At a recent half-day conference organized by the brilliant minds behind the “Six Team Conditions framework,” I found myself in a lively debate about the future of teams. Dr. Ruth Wageman set the stage, literally, by drawing a line of tape on the floor and inviting us to pick sides: Are teams today really different from the past, or is this just the same old wine in a snazzy new bottle?

I (mostly) confidently stepped over to the “New Bottle” side, though I was in the minority—about 30% of us, including Ruth herself. I felt like I was in good company! Why “New Bottle”? Well, I’ve spent years absorbing the work of the late J. Richard Hackman, and while his theories on team effectiveness still ring true, the context teams operate in has shifted in ways even he might not have fully anticipated.

Nothing New: Great Teams Thrive on Change

Hackman’s research laid the foundation for understanding what makes teams work. He famously said, “What matters most for team success is not the personalities or behavioral styles of the team members, but whether the team has a compelling direction, a strong structure, and a supportive context” (Hackman, 2002). These principles still hold, but the playing field has changed.

Teams today face relentless, rapid change. Whether it’s new technologies, shifting market dynamics, or global disruptions like pandemics, teams must constantly pivot. The truth is, high-performing teams have always conquered change. Hackman’s work showed that teams excel when they are designed intentionally, placed in challenging environments, and required to be resilient and adaptable. So no, this isn’t a completely new vintage—it’s the same classic recipe poured into a fancy, modern bottle.

A New Label, Same Grit: Teams Still Need the Basics

But, let’s be honest, the bottle has changed! Today’s teams are dealing with unprecedented challenges—remote work, technology-driven collaboration, global crises—all while expected to perform at their peak. So, while the wine (our foundational understanding of great teams) is still the same dependable Cabernet, today it’s wrapped in a chic, high-tech bottle that’s designed to impress and survive in tougher environments.

Think of it this way: we’ve swapped the cork for a sleek screw-top to handle the pace of today’s world. We’re still savoring the depth and complexity of Hackman’s proven theory, but in a bottle that’s ready for anything—hybrid teams, AI integration, global market shifts. In short, same recipe, but now in a new, spill-proof, travel-ready bottle for the modern team.

Enter AI: A New Team Member

Now, let’s talk about this new addition to the mix—AI. It’s no longer just a tool we lean on; it’s becoming a full-fledged team member. In my house, for example, we are solidly a “Hey Google” family. There are times when I’ll ask Google a question at the dinner table, and, out of habit, I always say “thank you.” My kids think this is hilarious. “Mom, it’s a machine!” they laugh. But for me, it’s about the energy I bring into the room, even if Google doesn’t have feelings (yet!).

Soon, though, AI won’t just be sitting on our kitchen counters—it’ll be part of our teams at work. We’ll be collaborating with AI, relying on its insights, and maybe even letting it make decisions. But this new addition to the bottle brings up big questions. As Dr. Wageman pointed out during our discussion, “When we start treating AI as a legitimate team member, we might shift the boundaries of what teams are” (Wageman, 2024).

AI Bias and Ethical Concerns: Who’s Making This New Label?

Here’s the thing—AI isn’t all smooth edges and shiny labels. Like any new bottle on the shelf, you’ve got to check the fine print. Who’s making this fancy new label? As Dr. Joy Buolamwini reminds us, “AI is not neutral; we are feeding it our biases” (Buolamwini, 2018). So, when AI starts offering insights or making decisions, we have to be mindful of the fact that it’s still very much human-made—and therefore subject to human biases.

We’ve seen AI missteps before—biased hiring algorithms, facial recognition systems that misidentify people of color, data that reflects existing inequalities. So, the question becomes: How do we ensure that our shiny new bottle isn’t contaminated with the same old problems? How do we trust this new team member without blindly accepting it?

The Implications for DEIB and Leadership

For those of us working to foster inclusive, high-performing teams, Coqual, a nonprofit global think tank’s research on diversity, equity, inclusion, and belonging (DEIB) is clear: creating environments where people feel they belong is key to unlocking their full potential. Our 2021 report, The Power of Belonging, found that employees who feel a strong sense of belonging are more engaged, more productive, and less likely to leave. But what happens when a non-human team member enters the mix?

It’s one thing to create psychological safety for human team members, but what happens when AI—despite its efficiencies—starts making decisions or replacing human intuition? AI can’t feel, but humans sure can. What happens when your ideas are sidelined by an algorithm, or worse, when AI is biased against certain groups because of the data it’s trained on?

Hackman’s work emphasized that great teams need structure, clear goals, and leadership that supports collaboration. AI can enhance performance, but we need human oversight to ensure that decisions are ethical, inclusive, and fair. We don’t want to end up with a beautifully labeled bottle that’s filled with the same old biases and blind spots.

Inclusive Dream Teams of the Future

If AI is part of our teams now, we need to rethink how we design those teams. Leaders must clarify where AI excels—data crunching, pattern recognition—and where humans are irreplaceable—creativity, intuition, and empathy. We need teams that leverage the strengths of both, without diminishing the human element.

At Coqual, we often talk about “listening with curiosity.” This applies not just to human team members, but to all sources of ideas. As AI starts generating insights, it’s critical that we balance excitement for what it can offer with human oversight to ensure our values—especially fairness and inclusion—stay front and center. After all, AI might be the shiny new bottle, but we need to make sure we’re pouring it thoughtfully.

DEIB in a World with AI: Beware the Expiry Date?

As we move forward, DEIB in a world with AI is going to raise some pressing questions. For instance:

• How do we ensure that AI doesn’t perpetuate bias, especially in crucial decision-making?
• How do we create inclusive environments where all voices—human and AI—are valued but held to ethical standards?
• Can we design teams that leverage AI’s strengths while fostering a sense of belonging for every human member, regardless of their interaction with AI?

We at Coqual know that inclusive leadership is key to building teams that are not only diverse but also feel psychologically safe. As AI enters the team dynamic, we need to make sure it supports, rather than overshadows, the human strengths that make teams truly innovative and adaptable.

The Future of Teams: More Than Just a Shiny Bottle

As I left the conference, I couldn’t help but laugh at how fitting the wine metaphor really is. We’ve swapped out the old, corked bottle for a new, sleek version that’s built for today’s fast-paced, high-stakes world. But it’s still the same principles inside. The wine hasn’t changed, but the bottle—and the world around it—has.

So, while we don’t have all the answers yet, I’m optimistic that with curiosity, empathy, and a commitment to inclusion, we can design teams that thrive in this era of AI-human collaboration. Let’s just make sure the new bottle doesn’t come with any old hangovers.

So, here’s to the future of work! Let’s raise a glass (or a robot arm) to whatever’s next!

References:

• Image generated by DALL-E, OpenAI (2024)
• Hackman, R. J. (2002). Leading teams: Setting the stage for great performances. Harvard Business School Press.
• Wageman, R., Nunes, D. A., Burruss, J. A., & Hackman, R. J. (2008). Senior leadership teams: What it takes to make them great. Harvard Business School Press.
• Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of Machine Learning Research.
• Coqual / Center for Talent Innovation. (2021). The Power of Belonging: What It Is and Why It Matters in the Workplace. Coqual.