AI in project management
Core topics and use cases of project management organisations to increase productivity through the use of artificial intelligence
As a consultancy with a strong focus on innovation and technology, we are constantly monitoring how new developments are impacting project management. It is clear that machine learning (ML) and generative artificial intelligence (AI) are playing and will play an increasingly important role. Leading organisations and method providers such as PMI, IPMA and PRINCE2 are intensively analysing how AI is changing project work and which use cases are already proving their worth. Gartner, for example, predicts that around 80% of today’s PM tasks could be taken over by AI by 2030 – a drastic change, but one that also offers great opportunities.
In this publication, we examine which use cases have been identified and described by project management organisations. This article reflects our perspective on current developments around AI in project management. The selection and evaluation of the content is independent and exemplary – focussing on innovation impulses, not on completeness. In a further publication, we will look at the use cases implemented by tool providers and thus compare ‘theory and practice’.
1. Common core topics that are mentioned across all standards
- Automating routine tasks: AI relieves project teams by taking over repetitive tasks. Algorithms can already create reports and project documents or schedule meetings automatically, for example. In the near future, the majority of such administrative tasks are likely to be managed purely by AI, saving valuable time.
- Data-driven decision support: Modern AI tools analyse large amounts of data and recognise patterns in order to derive forecasts. This provides project managers with well-founded analyses of complex scenarios and recommendations for action that lead to better, faster decisions.
- Forward-looking risk management: Machine learning algorithms can be used to predict potential risks and problems at an early stage based on historical project data. Project managers can take proactive countermeasures, plan resources in a targeted manner and reduce the likelihood of delays or failures. Real-time monitoring: Sensor technology and the Internet of Things (IoT), in combination with AI, continuously provide project data in real time. AI models analyse this data and provide immediate insight into the progress and performance of a project. Trends or deviations are recognised immediately so that the team can take countermeasures in good time to meet deadlines and targets.
- Intelligent communication assistants: AI-supported chatbots and virtual assistants support collaboration in the project. They answer routine questions, automatically log meetings and create status reports, which improves the flow of information within the team. This makes communication more efficient and relieves project participants of administrative communication tasks.
To summarise: AI significantly increases efficiency in projects and takes over tedious routine work. This allows project managers to concentrate more on strategic and interpersonal tasks, such as leadership, stakeholder management and creative problem solving. AI therefore primarily serves as an enabler in project management – an enabler that gives people more freedom for value-adding activities.
2. Detailed consideration of the project management organisations
2.1 Project Management Institute (PMI) – Use of AI in the PMI standard
In recent guidelines such as AI Essentials for Project Professionals and Leading AI Transformation, the Project Management Institute (PMI) emphasises that the use of artificial intelligence (AI) in project management is increasingly becoming a success factor. The focus is on specific potential applications along the project life cycle – such as more precise resource planning, well-founded risk analyses, data-based decision-making processes and automated real-time reporting. AI-supported communication and collaboration tools, e.g. chatbots as meeting assistants, are also emphasised as productivity-enhancing. At the same time, PMI raises awareness of key challenges such as distortions in AI training data (in particular hallucination) or a lack of model transparency. The clear position: AI complements human expertise – it relieves project teams, but does not replace them.
In the strategic guide Leading AI Transformation, PMI recommends a dedicated Transformation Management Office (TMO) that manages AI initiatives centrally and aligns them with the corporate strategy. The transformation takes place in three phases: Initiation (definition of AI strategy, pilot projects, stakeholder alignment), implementation (scaling successful solutions, technical integration, active change management) and expansion (embedding in core processes, sustainable value creation). Challenges such as data quality, ethical standards and cultural acceptance are addressed – supported by best practices from leading companies (including Siemens, Tesla and Starbucks).
The key finding: when used correctly, AI demonstrably increases efficiency, innovative strength and competitiveness. Project managers are increasingly taking on a strategic role – as shapers of digital transformation.
2.2 IPMA study on AI in project management
The International Project Management Association (IPMA), together with PwC Romania, has conducted a global study entitled Artificial Intelligence Impact in Project Management to analyse the status and expectations when dealing with AI. The key finding: project managers clearly see AI as a support – not a replacement. Only 3% consider a complete AI replacement to be realistic. Instead, 52% expect AI as a digital assistant, 44% as an advisory body. The vision: human-AI collaboration in which AI takes over routine tasks while humans retain leadership, responsibility and final decisions.
More than half of those surveyed assume that AI-supported project assistants will become common practice within the next five years. And many companies are already taking action today: 56% have a digitalisation strategy that takes AI into account in project management. The expectations are clear – higher productivity, better decisions, stronger project performance. At the same time, there is a clear experience effect: those who have already worked with AI are much more open to its further use.
Despite challenges – such as unclear responsibilities or data protection issues – optimism prevails. AI can make project work faster, more flexible and more responsive. However, targeted investment in further training, data literacy and an adaptable project culture are essential for successful deployment. Soft skills are becoming increasingly important: communication, critical thinking and emotional intelligence are coming to the fore, while repetitive tasks are becoming increasingly automated.
In future, IPMA sees project managers as integrative leaders who create an effective partnership between man and machine. The role is changing – from operational manager to strategic leader.
2.3 PRINCE2 – Integration of AI into methodological guidelines
The latest version of PRINCE2® 7 is the proven project management standard’s response to technological change – in particular the growing role of artificial intelligence. The new ‘Data and Digital Management Approach’ actively encourages project managers to consider digital technologies such as AI in the project plan. This recognises this: AI is changing how projects are planned, managed and implemented.
PRINCE2 increasingly sees AI as an integral part of the project team. According to forecasts – from Gartner, for example – around 80% of administrative project management tasks will be automated by 2030. AI tools are already taking over repetitive tasks such as reporting or deadline management, giving project managers more freedom for strategic management.
The PRINCE2 guidelines provide clear recommendations: Organisations should continuously invest in training, launch pilot projects with AI and regularly review processes for automation potential. The development of data literacy is also crucial – project managers must build up basic knowledge of data analysis, AI logic and data quality in order to be able to use tools effectively and work together with data scientists.
The PRINCE2 AI Practice Guide emphasises that AI does not contradict proven methods, but rather increases their effectiveness: routine tasks are automated, while governance, risk management and quality assurance continue to be based on a robust framework. The role of the project manager is changing – away from day-to-day operations and towards forward-looking management and leadership.
Those who specifically integrate AI into the PRINCE2 methodology increase productivity, decision-making quality and project success – without losing sight of the principles of clear responsibilities.
3. Conclusion: comparison and evaluation
Despite different perspectives, PMI, IPMA and PRINCE2 come to a similar conclusion: AI is fundamentally changing project management, but above all it harbours opportunities – provided it is used in a targeted and competent manner. In all three frameworks, it is clear that project management with AI support is more successful when humans and machines combine their respective strengths. Surveys show that AI can have a positive impact on project success through greater efficiency, better decisions and increased performance. At the same time, humans remain essential in order to control and monitor technology in a meaningful way.
The organisations examined set their own priorities: PMI provides strategic guidelines and recommendations on how to scale AI company-wide and make it part of the PM methodology. IPMA sheds light on the attitude and maturity of the project management community and recommends investing in people and cultural change so that AI is truly accepted. PRINCE2, on the other hand, seamlessly integrates AI into its processes and demonstrates in a very practical way how project managers can utilise AI tools within existing structures – without abandoning proven principles. The following overview summarises key use cases of the three organisations in tabular form:
All three organisations are also reacting quickly and actively to technological developments: they offer targeted training courses, certificates and practical guidelines to qualify project managers for the use of AI. As a result, new skills profiles are already emerging – e.g. the project-managing ‘AI partner’, who brings together technology and teamwork.
What they all have in common is the call to invest in skills and change management now. Project managers should undergo further training and demonstrate learning agility in order to utilise AI tools effectively. The classic role profile is changing: from ‘project administrator’ to project designer who understands technology and leads people. All analyses emphasise that soft skills – communication skills, leadership, adaptability – will become even more important in the age of AI, while routine work will be automated. AI offers the opportunity to enhance the role of the project manager by relieving them of administrative tasks and creating space for strategic thinking. In practice – especially in data-driven industries such as finance and banking – this means taking action now: Through pilot projects and the use of proven AI use cases (for example in the automated analysis of regulatory project reports or the predictive management of a project portfolio), organisations can quickly gain experience. At the same time, it is important to involve employees, reduce fears through transparency and develop new skills profiles (e.g. project managers with an affinity for AI and data). The current trends and recommendations of PM organisations provide a clear direction for this.
4. Outlook: More radical approaches in project management of the future
In addition to established developments, it is also worth taking a look at more radical, visionary AI approaches:
One possibility is fully autonomous project management, in which AI takes over all planning, control and monitoring tasks – with only minimal human intervention. Other concepts are aimed at AI-based decision-making without human approval, in which algorithms independently decide on priorities, resources and measures. The idea of AI in management roles goes even further, with artificial intelligence acting as project manager or even CEO – real examples from Asia show that such models are already being tested. These radical scenarios show: The use of AI in project management is only just beginning – those who think boldly will be able to create completely new forms of organisation in the future.
