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By Dan Picard
Editorial Director, PM Skills®
Proponents of artificial intelligence claim that it will revolutionize the field of project management, and recent statistics from the web seem to support this claim. One site says that AI is already being used in more than 20% of projects, with its importance expected to grow exponentially in the next few years. Another suggests that more than 80% of project tasks will be eliminated as organizations incorporate AI into their operations.
So what does all this mean for project leaders? What role will they play in leading projects? How will their responsibilities be affected? And what does AI use mean for inexperienced project leaders who are looking to gain a foothold in the profession?
Benefits and Cautions
There’s no doubting the benefits that AI can provide for project leaders. Among other benefits, AI could help project participants:
- plan projects faster.
- prioritize projects and tasks quickly.
- perform statistical analysis and statistical modeling at an accelerated pace.
- automate repetitive tasks.
- gain quicker access to historical information.
- incorporate lessons learned into ongoing work more easily.
- generate creative decision-making options.
But AI will only be as good as its programming. If AI programming is insufficient (especially early in its implementation), the system may not recognize the gaps in projects that experienced project leaders may see on the horizon. Or the system may not suggest the appropriate corrective actions and adjustments that would need to be taken. It will also likely be more reactive than proactive, waiting for triggers to prompt programmed responses. And it may automate tasks faster—but if those tasks turn out to be in error, then the system will just make mistakes at high speed.
And What About the Project Leader?
I used to work as an editor at a small publishing company. For each project I worked on, I would be asked to create an initial set of graphics. I would spend a good amount of time creating and refining the graphics to the best of my ability, and then I would pass the images on to our professional graphic designer for adjustments. Then the magic would begin: she would shift one shape slightly to the left, add a drop-shadow to another, tweak the text and icons, and suddenly, my best attempt was now hundreds of times better and looked like a piece of art.
That’s what will have to happen with AI and project management. Artificial intelligence will collect information and collate it faster than project leaders can. It will automate the (often mundane) tasks and activities that take up much of their time. It may even uncover things that project leads may not have thought of yet and offer creative ways to address those risks and issues. But it will be up to the project leader to evaluate and analyze those suggestions and to implement them holistically to ensure that the project will continue to meet its goals. It will take the understanding, knowledge, and experience of the project lead to know when, why, and how corrective actions need to be implemented. Only then will the “initial graphic” be magically turned into a “professional image.”
The Evolution of Roles
But this brings up another important point: how will the project leader’s role change if they no longer need to capture and collate information? And what does this mean for new project leads who don’t have the experience to fall back on to help make these next-level decisions?
The ability of AI to do the intelligence-gathering on projects means that project leads will now have to become more strategic than tactical in their everyday lives. They will need to be more proactive, more forward-thinking, and more creative in the ways they address their responsibilities. They will now have more data available in a shorter period of time, but will likely also need to know what to do with that data in the shortened time frame (and they will likely be expected to respond more rapidly, as well). And they’ll need to know what each data point (and the trends associated with these points) means at a granular level—not just that “the value in a particular cell is greater than one, so the project is ahead of schedule” but also what that “one” actually means, what information went into the calculation of the value of that “one,” and what factors could change that “greater-than-one” value to a “less-than-one” value.
This will, of course, make the life of the inexperienced project lead more difficult because they won’t have the depth and breadth of experience to rely on to help them evaluate and understand the nuances of their projects. This inexperience will make it harder for them to navigate their way through the multitude of data points and suggestions offered by AI systems, so they’ll need more mentoring and training to help steer them through dangerous times. This mentoring may then increase the workload of their more experienced peers, adding additional layers of complexity to already complicated work schedules. But for AI to be effectively integrated into project management practices, these evolutionary activities must be embraced and reinforced to ensure that the profession will continue to grow and thrive.
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