AI teamwork – AI may shrink teams and change collaboration, but teamwork is likely to evolve toward judgment, trust, and leadership.
AI is reshaping collaboration so quickly that the real question is no longer whether teamwork will change, but how fast.. In this context. the rise of generative AI is prompting many organizations to rethink how work gets done. and whether the “superpowered individual” can replace the traditional team model.
The argument for a simpler future is easy to grasp: when one person can create campaign assets. analyze data. prototype product ideas. or draft software code. the old logic of division of labor starts to look less necessary.. Misryoum notes that experimentation is already underway in places that use AI agents to handle or stress-test functions across strategy. operations. and development.. The direction is clear: some workflows that once required multiple roles can now be compressed into fewer hands.
Insight: Even when AI can produce outputs at scale, the hardest part of most projects is deciding what to do next. That is where human alignment tends to matter.
But Misryoum believes teamwork is unlikely to disappear.. Instead, it will shift in how teams are built, what they focus on, and what leaders are accountable for.. One likely change is team composition: smaller groups may be able to move faster. while both human and nonhuman contributors take on more of the day-to-day.. This raises a practical challenge for organizations. because “AI literacy” may need to become a shared. team-level capability rather than an individual perk.
Alongside that. effective collaboration will require common norms about when AI should be trusted. when it should not. and how to balance speed with quality.. Teams will also have to learn how to interrogate AI outputs and incorporate human judgment. including rewarding people for catching errors rather than merely for using tools efficiently.. In other words, the ability to question AI may become part of performance expectations.
Insight: Organizations that only reward throughput may end up paying for mistakes twice. Building incentives around verification and judgment helps prevent that.
The second shift is what teamwork is actually for.. Misryoum highlights that many teams spend too much time on coordination-heavy tasks such as reporting and internal alignment.. AI can often automate or accelerate those logistics, which suggests teamwork may need to move up the value chain.. In this view. collaboration becomes less about “status updates” and more about relational work: building trust. supporting constructive disagreement. and creating psychological safety alongside intellectual friction.
Finally, leadership itself will likely change.. Rather than acting primarily as a source of answers, leaders may need to orchestrate how people and AI systems interact.. That includes setting decision rights, clarifying accountability, and making sure leaders do not retreat from judgment when stakes are high.. Misryoum also points to a likely recalibration of measurement: if performance is assessed only by visible activity. organizations may reward the wrong behaviors in an AI-enabled workflow.
Insight: The risk is not that AI destroys teamwork, but that it reveals which parts of “collaboration” were mainly administrative. The opportunity is to redesign teams around thinking critically, deciding wisely, and connecting meaningfully so the whole becomes more than the sum of parts.