaged products
  • Analyzing shopping trends
Company Amazon
Location Washington, US
Value $1.92 trillion
Types of AI Used
  • Machine learning
  • Natural language processing
  • Generative AI

Benefits of AI in Project Management

Some of the key benefits of AI-powered project management technology include:

  • Better selection and prioritization of projects.
  • Improved support for a project management office (PMO).
  • Enhanced project definition, planning, and reporting.
  • Advanced project management solutions.


Benefits of AI

Cost Efficiencies

As of 2023, the global average for wasted investment due to poor project performance is 5.2%. AI has the power to improve resource allocation and the project management process for greater cost efficiencies.

In fact, according to IBM’s Global AI adoption report, 50% of organizations are realizing benefits from using AI, with 54% achieving cost savings and efficiencies. 


Ai adoption

In a 2023 Capterra survey, 93% of respondents who invested in AI project management tools reported a positive return on investment (ROI) with 44% stating they were extremely satisfied with using AI technology in project management.

Higher Project Success Rates

Only 35% of projects are completed successfully, additionally, 58% of project practitioners have had a project fail in the past year. Further:

  • 43% of projects are not completed within budget.
  • 48% of projects are not completed on time.
  • 31% of projects do not meet their goals.

AI has the potential to improve the success rate of projects by around 25%. This could equate to trillions of dollars of value to organizations and the economy as a whole.


Project Outcomes by Industry

According to PMI, organizations with higher levels of agility and project performance outpace other organizations in their use of AI (26% vs. 21). These organizations also make more use of AI project management tools or software (39% vs. 30).

PMs can make better decisions and increase success rates by leveraging AI. For example, a report by PwC found that using AI to analyze project data can lead to a 15% increase in project success rates.


AI productivity

Additionally, AI can uncover patterns and outliers in data, greatly aiding project quality control. Advanced testing systems can check the quality of work, identify deviations from quality standards, and suggest measures to improve quality.

More importantly, AI can analyze large amounts of data to assess potential risks and recommend risk mitigation measures. By providing managers and team members with more visibility and alerting them when projects are going off-course, AI contributes to higher project success rates.

Enhanced Team Collaboration

58% of project professionals work remotely with an increasing number of project management teams needing to cooperate across multiple locations and departments to deliver their projects.

In addition, 40% of respondents in a recent Project Manager survey say that 76 – 100% of their projects require collaboration outside of their immediate team.


Remote work

Navigating remote work and teams presents new challenges for organizations. However, AI can improve communication and team collaboration.

In fact, an Accenture study indicated that AI language models can increase team productivity by up to 25% by facilitating real-time communication and collaboration between team members.


Project management teams

AI can be used to identify which tasks are likely to cause delays, or which team members may be strained or under pressure. This, in turn, allows project leaders to optimize their teams to improve performance.

Lastly, AI can automate tasks and routine communications, such as sending reminders or updating project status which gives teams more time to work on complex tasks.

Greater Efficiency and Productivity

Generating reports and other relevant project documentation takes up a significant amount of time with over 50% of project managers noting they spent more than a day manually compiling project reports in a 2021 study.

Moreover, 20% of project managers say that documentation is the one task they wish they could spend less time on.

AI tools can provide instant, on-demand reporting in a matter of minutes, freeing up more time for project managers to strategize and lead.


AI Productivity

Through automating administrative tasks like scheduling and data entry, AI can save up to 20% PMs’ time, allowing them to concentrate on all the tasks that add to project success.

AI can improve the performance of agile teams by analyzing team data and processes. It can also:

  • Identify potential project management process bottlenecks.
  • Suggest ways to improve team dynamics such as adjusting workloads, boosting communication, or improving workflows.
  • Identify which team members need assistance with their tasks or predict which tasks are likely to be completed by the next meeting.
  • Optimize task progress by determining the optimal balance between workload and team capacity.

Better Forecasting and Decision Making

58% of all respondents in an MIT Sloan global AI survey found that AI improved efficiency and decision-making among teams.

Projects are prone to cognitive biases, especially during the planning stage. By relying on historical data and predictive analytics, project managers can make better decisions regarding project approaches and resource allocation.


PMO Challenges

Project managers also have to deal with an overwhelming amount of information, which can affect decision-making and lead to poor project outcomes. The key benefits of using AI include:

  • Robust business intelligence.
  • Access to real-time data.
  • Data-driven recommendations across the entire project lifecycle.
  • Improved project insight.
  • More accurate forecasts to guide decision-making.

Better Resource Management

In KeyedIn’s 2023 PMO outlook report, 52% of project management professionals struggle with resource management.

When asked what area of resource management was most challenging, capacity planning topped the list with 42% of the responses, followed by:

  • Resource allocation – 37%
  • Resource utilization – 11%
  • Skills tracking – 7%
  • Managing contractors – 3%

PMs can reduce project costs by up to 10% by optimizing resource allocation. This enables organizations to complete projects more efficiently and with fewer resources.


Resource Management

22% of PMOs find it hard to decide which projects need their time and resources. In the KeyedIn report, saying “no” to projects was the top related struggle at 42%, followed by accurately “scoring ” the value of portfolio projects at 33%.

Using AI, project managers can analyze multiple projects and gain insights on which projects they should prioritize given available resources and other factors.

Challenges of AI in Project Management

As per a 2023 Capterra survey, the top barriers to AI adoption in project management include:

  • Lack of clear strategy – 28%
  • Ethical, privacy, or security concerns – 26%
  • AI technology immaturity – 25%
  • Inadequate IT support – 21%
  • Inadequate skills within project teams to implement AI – 20%

Workforce Challenges

In a 2023 Microsoft report, 60% of employees admitted they didn’t have the AI skills they needed to do their jobs. Meanwhile, 82% of business leaders agreed that employees needed new skills to be ready for the growth of AI.

Similarly, in an IPMA survey, only 33% of respondents working in organizations stated they had experience in using artificial intelligence in project management.

Implementing AI technologies requires expertise that many organizations don’t have. These skills include:

  • Understanding and applying algorithms
  • Interpreting AI results
  • Using AI effectively in a business context


Barriers to Ai adoption

The lack of AI training in project industries could result in a growing gap in AI knowledge and so negatively affect AI implementation levels.

In a Scope Master survey, only 12% of project professionals said they had received adequate AI training. More specifically:

  • 33% in the 35-49 age group and 42% in the 50-69 age group had received no training.
  • 38% of respondents from the IT sector received no training in AI compared with 24% of respondents from construction, manufacturing, and transport.

Data Challenges

Although 49% of respondents in a Scope Master survey were very likely to use artificial intelligence to analyze large volumes of data and improve decision-making, a majority highlighted several data-related challenges:

  • The use of artificial intelligence in project management depends on the availability of quality data.
  • Organizations must source, clean, prepare, and manage large volumes of data.
  • Reusing the same data sets and AI models for different problems can be difficult.
  • Inaccuracies or gaps in data can lead to biased or inaccurate predictions.


Data challenges

Privacy and Security Challenges

The use of AI in project management raises serious ethical and data protection concerns. Key issues organizations must prepare for include:

  • The ethical use and protection of personal data.
  • The use of AI systems that are fair and transparent.
  • Mitigating discriminatory or unethical outcomes.

High Adoption Costs

As smaller project teams and companies often have limited resources and budgets, AI adoption can be particularly challenging for them. AI-driven solutions and technologies require high initial investments in:

  • Hardware
  • Software
  • Skilled labor costs
  • Training and development of employees
  • Enhanced security measures


AI costs

The costs of implementing AI vary from business to business. Costs can run into thousands of dollars per year for AI-enabled software tools and easily run into millions for businesses looking to maintain their own data systems or establish their own in-house AI teams. A small AI team can cost a business upwards of $350,000 per year in labor costs alone.

Integration Challenges

AI is transforming project management, but its integration into traditional project management practices remains a key challenge for businesses.

While AI-powered tools and technologies offer new opportunities to improve project deliverables, integrating them into existing workflows and processes can be complex and costly.

Over 70% of respondents in an IPMA survey indicated a limited understanding of AI technologies as the key barrier to integrating AI into their processes.


AI barriers

Moreover, as the use of AI in project management is still relatively new, there are no set standards or best practices for organizations or project teams to follow. This makes it difficult to integrate AI into project processes in a consistent and effective way.

The Future of AI in Project Management

According to Capterra, companies expect to increase their investment in project management AI by an average of 32% in 2024.


Ai adoption

By 2026, the adoption of AI in project management will increase to 49%. This is up from an adoption rate of around 21% in 2021.

The Changing Nature of Project Management

By 2025, the RPS Group forecasts that AI will be able to:

  • Schedule projects
  • Assign resources
  • Create basic cost plans
  • Assist with contract interpretation and administration
  • Undertake earned value assessments
  • Forecast completion costs
  • Assist with identifying trends for opportunity capitalization and risk mitigation

By 2030, over 80% of tasks in project management will be fully powered by big data, ML, and natural language processing.

The Changing Role of Project Managers

48% of project professionals believe that AI is very likely to change the role of project managers, while 24% believe that it is extremely likely. However, many agree that AI isn’t likely to replace project managers entirely.


The changing role of Project Leaders

As AI takes over mundane or routine tasks, the role of the project manager may transform into one that is strategic as opposed to tactical. PMs will likely spend most of their time:

  • Coaching and supporting project teams
  • Maintaining regular communication with key stakeholders
  • Cultivating a high-performing culture


How is AI used in project management? 

How is AI changing the role of project leaders? 

How do you leverage AI in project management?