Human?AI Capital Allocation for Large-Scale Robotic Systems: A Framework for Efficient, Accountable, and Adaptive Investment Decisions
Abstract
Large-scale robotic systems require sustained capital investment across hardware, software, data infrastructure, maintenance, and skilled human labor. Once deployed, many of these investments are difficult to reverse, making capital allocation a central governance decision rather than a routine financial task. Organizations increasingly rely on artificial intelligence to support capital planning through forecasting, scenario analysis, and option comparison. While such tools improve analytical consistency, they also introduce risks related to model bias, misaligned objectives, and weakened accountability. Human-led capital allocation, in contrast, preserves responsibility but struggles with scale, consistency, and long-term risk recognition. This paper examines how humans and AI systems should jointly allocate capital in large-scale robotic systems. Drawing on literature from robotics deployment, decision science, AI-assisted investment, and governance, the study adopts a conceptual synthesis approach to analyze how decision authority, analytical support, and responsibility interact across the capital allocation lifecycle. The paper contributes a structured human?AI capital allocation process that explicitly assigns authority boundaries, escalation points, and accountability mechanisms across planning, approval, monitoring, and reallocation stages. The analysis shows that neither human-only nor AI-only approaches adequately address the combined demands of scale, uncertainty, and safety in robotic systems. Joint human?AI arrangements perform best when analytical support is constrained and human authority is clearly defined. By reframing capital allocation as a governance and authority design problem rather than a purely analytical task, the paper offers practical guidance for organizations deploying robotic systems at scale and contributes to ongoing discussions on responsible automation.
Citations
IRE Journals:
Uju Eziokwu, Adetunji Oludele Adebayo, Nathaniel Adeniyi Akande, Udoka Cynthia Duruemeruo, Sopuluchukwu Feargod Ani "Human?AI Capital Allocation for Large-Scale Robotic Systems: A Framework for Efficient, Accountable, and Adaptive Investment Decisions" Iconic Research And Engineering Journals Volume 7 Issue 9 2024 Page 609-621 https://doi.org/10.64388/IREV7I9-1713393
IEEE:
Uju Eziokwu, Adetunji Oludele Adebayo, Nathaniel Adeniyi Akande, Udoka Cynthia Duruemeruo, Sopuluchukwu Feargod Ani
"Human?AI Capital Allocation for Large-Scale Robotic Systems: A Framework for Efficient, Accountable, and Adaptive Investment Decisions" Iconic Research And Engineering Journals, 7(9) https://doi.org/10.64388/IREV7I9-1713393
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