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Where can generative AI improve project management efficiencies and qualities?
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Published
27 November 2024
Generative AI – a catalyst for revolutionising traditional project management processes
In today’s rapidly evolving business landscape, AI offers experienced project managers – and, honestly, just about anyone working with or on projects – new ways of enhancing productivity, reducing risk and boosting quality.
In this article, we explore some of these efficiencies, guided by our experiences applying generative AI in project management and teaching AI in Project Management.
The role of GenAI in project management
Project management is inherently complex. Balancing timelines, stakeholders and resources can be overwhelming and often keeps project managers awake at night. For most project leaders, the challenge is not just meeting targets but consistently improving efficiency and delivering high-quality results. In short, we extend the invitation and idea that GenAI offers a promising solution to this conundrum. Not to replace project managers but to serve as powerful enhancements to existing project management tools and practices.
To manage expectations and interaction with this new technology, think of GenAI as your new colleague – always ready to help, analyse and collaborate, with roughly the same caveats as if you were literally onboarding a new colleague to your project-leading reality.
Enhancing efficiencies
The efficiency-enhancing power of using GenAI tools comes from its ability to help project managers do more with less. In an Implement Consulting Group meta-study of research assessing productivity efficiencies in individual working tasks, we point to an average productivity boost of as much as 32% . This is in essence what allows project leaders – as well as project teams – to free up valuable time to be spent on other value-adding activities.
The efficiencies, however, do not serve to replace project managers, but rather we argue that GenAI in a project setting should be seen as an assistant that helps tackle repetitive tasks, analyse project plans and even brainstorm on risk scenarios. This enhancement allows project leaders to focus on the human aspects of their role, e.g. decision-making, team leadership and creative problem-solving.
Improving quality
Project quality often correlates with the time and energy spent on planning and monitoring. Yet, in the fast-paced environments in which most project leaders find themselves, these tasks are often rushed. We view GenAI usage as a quality enhancer in helping project managers reduce the mental burdens of planning and monitoring and help them focus more on strategic planning and more de facto steering.
As an example, GenAI is used to great effect in writing project briefs, brainstorming on risks, generating risk assessment or analysing the stakeholder landscape – all tasks contributing to a more structured and thorough project plan.
An interesting way to think about GenAI in a project setting is as a “sparring partner”. Imagine you have a draft project timeline. GenAI can be used to review it, suggest adjustments or flag potential issues. It is like having a second set of eyes that is always on call. This is not just about speed – it is about improving the quality and robustness of the planning process, leading to better project outcomes.
Different roles for different tasks
Let us get back to the notion of thinking of GenAI usage as a “new colleague”. While this serves as a good analogy for how to think of and interact with GenAI tools, we find it beneficial to extend the line of thinking into four different roles that GenAI can assume when helping project managers solve certain tasks:
When we engage with and train others or in our day to day-to-day work as project leaders, we tend to categorise our tasks and ways of using GenAI in four:
- “AI as an expert” for dialogue applications
- “AI as an author” for generating content
- “AI as an analyst” for analysis of existing content you have developed, have sent or found elsewhere
- “AI as an editor” for modifying the content – across formats, contexts or languages
In general, we invite project leaders (but also, practically everyone else engaging with GenAI tools for daily working task) to think: “If you had an editor, expert, analyst or author by your side – 24 hours a day – what are you going to ask for help with?“
In project settings, here are some examples of tasks that the four distinct roles may assist with:
- AI as an expert – from prompt to expertise
GenAI can provide on-the-fly explanations of concepts you may not fully grasp or help spar on approaches or issues you face in your projects. Do you need to understand the implications of integrating an API into your project? How to cope with a person that comes from another background and cannot conform to the waterfall approach? Or typical pitfalls when engaging in cross-time zone collaborations?
GenAI can be used to help inspire you on approaching these questions and likely many others. - AI as an author – from prompt to content
Whether drafting an email to stakeholders or creating an initial project charter, GenAI can be used to help generate content faster, turning initial thoughts into a quick, rough draft, requiring you to spend time critically reviewing content and finalising – leaving you more time for critical analysis and strategic thinking in relation to your project.
Again, think about what you are writing in the scope of your project work, and what could you reasonably expect a GenAI-based “author” to help you draft? - AI as an analyst – from content to information
The analyst role is where you, as a project leader, have something at hand (documents, texts, pictures, etc.) where you are looking for an outsider’s perspective or to extract some level of information. It might be useful to analyse a stakeholder plan and offer insights into potential risks or missed connections, or it might be useful in assessing e.g. “How would my technical project group perceive this project plan?” – much like a seasoned analyst would do. - AI as an editor – from content to (new) content
The AI as an editor role involves transforming content from one version to another. Imagine a document or a piece of text that is very detailed and technical to be explained to the steer-co or to a non-technical team member. This is where “AI as an editor” comes into play. It can be used to simplify complex terminology or even help with standardised communication tasks, such as turning a series of PowerPoint slides and emails into a steer-co email draft.
These four roles are of course not meant to anthropomorphise the technology, but rather we employ them as ways of thinking about GenAI use, helping project leaders and other recipients think about and facilitate the translation of daily working tasks into Generative AI usage scenarios – helping to assess where we may benefit from the use of GenAI tools throughout a project life cycle.
A balanced approach to GenAI usage
As we use, discuss, train and engage with GenAI in a project management setting, we make a key distinction between what we label as “process vs product”. While on one hand, we subscribe to the notion that GenAI can greatly accelerate the creation of deliverables and communication (“the product”), we believe it is crucial to balance it against the value realised from the collaborative elements of humans engaging in project planning and similar settings (“the process”). Engaging with stakeholders, discussing plans in workshops and aligning visions and plans are irreplaceable aspects of humans engaging in the human ways of doing project management. However, GenAI serves as a worthy jumpstart to some of those processes – providing a baseline project plan or risk assessment to discuss, for example – so that the human inputs become more focused and more effective.
In practice, this means that GenAI applied in a project setting should be viewed as flexible. Project managers need to figure out when and where it makes sense to start with GenAI (such as a risk overview) and when to start with a team, in a meeting room, drawing on a board. But even if you start one way or the other, with multimodal capabilities in most GenAI tools, we are not limited and can quickly digitise any physical artefacts that we create during the human phase and then collaboratively refine it with our teams.
The result? Faster processes and turnarounds without sacrificing the quality gained from team collaboration and brainstorming.
GenAI as a catalyst for better project management
From our own experiences and from project leaders we engage with, most see GenAI as a potential game-changer in project management. Not by replacing the human element but by enhancing it.
GenAI can be used to help tackle the repetitive, tedious aspects of project work, providing more bandwidth for strategic thinking and team engagement. There are also parts where GenAI is used and tested in project management settings, where it does not perform as hoped. It is about exploring those tasks, getting a feel for where it makes sense and perhaps also your individual stance on “product vs process”, and the question is: in which situations can you augment your project management skills, and where do you (still) need to engage with teams and peers to steer the project?
Thinking about GenAI as a new colleague that can take on different roles for different tasks is a first step, but it is also a colleague that never tires, that always is online and that has a vast knowledge base – always ready to help you brainstorm, create, analyse or transform.
We believe that the future of project management is not about choosing between humans and GenAI – it is about understanding how to make the most of both. Therefore, we encourage you to explore and let GenAI be your partner in navigating the complexities of project work and handling tedious tasks so you can focus on what matters most: delivering value and leading your team to success.
Sources
1) Chang & Noy, MIT (2023), Peng & Kannan, Microsoft Research (2023), Nielsen, Nielsen Normann Group (2023), Tabachnyk & Nikolov, Google (2023), Goldmann Sachs Research (2023), Chui, et.al., McKinsey & Company (2023), Dell’Acqua et al., MIT, Havard, Boston Consulting Group (2023), Brynjoflsson, Li & Ray-mond, National Bureau of Economic Research (2023), Korinek, National Bureau of Economic Research (2023), Congressional Budget Office (2023)
2) A question we are often asked is how we work with this. One thing is to rely on the knowledge built into GenAI systems, but there are also other ways of embedding knowledge into GenAI systems to use past pro-ject learnings, see e.g.: https://implementconsultinggroup.com/article/building-high-quality-rag-systems
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