Will AI Replace Project Managers?

Project managers are the glue that keeps teams together. They are the maestros conducting the orchestra of timelines, potential blockers, conflict resolutions, and opportunity discoveries. But for many, project management isn’t a job title – it’s one of many “jobs to be done” one struggles to balance – and a job that often gets in the way.

In the midst of the AI boom, a question then arises: will project managers be replaced by AI? Let’s take a look at where AI project management is going in 2023 and beyond to determine how AI might evolve or replace project manager roles within the software industry.

What is (and who is) a project manager anyway?

Let’s break down the role of a project manager before discussing what parts of the job are ripe for takeover by our robot overlords. For the purposes of this article, a “project manager” is anyone that manages or oversees projects, not just someone with “Project Manager” in their title. On software teams, this could look like many people – from engineering leads to scrum masters to product managers and even developers from time to time.

Duties this person may be carrying out include:

• Planning meetings to ensure alignment among collaborators – brainstorming sessions, Issue estimation, backlog refinement, stand-ups, etc.
• Recording tasks and project goals and building roadmaps – ie., creating “Epics” and “Issues.”
• Produce reports for external stakeholders on team and project performance.
• Ensure the project moves along, getting regular status updates from team members and identifying and removing blockers and dependencies.

Note – this is not an exhaustive list, but it gives us a basis for analyzing where AI may take the wheel.

Where AI is right now: you get what you give

If you’ve spent even a millisecond exploring today’s budding AI solutions–ChatGPT, Bing, Google Bard, etc. – you know that AI is only as useful as the prompts and data you feed it.

Because of this, its primary use cases as of 2023 are:

• Summarizing data
• Sifting through data to generate suggestions
• Compiling data from disparate sources

In the world of AI project management, this will likely mean that the first areas of their jobs to become AI-powered will be in the last two points above (ie. producing reports, gettings status updates, and identifying blockers). While this will be massively helpful to everyone on the team, this is only a small percentage of a project manager’s role.

Can AI really plan projects?

Despite some of the challenges with prompting AI for desired outputs, it’s hard not to notice how powerful natural language processing (NLP) is becoming. This really brings into question whether or not AI can actually plan a project. As far as the software community is concerned, the first instances of this are coming to us in the form of AI-generated Issue metadata.

You can see an example of this in Zenhub with AI-generated Issue labels, which use historical issue data to generate suggestions. With this and other early examples, it’s easy to visualize how AI-powered ways of recording project plans may eventually start to make their way into higher-level planning.

However, organizations and teams have unique challenges, goals, ways of working, and preferences to consider that may limit AI. Let’s take a look at some of those limitations to get a better idea of what AI might not take over:

Managing teams that lack historical data

AI thrives off of repetition and pattern recognition. For teams that lack historical data – eg., if they’re new to working together, have a project that is new in nature (ie. not similar to what they’ve done in the past), or just don’t have a record of past projects, AI may struggle to provide accurate information such as appropriate timeline suggestions.

The inability to initiate projects

AI is great at summarizing information. So, in the future, finding out information about a project will likely be a matter of consulting a bot. But getting a project rolling in the first place – making sure all the right people are aware of it, are ready to prioritize it, and understand why it’s important to the business – might require at least *some* human input to get the ball rolling.

The inability to understand cultural differences and preferences

Whether it’s cultural traits that are unique to an organization, a group of people, or a whole industry – this lack of context may inhibit AI from making suggestions that are relevant to new projects. This would especially be a challenge for those working with clients or other external stakeholders whose preferences and ways of working may differ from their internal team.

AI project management tools will benefit “non-project managers” the most

Perhaps the biggest benefit of the AI boom is its potential to provide relief to “non-project managers” – these are the amazing people on your team taking charge of projects without “project manager” in their job description. With AI, these team members can spend less time performing this role off the side of their desks and can focus more on core duties.

Some tasks these team members might start to leverage AI to do are:

• Daily standups and socializing status updates or project blockers – this could be easily summarized by AI.
• Story point estimation – story points may be suggested by AI based on the team’s historical issue data.
• Backlog refinement – AI might be able to suggest Issue prioritization for future sprints.
• Task categorization – AI may suggest task categories, like tags and assignees. (We’re already seeing this with Zenhub’s AI labels).

In the software world, the impact of helping these “non-project managers” free up their time is significant – especially for tech startups with little resourcing at their disposal. For these folks, the demand for status updates and project reports can derail tactical work and cause serious burnout – all issues that (hopefully) will be a thing of the past thanks to AI.

AI for project management is about letting humans be the intelligent ones

It’s unlikely AI will entirely wipe out the role of “Project Manager.” It will, however, enhance project managers’ capabilities and give them more time to be strategic, think outside of the box, and make decisions that work best for their team.

Because AI’s core limitation is understanding project nuances, for full-time project managers, I could see this acceleration making it more necessary for PMs to become more specialized in their respective industries to better fill in the gaps left over from AI. For the software industry, this may look like PMs brushing up on their technical skills or getting more in tune with customer and business needs to add greater value to project planning.

These are, of course, just our predictions. Only time will tell how AI will fully impact how we manage projects in the future, but in the meantime, we recommend joining Zenhub’s AI wait list for updates on AI project management tools for software teams.