Interview – Bridging AI Hype and Reality in Project Management

Are we expecting too much from AI, or have we not yet tapped its true potential in
managing work? To dig into this question, I spoke with Dr. Jane Atkins, a professor at
Stanford University and an expert in organizational technology. Dr. Atkins has a unique vantage
point: she’s seen countless “AI in business” fads come and go, but also works closely with
companies genuinely reinventing how work gets done. Our conversation focused on how to
ensure AI’s promise (especially the true intelligence we’ve been discussing) translates into realworld results for teams. Below are highlights from our interview, shedding light on what’s hype,
what’s real, and what’s next.
Q: Many tools today claim to use AI for productivity. What’s missing in the current wave of
AI project management tools?
A: (Dr. Atkins): “Most current tools are still fairly narrow. They might automate a simple task
or give you a basic recommendation, but they lack contextual understanding. I’ve observed
teams try out AI add-ons that, for example, auto-prioritize tasks — but the minute something
unusual comes up (like a sudden change in client requirements), the AI’s advice goes haywire
because it doesn’t truly grasp the project’s shifting context. The missing piece is what we’d call
domain intelligence: the AI needs to understand the environment it’s operating in, not just follow
a generic algorithm.”
She gave a concrete example of a pilot she oversaw: a project management AI suggested
reassigning a critical task to a junior employee because its algorithm saw that the senior engineer
was overloaded. In isolation, that might seem logical, but the AI failed to recognize the task’s
complexity and the junior member’s lack of experience. “A human manager wouldn’t make that
swap because they intuitively understand the skill match and stakes involved,” Dr. Atkins noted.
“The AI didn’t have the full picture – it was optimizing one dimension (workload balancing)
without understanding quality and risk.” This anecdote underscores why “true intelligence” is so
important – an AI needs a multifaceted understanding of projects, not just single-metric
optimization.
Q: How can organizations move toward AI with true intelligence? Is it about technology,
data, culture…?
A: “All of the above. Technologically, one promising route is integrating knowledge bases or
graphs into AI, so it has a memory of past projects and a web of relationships between data
points. That way, it isn’t starting from scratch every time or making isolated judgments – it’s
drawing on a broader understanding. Culturally, companies need to trust AI with more than
trivial tasks. In our research we found that when teams treated the AI as a partner rather than a
tool, they got far more out of it. For instance, instead of just accepting whatever the AI
suggested, project leads would have the AI explain its reasoning, then discuss as a team. That
interplay actually helped train folks to think more systematically too. It’s like having a very
analytical team member that forces everyone to up their game.”
Dr. Atkins pointed out that companies leading in this space often invest in high-quality data and
domain models. “One tech firm I know created an internal ‘project graph’ of all their past
project data. When they plugged an AI into that, its recommendations became remarkably
insightful, almost like it had inherited the collective experience of dozens of project managers.
We saw error rates drop and timeline estimates improve significantly.” Indeed, she mentioned a
finding from industry analyses: when AI is given rich contextual data, some organizations have
seen project delivery speeds improve by 20-30% internally, beyond just the basic 30% time
savings in admin work that McKinsey notedhashstudioz.com. It shows that efficiency gains can
compound when AI moves from shallow to deeper understanding.
Q: What pitfalls should teams watch out for when implementing such intelligent systems?
A: “One big issue is over-reliance without understanding limits. I always say: treat AI as a
genius intern, not a seasoned executive. It can digest information and spot patterns at
superhuman speed (no human can read a thousand status reports in a second and draw
conclusions!), but it still lacks the full common sense and ethical judgment of an experienced
human. We’ve seen cases where an AI-driven scheduling tool pushed teams toward
unrealistically short timelines because it assumed everyone could be 100% productive with no
downtime – clearly not how humans work! Another pitfall is data bias. If the historical data
contains biases (say, consistently underestimating testing time), the AI can actually reinforce
those mistakes unless someone intervenes.”
Her advice for getting it right is to keep humans in the loop: “The best outcomes happen when AI
and humans collaborate. Let the AI crunch numbers and propose insights, but have the team
validate and adjust those insights. In essence, augmented intelligence rather than autonomous
intelligence. That’s how you prevent disasters and get the best of both worlds.”
This interview with Dr. Atkins highlights a critical juncture: to unlock AI’s full potential in
project management, we must infuse it with true intelligence and use it wisely. As we move
forward in this blog series, the focus will shift to how our own product, ActionBoard, tackles
exactly these challenges. Armed with expert perspectives and lessons learned, we’ll see how
ActionBoard’s design attempts to embody true intelligence – from integrating rich context via an
ActionGraph to keeping humans in control of the narrative. In the next blog, we turn theory into
practice, introducing how ActionBoard.ai is bringing these ideas to life for teams today.