What question did this study set out to answer?

This research aims to create a framework that helps organizations effectively measure and communicate AI capacity to decision-makers.

June 1, 2026Open Access

Desk Power and Concurrent Human-Speed Workflows: A Dual-Metric Framework for AI Infrastructure Decision-Making

Key Points

This research aims to create a framework that helps organizations effectively measure and communicate AI capacity to decision-makers.
Developed a dual-metric framework consisting of Desk Power (DP) and Concurrent Human-Speed Workflows (CHW).
Incorporated factors like token generation rate, system availability, and response-speed thresholds into the metrics.
Provided worked examples covering various AI systems from consumer-GPU to multi-GPU clusters.
Demonstrated that DP quantifies AI server capacity in terms of equivalent human office workers.
Showcased that CHW measures simultaneous user capacity under specified responsiveness thresholds.
Created a vendor-neutral language for effectively communicating AI infrastructure capabilities.

Abstract

Abstract: Organizations deploying AI inference systems for knowledge-work automation currently lack a shared, intuitive vocabulary for capacity planning and procurement. Existing metrics — tokens per second, FLOPS, benchmark scores — carry precise technical meaning but fail to communicate operational impact to the managers and executives who make infrastructure investment decisions. This paper introduces a dual-metric framework that addresses this gap. The first metric, Desk Power (DP), quantifies the economic replacement capacity of an AI server in terms of equivalent human office workers, drawing explicit analogy to James Watt's mechanical horsepower. The second metric, Concurrent Human-Speed Workflows (CHW), quantifies the number of simultaneous users or workflow streams a given system can serve at or above a specified responsiveness threshold. Together, DP and CHW answer the two questions that drive real procurement decisions: how many employees can this system replace (DP), and how many employees can it serve at once (CHW). A formal mathematical framework is developed for both metrics, incorporating token generation rate, system availability, parallelism, quality correction, and response-speed thresholds. Worked examples are provided for systems ranging from local consumer-GPU edge nodes to enterprise multi-GPU clusters. The proposed framework offers a vendor-neutral, audience-appropriate language for AI capacity planning, workforce transition analysis, and regulatory impact assessment. Keywords: AI capacity measurement, large language models, office automation, workforce displacement, inference throughput, concurrent users, server sizing, decision-making framework. Title: Desk Power and Concurrent Human-Speed Workflows: A Dual-Metric Framework for AI Infrastructure Decision-Making Author: Eng. Nawaf F. Al-Mutairi International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 14, Issue 2, April 2026 - June 2026 Page No: 10-16 Research Publish Journals Website: www.researchpublish.com Published Date: 27-May-2026 DOI: https://doi.org/10.5281/zenodo.20408317 Paper Download Link (Source) https://www.researchpublish.com/papers/desk-power-and-concurrent-human-speed-workflows-a-dual-metric-framework-for-ai-infrastructure-decision-making

Desk Power and Concurrent Human-Speed Workflows: A Dual-Metric Framework for AI Infrastructure Decision-Making

Key Points

Abstract

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