Journal of Advances in Developmental Research

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The Delegation Decision: A Product Manager's Framework for Deciding What to Hand to AI Agents

Author(s) Aditi Kapildev Vatse
Country United States
Abstract As AI Agents become capable of executing multi-step operational workflows autonomously, product managers face a structurally new decision: which tasks to delegate to agents and which to preserve for human judgment. Existing literature addresses how to design human-AI collaboration once the automation boundary is set [1,5] and how to measure return on investment after deployment, but no practitioner framework exists to guide the prior question of where to set that boundary in the first place.
This paper proposes ADAPT, a five-criterion evaluation model that product managers can apply during discovery and scoping to assess whether a workflow task is appropriate for AI Agent delegation. The five criteria are: Ambiguity Tolerance [1], Decision Reversibility [8], Audit and Compliance Exposure [10,11], Process Stability [3], and Trust Threshold [2]. Each criterion is grounded in established peer-reviewed research. The framework is demonstrated through three anonymized worked examples from a Fortune 500 operational technology enterprise and is shown to prevent both over-delegation risk (compounding autonomous errors) and under-delegation risk (unrealized automation value).
Keywords AI Agents, Agentic AI, Product Management, Delegation Decision, ADAPT Framework, Task Allocation, Human-in-the-Loop, Automation Boundary, AI Governance, Workflow Automation
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-05-10
DOI https://doi.org/10.71097/IJAIDR.v17.i1.2035
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Crossref DOI prefix of IJAIDR is 10.71097/IJAIDR

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