AI-mediated teamwork

AI-mediated teamwork tracks how AI changes the reasons teams are formed and the benefits they are expected to provide: performance pooling, expertise integration, coordination, social connection, and motivation. The point is not to call AI a teammate by default. It is to ask when AI reproduces, weakens, or reorganizes functions that were previously supplied by human teammates.

2026-dellacqua-cybernetic-teammate gives the wiki its first direct field-experimental evidence on this question. In a preregistered P&G product-innovation experiment, teams without AI outperformed solo workers by about 0.24 standard deviations, but solo workers with AI improved by about 0.37 standard deviations and statistically matched non-AI teams. That makes AI-mediated teamwork a redesign question: in bounded ideation work, AI may supply enough perspective and language support that one worker can reproduce some benefits of a cross-functional pair.

The source also separates teamwork’s expertise-integration function from its headcount structure. Without AI, R&D participants produced more technical ideas and Commercial participants produced more market-oriented ideas; with AI, both groups produced more balanced solutions. AI did not merely make the same worker faster. It helped workers reason across functional boundaries that human teams normally bridge.

The published analysis further localizes that contribution inside innovation work. 2026-dellacqua-cybernetic-teammate finds that AI mainly improves the quality of ideas generated rather than selection accuracy, so the useful redesign is not “replace the team with a solo worker and AI.” It is to distinguish generative breadth and quality, where AI may supply part of a team’s contribution, from evaluative selection, where human judgment remains a separate work function.

2025-dellacqua-super-mario-ai gives the counterweight: AI can weaken teamwork when it enters an interdependent coordination routine as a replacement coworker rather than as an ideation aid. In a laboratory experiment where four-person firms played a team-based game, high-performing automated agents individually outperformed humans but reduced firm performance, increased coordination failures, and lowered trust and effort among directly affected human teammates. This makes team performance a system property, not a sum of member capability.

The result sharpens the topic boundary. AI-mediated teamwork is not only whether AI can supply expertise or perspective; it is whether the team can preserve coordination routines, mutual predictability, motivation, and trust when one human role is replaced by an automated actor. The skill heterogeneity matters: low- and medium-skilled remaining teams struggled to integrate AI, while high-skilled teams did not show the same decline, suggesting that human team capability conditions whether automation becomes a complement or a disruption.

2026-buechsenschuss-genai-organizational-networks extends the teamwork question outward from bounded teams to the organizational network. In its field experiment, a grounded GenAI assistant increased collaboration and knowledge-sharing centrality across employees, suggesting that AI can change not only whether a team is needed for a task but also who becomes connected to whom as work and knowledge move through the firm.

The social result is more tentative but important for I-O framing. AI-supported participants reported more positive and fewer negative emotions after the task, including higher enthusiasm, energy, and excitement and lower anxiety, frustration, and distress. That suggests AI can affect the motivational and affective texture of teamwork, but the evidence is short-run self-report during an experiment rather than durable relatedness, trust, or team development.

2025-klonek-ai-work-stress gives the page a work-design stress mechanism for human–AI team processes. In ChatGPT-related teamwork tweets, transition and action processes were linked to lower stress, and action plus interpersonal processes weakened the positive association between AI control and stress. That makes AI-mediated teamwork more than a metaphor for performance: planning, goal-progress, and socioemotional processes may operate as resources that protect workers when AI has more control.

2026-shi-when-bots-join-the-team shifts the coordination question from replacing a teammate to adding a visible institutional participant to the team’s shared interaction record. In 2,991 open-source projects around first adoption of rule-based bots, adoption coincided with more repeated human engagement and bot-directed social memory, fewer conflict cascades, and more distinctive project outputs; the evidence is consistent with bots reinforcing coordination functions that organize human collaboration rather than simply displacing it.

The mechanism evidence is suggestive rather than causal. In joint models, repeated engagement, bot-to-human addressing, and role differentiation were associated with fewer conflict cascades, while role differentiation, bot-directed social memory, and inter-bot interaction were associated with greater output distinctiveness; because the event study has no untreated comparison group and bot adoption may be endogenous, 2026-shi-when-bots-join-the-team does not establish that bots caused these changes or mediated them.

Connections

• Connects to human-ai-collaboration because the teammate metaphor can be empirically useful for performance, expertise, and affective functions, while accountability and stewardship still sit with humans.
• Connects to work-redesign because AI can change whether organizations need a team, a solo worker with AI, an automated replacement inside a team, or a different coordination structure for a given problem.
• Connects to automation-and-substitution because substitution can occur at the team-function level before it becomes job-level labor replacement.
• Connects to ai-mediated-work-experience because teamwork is partly a motivational and social experience, and human–AI team processes can change stress, control, and social-resource perceptions rather than only team output.
• Connects to human-ai-task-taxonomy because teamwork claims need task-level detail about output definition, authority, auditability, expertise mix, and whether the work requires coordination beyond idea generation.
• Connects to ai-mediated-organizational-networks because rule-based bots can reinforce coordination within a project’s shared interaction record, while GenAI can change bounded team functions and broader cross-team knowledge-flow patterns at the same time 2026-shi-when-bots-join-the-team.

Contradictions & open questions

• 2026-dellacqua-cybernetic-teammate supports AI as a partial substitute for some teamwork benefits in bounded product-innovation tasks, while 2025-dellacqua-super-mario-ai shows that replacing a human teammate with a high-performing automated agent can reduce performance when the task depends on coordination routines, trust, and effort. The contradiction is productive: AI may substitute for some expertise-integration functions while disrupting interdependent teamwork functions.
• 2026-shi-when-bots-join-the-team instead associates the adoption of rule-based bots with fewer conflict cascades and more repeated human engagement in open-source projects, contrasting with the performance and trust disruption observed when an autonomous agent replaces a human teammate in 2025-dellacqua-super-mario-ai. Whether this reflects bot role, task interdependence, team skill, or the difference between archival associations and experimental replacement remains open.
• 2026-buechsenschuss-genai-organizational-networks points in the opposite direction from simple team substitution: grounded GenAI increased human-human network ties rather than reducing them, though the evidence is self-reported and short-run.
• The same source finds little evidence that teams with AI outperformed individuals with AI on solution quality, which raises a sharp redesign question: when does AI make teams more valuable, and when does it make team formation redundant for the immediate task?
• The positive emotion finding should not be overread as relatedness. Enthusiasm after AI use may come from novelty, perceived fluency, reduced effort, or task success rather than genuine social connection or sustained team climate.
• 2025-klonek-ai-work-stress treats team processes as text-coded features of public social-media posts, so it should not be read as evidence that durable human–AI teams reduce stress in organizations; it is better evidence that team-process constructs belong in the nomological network for AI work stress.