By 2030, 80% of project management tasks will be run by AI [1].

“AI is going to revolutionise how program and portfolio management (PPM) leaders leverage technology to support their business goals,” said Daniel Stang, research vice president at Gartner, the multi-billion dollar information technology research and advisory company on whose research that number is based. “Right now, the tools available to them do not meet the requirements of digital business” [2].

Ever since ChatGPT’s emergence in 2022 hastened talk of the so-called AI revolution, a number of scaremongering predictions have been thrown around regarding which jobs will stay and which made obsolete.

A more productive topic of conversation would be how AI is going to bring about change within roles. AI’s sophistication will need to develop before it starts affecting our day-to-day, but once it reaches that stage, which most predictions suggest it soon will, project management is sure to be one of the first roles affected. Key skills of project management such as scheduling, budgeting and monitoring of progress are more easily adopted by technology than positions that rely on creative thinking or interpersonal skills. As such, project managers will have to adapt.

The benefits of integrating AI into project management

Every year, roughly $48 trillion are invested in projects. And yet the Standish Group reports that only 35% of projects are considered successful [3]. Writing in Harvard Business Review, Antonio Nieto-Rodriguez and Ricardo Viana Vargas, Ph.D, put this down to “the low level of maturity of technologies available for managing [projects]” [4].

“Most organizations and project leaders are still using spreadsheets, slides, and other applications that haven’t evolved much over the past few decades,” they write. “These are adequate when you are measuring project success by deliverables and deadlines met, but they fall short in an environment where projects and initiatives are always adapting — and continuously changing the business” [5].

AI can help cut money waste and contribute to a greater project success rate. The key solutions it offers are: enabling a better, more transparent process for selecting and prioritising projects, an ability to tailor approaches specifically to each client, an enhanced ability to monitor ongoing projects in real-time, improved risk-management and testing systems, and freeing up project managers to focus on higher-level output.

The project selection process

As a company, choosing which projects you want to take on and which you want to prioritise is important. You’re going to be investing company time and resources, potentially for a number of months or years. You want to choose a successful venture/client/collaboration that will bring value and prestige to your organisation. AI can help.

AI capabilities like machine learning provide a level of accuracy in predicting which projects will be worthwhile in a way humans cannot, using data rather than gut-feel. Through an assessment of existing company data, AI can offer faster identification of which projects align with the company mission and which are likely to succeed. This approach also removes human bias from the decision-making process.

Once the project is selected, AI can then help tailor that process to each specific client by assessing how similar projects have worked in the past and either following a similar template (for successful projects) or trying something new (for unsuccessful ones). On top of that, accurate cost estimation can avoid an over-allocation of funds or, worse, an insufficient estimation that either leaves you crawling to your client looking to change your invoice or being forced to work additional unpaid hours.

Monitoring ongoing projects

In an experiment, Peter Kestenholz, founder and Head of SPM Innovation and AI at Projectum, had AI tools create work schedules based on a context initiative described by project managers.

Kestenholz used the prompt: “Global roll-out of a new ERP system to five regions following the PMBOK methodology, starting the second week of May 2023 and to be finished a year after.” The result? “In roughly 30 seconds, a full schedule was returned, containing all the requirements, the proposed task duration and a score that reflected the likelihood of the AI’s estimation of the task duration” [6].

He noted that the better project managers described the project at hand, the better and more specific a schedule AI produced, adjusting as further details were added.

As this technology becomes more sophisticated, project managers will be able to monitor projects in real-time, tracking precisely how far ahead or behind the curve they are. The AI schedule will adjust according to this information, letting them know whether they need to up their efforts to get back on schedule or are able to continue as is.

Nieto-Rodriguez and Vargas say these intelligent tools can optimise the project management office (PMO) through the ability to anticipate potential problems as they arrive and address simple ones automatically. They can also automate the report process, help select the best methodology for each project, and update the approach according to any internal or external changes as and when they appear [7].

Risk management and advanced testing systems

A key feature of AI is that it can anticipate – and mitigate – risks that might otherwise go unnoticed. Of course, project managers, experienced ones most especially, will often be aware of potential risks too, having seen and overcome many before over a storied career. For this reason, it can be useful to rely on both the experience of a project manager and the AI tools to ensure nothing slips through the cracks.

If the AI is fed data on potential risks, it can save the project manager the trouble of scouring the project constantly, as well as being able to flag the risk the second it emerges, rather than being dependent on whether or not the manager is busy elsewhere. For less experienced project managers, having a full risk log readily available can significantly improve productivity and help get them up to speed quickly and without error.

For software-related projects, advanced and automated system testing solutions allow early detection of defects and self-correcting processes. These advanced systems can ensure all bugs have been spotted and dealt with prior to release, saving recalls, and ensuring no faulty systems or products make it to the market. Such testing is already in use on large-scale projects, such as in constructing the Elizabeth Line in London [8], but will soon become commonplace for smaller-scale work too.

The role of the project manager

The existential fear is that AI will replace us. Project managers may read the capabilities above and wonder what their role is in the coming world. But the point is not to eliminate project managers, rather to streamline their processes. They will be in charge of these technologies, not the other way round. It’s vital that AI is overseen by someone who understands the information the AI is giving them – who is AI literate, essentially – and who is inputting useful information back into the system so as to allow for better insights.

Equally, by removing some of the more tedious, time-consuming aspects of the role, such as scheduling or writing up meeting notes, project managers will then be free to pursue more high-value work like meeting with stakeholders and coaching their teams.

“AI offers an opportunity to free up project manager time and allow them to do what they do best—lead teams and get the very best out of people,” says Peter Taylor, vice president of global project management at Ceridian and author of AI and the Project Manager: How the Rise of Artificial Intelligence Will Change Your World. “This will no doubt deliver far better results, fewer errors, more motivation, and greater success” [9].

The role of data

AI is trained on data. As such, the data that companies feed into their AI system’s are pivotal to how the system works. Writing in Harvard Business Review, Tomas Chamorro-Premuzic and Christine Boyce note how, “Large organizations with the time and resources to gather, clean, and structure their own data will be best positioned to maintain a differentiated position in the market” [10].

That said, it’s not just about company size. It’s about how data is used. AI will be trained to replicate the processes of successful projects in order to produce the same results. That means that prior to inputting that data into AI systems, businesses need to accurately assess which of their projects were successful and which fell short – something that sounds simple in theory, but in practice may require parking some pride. 

Chamorro-Premuzic and Boyce also acknowledge that, “while training data is a critical first step, care must also be taken to gather ongoing input — i.e., monitoring progress and results — in a way that collects what is needed without being so intrusive as to disrupt or impair people or the process” [11]. A balance is required. Getting that balance right is another responsibility of the project manager.

Another factor, especially during the early stages of the AI revolution such as we currently find ourselves in, is how much data companies feel safe inputting into AI systems. The security of that data is not yet clear and proceeding with precaution is advised. As such, we may only see tentative steps at first, but no doubt, as AI processes become increasingly normalised and security better trusted, project management will soon be centred around it.

The changing face of project management

AI’s integration in the business sphere is an inevitability. Due to its organisational structure and the benefits AI offers it, project management will be one of the first areas of integration. AI can help businesses select the right project, monitor that project in real-time, adjusting scheduling and approach as necessary. Managing and mitigating risks ahead of time will save businesses money and time, while advanced testing will help ensure nothing makes it to market without being optimised first.

Tasks that once belonged to the project manager will still fall under their remit, but their role will be based more on supervision, freeing up time for other vital tasks, not least training staff on AI tools and ensuring the best (honest) data is being put into the software. Until such a time as security can be verified, trusting all data to AI systems will not be the norm. But AI’s day is coming, and project management will be first to feel the shift.

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