AI proactivity

AI proactivity is the timing and scope of system initiative: whether the system waits for a request, offers recommendations, redirects attention, takes preparatory action, or acts without a fresh human prompt. It is not equivalent to AI competence or agentic delegation. A highly competent system can remain reactive; a proactive system can merely surface a suggestion or can act autonomously. The work-design question is whether its initiative arrives at a point that preserves human control, attention, ownership, and social standing.

2026-lepine-precision-proactivity gives the clearest interaction-level warning. In GPT-4o-assisted financial valuation work, model-initiated task switching was the strongest proactive behavior associated with performance decline, followed by information sprawl and truncation. AI-generated content could still support quality, so the finding is not that initiative is categorically harmful; it is that initiative which fractures the worker’s task structure can create extraneous load that outweighs the content benefit.

2026-ghosh-new-social-image identifies a different, social-experiential pathway. In a 2 × 2 × 2 video-vignette experiment, lower AI proactivity and competence generally produced more favorable perceived ownership, meaningfulness, satisfaction, role dynamics, and affect. The result makes proactivity a role-design variable rather than only an interface feature: an AI that acts too independently can change how workers see their own contribution and how they think peers see it.

2026-zhu-choose-your-agent separates initiative from execution authority in a bargaining context. Participants preferred a proactive Advisor that preserved human control, whereas the autonomous Delegate produced stronger group-welfare gains. The contrast is useful precisely because neither proactivity nor control is a simple universal good: the right arrangement depends on the task’s interdependence, the value of timely action, the human’s remaining judgment work, and the social consequences of the handoff.

Connections

• Relates to human-ai-collaboration because initiative changes when humans must interpret, verify, interrupt, or recover an AI contribution.
• Relates to cognitive-load-in-ai-assisted-work because ill-timed system initiative can create task switching and information sprawl rather than reduce work.
• Relates to agentic-delegation because proactive recommendation and autonomous execution are distinct authority arrangements that should not be conflated.
• Relates to ai-use-image-concerns because AI initiative and competence can shape workers’ anticipated ownership, standing, and evaluative image, not only task performance.
• Relates to ai-mediated-work-experience because the timing of AI action can alter autonomy, competence, meaningfulness, affect, and the social texture of a role.

Contradictions & open questions

• The evidence does not support a monotonic rule that less proactivity is better. 2026-ghosh-new-social-image measures perceptions in vignettes, 2026-lepine-precision-proactivity observes one high-pressure financial task, and 2026-zhu-choose-your-agent studies strategic bargaining; each shows a different outcome and authority configuration.
• The field needs measures that distinguish proactive suggestion, interruption, task switching, background preparation, and autonomous execution rather than treating them as one proactivity score.
• It remains unclear when workers should be able to set, negotiate, or revise proactivity thresholds, and whether those controls protect agency and attention without shifting too much configuration burden onto workers.