AI use-case selection depends on reallocating validation capacity, not a fixed ranking
Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.
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Economics & Sustainability
Cost-effectiveness, labor, procurement, and computing externalities.
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Explore 9 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
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09 ARTICLES
A Pilot Comparison Design for Moving Generative AI into Production
Compare the existing and AI-assisted workflows under aligned conditions, including human review and revision time, confirmed quality, and unfinished cases. Use a record to decide deployment, hold, retest, or stop.
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Does algorithmic management deprive workers of discretion?
We organize work design by separating allocation, monitoring, evaluation, and reward by function, and setting minimum standards for autonomy, connectivity pressure, anxiety, and the ability to appeal, in addition to efficiency.
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Using the strongest model for every request is not always the best investment
Designing a system that sends all requests to a high-performance model, or one that fixes them to a low-cost model, is not necessarily optimal; drawing from cascade and router research, we consider routing under quality constraints.
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The power and water footprint of generative AI cannot be reduced to a per-question figure
Define measurement boundaries for power and water that include usage, processing volume, model, region, time, and equipment, rather than relying on the impression value of a single prompt, and reflect these in procurement and operations.
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AI vendor selection must test the ability to resume operations after exit
We organize vendor evaluations that test data, model dependency, contracts, portability, and stoppage/alternatives before implementation, rather than selecting based solely on accuracy demos.
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Will generative AI change employment, or will it restructure work first?
We analyze where changes emerge by dividing the impact of generative AI into task time, wages and working hours of existing employees, freelance demand, and youth hiring.
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When does AI coding accelerate development, and when does it slow it down?
Average effects of AI coding are not enough for investment decisions. Research from laboratories, enterprises, and mature open-source projects reveals the work, personnel, and measurement conditions that determine whether development speeds up.
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The ROI of Generative AI Is Determined by Pre-Implementation Design
Do not equate productivity gains demonstrated by research directly with business value. We propose designing ROI by linking time, quality, accuracy, and usage costs at the business process level.
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