Team collaboration - does AI help or hinder?
By Nathan Waterhouse · 2024-12-23
I recently faced a daunting challenge: collaborating with an expert partner from one of the big four consultancies on a high-stakes project. Their reputation preceded them, and I felt the pressure to prove myself. To make matters worse, their deep domain knowledge left me feeling completely out of my depth. Although it was clear their role was to be that expert voice, it felt like I was in the kiddy chair whilst the grown-ups spoke. If I couldn’t speak the same language, how could I direct the project and ensure its success?
Desperate to bridge the knowledge gap, I turned to ChatGPT. I learned as much as I could, querying unfamiliar terms when I encountered them, and looked for synergies between our different worlds, determined to speak their language. Using ChatGPT to decode industry jargon and find synergies was a pivotal first step in leveling the playing field, enabling me to speak their language and contribute meaningfully.
However, the real turning point came when we moved beyond the spreadsheets and presentations. Over a casual coffee break, we discovered a shared passion for classic films. Suddenly, the intimidating expert was just another film buff, eager to debate the merits of Inception. That conversation sparked a connection that transformed our working relationship.
Reflecting on this experience, it became clear how critical it is to balance the efficiency and knowledge provided by AI with the interpersonal connections that build trust and foster collaboration. While AI equipped me with the tools to prepare intellectually, it was the human connection forged over shared interests that solidified trust and made collaboration thrive.
This experience, and many I've had over the last 18 months got me thinking about collaboration. As AI tools become ever more integrated into our daily workflows, we need to ask: is this new digital teammate enhancing our sense of connection—or quietly eroding it? In this post I explore the role of AI in collaboration, and suggest some ways that leaders can start to improve, rather than weaken, collaboration when working with AI in their teams.
A Shifting Landscape of Collaboration
The rise of AI tools has made it easier than ever to get quick answers without “bothering” a colleague. From analytics engines that instantly reply with data insights to 24/7 chatbots that never sleep, the temptation to rely less on human expertise is real. On one hand, this can free us from mundane tasks, speed up decision-making, and save our peers’ time. On the other, it might mean we share fewer “lightbulb moments” and ultimately weaken the social bonds that hold teams together.
A Deloitte study found that 73% of executives believe effective collaboration is essential for organisational success. For leaders, this poses a strategic question: how can AI be implemented to enhance productivity without compromising the collaboration and trust that underpin high-performing teams?
Where Are We Finding Satisfaction Now?
McKinsey estimates that generative AI could add between $2.6 trillion to $4.4 trillion annually to the global economy by enhancing productivity across various sectors. But what about human satisfaction? Are we losing the subtle joy of picking up the phone—or firing off a Slack message—to say, “Hey, can you help me think this through?” Some worry that swapping colleague-to-colleague interactions for queries to an algorithmic oracle may diminish the sense of belonging and trust within teams. After all, it’s not just about saving time; it’s about maintaining the relationships that enrich our working lives.
For HR leaders, this raises an important issue: How can organisations ensure AI tools support, rather than erode, the sense of belonging and engagement that employees derive from collaboration?
Morten Hansen’s Barriers to Collaboration
It feels like a framework might help us think through collaboration and how AI may affect it. Morten Hansen , author of 'Collaboration', identifies four key barriers to collaboration that organisations must address to foster effective teamwork. Let's explore about how AI can play a role:
Not Invented Here (NIH): This barrier refers to the reluctance to accept external ideas. One of the things AI is really good at is helping people learn from all the collective intelligence that's out there in books and the internet. AI tools can provide objective data and insights that help teams evaluate external contributions based on merit rather than bias, breaking down NIH resistance (of course there is AI bias, so we need to be mindful when selecting and filtering sources from AI).
Hoarding: Often, individuals or teams withhold resources or information due to competitive dynamics. One way in which we are seeing this today is the way that employees are hiding their AI usage, perhaps to get one up on a competing business group. But AI-powered knowledge management systems could ensure transparent access to critical information, encouraging sharing and reducing hoarding, as long as they were implemented fairly.
Search Inefficiencies: In large organisations, knowing who to turn to for help can be challenging. AI can act as a matchmaker, identifying and connecting individuals with complementary skills or expertise across departments, saving time and boosting efficiency. When I used to run OpenIDEO.com, we had community managers who played this role, but as the community grew to the 100,00s, we started enabling members to add their skills. A similar system to this, with a chatbot suggesting which colleagues could help at tricky moments could be a novel way to foster new connections. By scanning skills, project histories, and interests, AI might suggest connections—linking a marketing specialist in London with an R&D engineer in Singapore who share a passion for sustainable packaging.
Teamwork Challenges: Hansen's fourth barrier is called the 'Transfer' barrier. We often only work with those we know well and it can be hard to relate to those we don't work with. Could AI act as a mediator by analysing team interactions, offering suggestions to enhance communication, and ensuring all voices are heard? AI is already helping translate between languages in platforms like Slack and others. Even within the same language, differing mental models can mean knowledge doesn't transfer well. AI could help by translating here too.
By leveraging AI to address these barriers, leaders can create a more collaborative and innovative environment while maintaining focus on strategic objectives.
Real-World Examples: Breaking Down Barriers or Building Walls?
Consider global teams that embrace AI-powered translation tools. For a European engineering firm, instant multilingual transcription meant colleagues from different continents could collaborate seamlessly on intricate technical challenges—no one was sidelined due to language barriers. This aligns with Morten Hansen’s idea of overcoming "Search Inefficiencies," where AI acts as a matchmaker by connecting expertise across locations and time zones.
A similar success story emerges from Beyond Better Foods, a snack company featured in Slack’s customer stories. By integrating digital collaboration platforms, their teams not only operated more efficiently but maintained a consistent, human-centric dialogue across time zones. Here, AI fostered inclusivity rather than isolation, reflecting Hansen’s emphasis on reducing "Teamwork Challenges" by enhancing communication in cross-functional teams.
Leadership and Culture
Yet the picture isn’t always rosy. Research from Columbia Business School ran a study called “Super Mario Meets AI” which showed that teams interacting with AI can experience reduced coordination if the technology isn’t integrated thoughtfully. And in another truss structure design experiment, a single low-performing AI ‘teammate’ ended up dragging down the entire group’s decision quality. For more details, you can read the study here: Are confident designers good teammates to artificial intelligence?. These studies underscore that AI’s impact depends heavily on things like good leadership, governance, clear policies and frameworks.
However, leaders must ensure that reliance on AI does not undervalue the unique insights and empathy that human colleagues bring. AI should complement—not overshadow—the human dimension of collaboration, preserving the diverse thinking essential to innovation.
Building a Positive AI-Enhanced Collaborative Culture
Leaders have a critical role in shaping a beneficial coexistence with AI. Consider these steps.
Promote AI Literacy: Educate employees—especially senior leaders—on AI’s capabilities and limitations to ensure thoughtful application rather than over-reliance.
Create Transparent AI Policies: Build trust by clearly communicating how AI tools work, what data they collect, and how decisions are made. Transparency aligns with reducing hoarding and building a collaborative culture.
Test and Learn: What works for one business may not suit your unique culture. Run experiments in parts of your organisation, perhaps with specific teams, and measure the outcomes on collaboration.
Encourage Cross-Functional Teams: Facilitate collaboration between AI specialists and domain experts to ensure AI initiatives align with strategic objectives. This approach overcomes search inefficiencies and maintains focus on meaningful, value-adding projects.
Use AI as a “Matchmaker”: Explore how AI could identify high-potential partnerships across departments or regions, ensuring each collaboration is purposeful and impactful.
Seek regular feedback: Regular feedback loops between employees and leaders can ensure that AI implementation aligns with team goals and resolves unforeseen issues.
Collaboration in the age of AI, Balancing Benefits and Risks
As we've seen, the potential upsides are clear: increased productivity, faster decision-making, better global connections, and improved diversity of perspectives. Yet, if poorly implemented, AI could make things worse, not better. For example, AI could exacerbate “Hoarding” if employees fear job displacement and choose to withhold knowledge or misuse AI tools. Similarly, “Search Inefficiencies” may worsen if AI systems are implemented without proper training, leading to reliance on incomplete or biased recommendations. For C-level executives, the challenge lies in balancing these benefits and risks while ensuring AI remains a tool for empowerment, not dependency.
As a leader, how do you see AI shaping the future of collaboration in your organisation? Are you finding opportunities to integrate AI into your team’s workflows in a way that strengthens trust and innovation? Have you encountered challenges in balancing efficiency with human connection?
As my story at the start of the post shows, we can shape a future where AI tools reinforce (rather than replace) the human bonds that make work both productive and fulfilling. And remember Morten Hansen’s collaboration framework: the key is not to collaborate more, but to collaborate better—with a sense of purpose, discipline, and understanding of where AI adds genuine value.