When googling about Artificial Intelligence in Project Management you’ll find loads of articles talking about how AI will revolutionize and transform project management, how it will automate processes, etc., and how it possibly will eliminate the project manager role all together.
Reading most of those articles, you are getting the idea that AI indeed has the capabilities that we all know already — processing large amounts of data quickly, finding patterns in data, learning from it, making predictions. It is also clear that the project management practice where capabilities of forecasting project scenarios and outcomes, predicting the impact from risks and issues, estimating work, etc. are critical building blocks, is an obvious use case for the application of AI. There is therefore no doubt that AI will have a significant footprint in the project management area.
The most important question though is what it will do to project management from an organizational point of view, to established processes, methodologies, frameworks, etc.
Will AI just be another tool that will make the project manager’s life easier, just like Microsoft Project, Jira, Slack, etc.? Project managers appreciate such tools just like accountants appreciate tools like Microsoft Excel which made their life in many ways significantly easier.
Will AI be like a new project management methodology, just like Agile? Agile methods had a significant impact on project management, especially in technology/software development projects, which also led to new software tools (e.g. Atlassian Jira) and required a new mindset for adoption.
The impact of AI in project management however will be much broader and truly disruptive. AI will not be just a new tool nor just a new methodology, instead we can expect that it will fundamentally redefine the project management practice.
The reasons are in the nature of AI, alias in the domain of Machine Learning, and also in what project management is all about. When you look at project management processes, there are 3 elements that define project management in its core nature, which in my view are also the 3 major pain points in projects:
Uncertainty The expected outcome from any project always will be uncertain. Nobody can guarantee a project delivery on time, budget, in scope, and with agreed quality. It simply is not possible with current practices — unless the project manager has the unlikely ability to read the future from some crystal ball.
Forecast There are many techniques to forecast project activities and tasks, durations of tasks, potential bottlenecks, etc., and build a realistic plan around this in order to meet a target delivery date. What needs to flow into such planning as well is a forecast of issues that potentially will occur along the way, hence some diligent risk management and tracking is critical. We usually are horrible at forecasting — this is just human nature and we usually estimate too optimistically.
Learning There are not many domains where learning is so important as it is for project management — but in most of today’s projects, teams just swing from one to the next project without really learning from their experiences. Projects are said to be unique endeavours but that is simply not true — projects are never really 100% unique. When you are building a house, then you may build a unique type of house but there are many processes and work packages in such project which were done and executed precisely the same way in other house construction projects before. What is key here is to learn from such past projects and processes, adopt the things that worked, and avoid the things that did not work well before.
Unfortunately, in all those 3 areas, today’s project management practices are failing which is the cause of consistently low project success rates. And it is a natural consequence that capabilities in form of predictive analytics, AI, and Machine Learning have to be leveraged to address those critical items in the project management domain.
I therefore expect that the following key elements will define the future of project management:
Data-driven decision-making processes Decisions in projects so far are being taken in a very intuitive fashion, usually based on own professional experience, but often also led by political and bias-driven motivations. A change towards a data and fact-driven approach which will take into consideration past challenges, learnings and plain facts, is a fundamental and radical change and I expect strong resistance as it requires a true mindset change.
Leading products instead of managing projects Today’s project managers are too much involved in more administrative and repetitive tasks, although a lot of day-to-day tasks can be automated by using either Robotics or Machine Learning approaches. Project managers should focus more on strategic topics, driving product development in order to close the apparent gap between project management and product management.
Hybrid Intelligence The combination of machine and human intelligence in projects is an important element of future project management concepts, as it will combine the strengths of both worlds for an optimal project management approach. AI will not be able to provide people leadership, negotiate with clients, etc. — in all those more leadership and intuition driven activities, humans are superior to AI and can play out their strengths, while AI can focus on its strengths in terms of collecting and analyzing and learning from large amounts of data.
AI is here to stay in project management and while it is not yet clear, how exactly the new project management world will look like, it will change drastically and the way projects are being managed will never be the same anymore.
Project management processes will be rewritten.
Some roles will disappear and other new ones will be introduced.
Intelligent tools will hit the market.
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