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Disrupting Project Management—Preparing for AI Collaboration



Gartner projects that by 2030 artificial intelligence will replace up to 80 percent of the tasks managed today by project managers. This isn’t necessarily a bad thing. Project managers manage a myriad of granular tasks that could (and should) be offloaded to smart machines so that PM’s can focus on implementing more complex, high-risk initiatives. 

We may be moving at a faster clip.  
In a couple of years we can expect more companies to seek out project managers skilled in navigating and leading the charge of implementing complex AI projects. And if you have direct hands-on change management and operational experience in addition to PM capabilities—you’ll be in a sweet spot position.
Why not use this opportunity to reinvent ahead of the curve? 
This brings me back to one of the more interesting topics during last year’s MIT EmTech Next conference with Julie Shah’s presentation: Robot See, Robot Do: Exploring Human-Machine Collaboration where she shares her team’s development work on robots being trained on the highly complex work of human experts. 
You can catch Shah’s talk here. Key callouts of her presentation as it relates to this post:

  1. Training a robot to collaborate with humans isn’t as easy as it might seem. We learn through observations and apprenticeship processes, developing and refining our skills to infer what others are thinking, anticipating what people will do next, and then making quick adjustments when things don’t go according to plan. 

  2. How do we emulate the process of learning from complex workflows and teach robots the same process? 
    • How do we take insight from how people learn the flow of a real world environment—leverage insight from well established cognitive models for how people learn in these situations and then design structured computational models that give the robots a scaffold for observating us in our messy world and learn how to quickly collaborate with us in difficult environments that include predicting the decisions that team members will make.

  3. Robots must be able to manage three sequential capabilities in becoming a member of a flexible work team:
    • Be able to refine shared plansin the real world (broadstrokes)—how do robots detect changes in strategies based on work load or whether team members got enough sleep the night before—how can robots learn through observation when real-world data is difficult to collect and inefficient for robots
    • Be able to execute the shared plan—understand where the team is at different junctures and what team members will likely do next
    • Be able to make quick adjustmentsas needed in order to effectively work with us

  4. Getting humans to collaborate with smart machines may be even trickier. Julie Shah shares in the video an experiment where they replaced a human team member with a robot and how the dynamics between humans and the robot shifted. The humans acted differently. They hoarded their work and decoupled work tasks—they didn’t want to share with the robot. 
    • We’ll need to develop empathy models for humans in order for us to collaborate with robots—smart machines are alien to us.
    • There’s a threat to our ability to truly leverage this technology if we don’t do the work on the human side first. Reducing the cognitive burden on humans will be a real game changer.
Will you be ready for AI collaboration? Be sure to check out our Seeding Change site for our upcoming online course AI in the Workplace that we’re excited to be launching this summer. Be sure to share your ideas with us on what you’d like to see included in this online course—now’s the time!