The Growing Importance of Engineering Project Management Driven by Generative AI

PADT’s simulation and product development teams are huge believers in project management. And the AI revolution is strengthening that belief. Generative AI is changing almost every profession, personal relationship, and creative endeavor. In the early days of this revolution, those of us who do engineering, with the exception of software engineering, could take a wait-and-see approach because the tools were not yet good enough to handle the math- and process-driven work we did. But we knew it was coming. The entire list of tasks engineers perform to specify, design, test, and produce hardware, chemical reactions, and infrastructure would be enhanced by generative AI.

Well, although it is not across the board, it is starting to make its way into our profession, and those of us who engineer the world need to figure out how to adapt. We know better than most people that a more effective and efficient way of doing tasks will eventually take over.

Here at PADT, we are taking two approaches. The first is to learn the new tools and apply them where applicable, and the second is to double down on project management (PM).   

This post is the first of a series exploring that second response. While generative AI will make project management more effective, the increasing adoption of AI in engineering will make project management even more important.

Someone Has to Manage What Tasks are Done by AI, Engineers, or a Combination

For a given project, someone has to decide how each task is going to be done and if it will be completed by an engineer, some sort of AI tool, or most likely a combination of the two. The Project Manager will need to learn what the options are and make the right calls.

This is no different from the current role of project managers in monitoring and managing resources. If anything, it makes the PM role more important. It is similar to deciding if a set of tasks should be done by in-house teams, consultants, or offshore resources. Project managers will need to do the same cost-benefit-risk analysis and track things to learn what did and didn’t work. This will require a deep understanding of the available tools or access to experts who can provide the needed input.

Someone Needs to Watch the Black Boxes

In a traditional engineering project, there are people at every step of the process who know what is going on for a given task. When AI becomes more pervasive, many of those tasks will be done simply by providing input and reading output, and there is a chance no one will know what is going on inside the tool. It will be a black box. And someone needs to watch the cost and output of those black boxes.

Right now, that role should probably fall on the project management team. Maybe over time, a new role, AI Supervisor or something like that, will evolve. But at this point, that human oversight is best handled by project managers. And part of that oversight is ensuring the output of AI tools is accurate. Design review may start to include AI chatbots, but that still needs to happen.

Budget and Schedule Are Still in Charge of Every Project

The promise of generative AI is the ability to get better engineering faster and for less money. The project manager’s role will continue to be monitoring the budget and schedule to ensure this remains true. We still don’t have a handle on the cost of these new generative AI tools. We also assume they will do tasks faster than humans. The PM team will need to gather data and get accurate information they can make informed decisions with.

Project management teams are already using AI tools to monitor budgets and schedules and create reports and dashboards. Over time, this will become more important because there won’t be as many human engineers in the loop to raise yellow or red flags when things start to go wrong.

Join Us as We Figure This Out

This post just scratches the surface, highlighting a few reasons we think project management will become more important as we transition to this new way of doing engineering in our consulting team, where we deliver simulation and product development services. In future posts, we will look at ways to use AI for better project management, the challenges it presents, and some questions we don’t yet have answers to. We will post on the PADT Blog and on PADT’s LinkedIn. Join us on the journey, and maybe together we can figure this out faster.