Artificial intelligence has revolutionized the way we work. AI-powered tools are helping organizations boost efficiency, automate routine tasks, and produce data-driven insights with unprecedented speed.
And the revolution is just beginning, with companies like Meta spending incredible amounts of money trying to create an AI-specific competitive edge.
But despite this investment and these gains in productivity, relying on AI for many tasks risks eroding the human interactions that underpin innovation, creativity, and team cohesion in the business realm.
The Downside of AI
When AI takes on tasks once reserved for humans, it reshapes how we collaborate. Instead of huddling around whiteboards for spirited brainstorming sessions, teams increasingly rely on tools like ChatGPT to propose ideas, refine messaging, or design prototypes. What used to be dynamic interaction slowly becomes a solitary interaction with an algorithm. The AI “co-creator” can be efficient, but it often lacks the spontaneity and serendipity of human exchange.
This shift impacts organizational health and human flourishing.
Spontaneous conversations in hallways or “watercooler moments” can fuel creative breakthroughs and social connection that are already rare among distributed teams. Layer on an AI assistant that summarizes meeting minutes, suggests strategic pivots, and schedules upcoming meetings, and the incentive (and opportunity) for genuine peer-to-peer dialogue virtually disappears. When colleagues default to prompts and outputs, they lose opportunities for personal conversations to clarify assumptions, explore divergent perspectives, or challenge each other’s thinking in real time.
Successful Adoption Strategies
The metrics we rely on to assess performance may amplify this effect. If a dashboard reports that response times have improved or error rates have declined, it’s easy to overlook subtler declines in team morale or trust.
After all, productivity metrics do not capture the bonding that occurs when one colleague builds on another’s half-formed idea. Nor do they capture the gains achieved by wrestling through ambiguity together to achieve clarity. Over time, organizations risk becoming efficient but brittle — able to execute known tasks quickly, yet less prepared to navigate unforeseen challenges that demand collective creativity.
Balancing the productivity gains of AI against the potential loss of human and organizational capabilities requires intentionally designing workflows and corporate cultures for successful integration. Leaders should ask: Which activities benefit from AI’s speed, and which are best left to human collaboration?
One practical step is establishing “collaboration windows” — scheduled times when AI tools are set aside and teams focus on face-to-face interaction. These opportunities are more challenging to find as more employees become either mostly or entirely remote workers. But protecting space for authentic human interaction and collaboration will continue driving deep productivity.
Train Them Up in the Way They Should Go
Training also matters.
Organizations that upskill employees to use AI thoughtfully — using it as a collaborator rather than a crutch — can preserve critical thinking and interpersonal skills. Encouraging team members to annotate AI suggestions with their own rationale or counterarguments, and to share their successes and failures with various prompts can foster a collaborative view of AI’s contributions. This approach keeps humans firmly in the driver’s seat, leveraging AI as a powerful support rather than a substitute for human dialogue.
It’s not just employees who need to be trained. Organizations must adjust as well by starting to measure the right outcomes.
Beyond traditional productivity KPIs, companies should track indicators of collaborative health: frequency of cross-functional meetings, sentiment in peer feedback, and narratives of the “aha” moments documented in project retrospectives. These qualitative measures spotlight the human dimensions that raw productivity analyses overlook.
AI is not the enemy of collaboration — far from it. In fact, AI tools can improve collaboration by improving written communication, coaching employees struggling with interpersonal issues with colleagues, or even providing insights about the subtle social cues employees who often feel a need to finish more tasks in less time may be missing. For example, a colleague recently texted me in response to an email I’d sent: “According to Google Gemini, your email about the project was rated ‘somewhat snarky.’ Is something about the project bothering you?” In reflection, I realized I had composed the email in a rush and was unaware of the subtext that came through. In this case, AI helped me think through my reaction to the original request more clearly, and to communicate more candidly about its costs.
This kind of interaction between human emotions and the subtle cues embedded in our communication, combined with the analytic power of AI to analyze and synthesize information can deliver stronger performance, increased productivity, and better organizational culture.
By intentionally integrating AI into workflows, safeguarding dedicated spaces for human exchange, and monitoring collaboration health alongside efficiency metrics, leaders can harness AI’s power without sacrificing the human spark. In doing so, they ensure that teams remain not only productive but innovative, resilient, and deeply connected.
Michael Williams is the associate dean of Academic Affairs and an associate professor of Information Systems Technology Management at Pepperdine University’s Graziadio Business School.
