RESEARCH TOPIC / ORGANIZATION-ADOPTION
Adoption, learning, knowledge, and organizational capability.
GROUNDING MAP
Explore 15 research notes through a shared decision domain. Each article identifies its conclusion, sources, findings, and limitations.
After AI-enabled RPA was introduced, interviews suggested monthly research time fell about 90%. Later, only 10–20% of estimated freed time was explained as new work or extra output. This note shows how to measure the time actually freed and where it went.
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After work has been redesigned, decide how remaining steps combine core systems, APIs, RPA, AI, and people—and what proves the operation can run without its builder.
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Treat faster local tasks as only one signal; redesign bottlenecks, exceptions, expert handoffs, and the allocation of released capacity across the full workflow.
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Do not mistake general research on psychological safety for AI-specific causal evidence; integrate dissent, error reporting, stop proposals, and retaliation checks into the execution procedures for AI implementation.
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We distinguish between load reduction via external memory and the reduction of internal encoding and retrieval, measuring both the performance when AI is usable and the independent execution when it is not.
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From experiments showing that evaluations change when a work is labeled as AI-generated, we separate blind content evaluation from eligibility reviews of provenance, rights, and production process.
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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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We design an operation that distinguishes the quality of AI-generated empathetic text from the recipient's perceived understanding, connection, and source preference, while safeguarding human editorial responsibility and recipient choice.
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We do not end the discussion with the notion that “metrics become targets and the frontline deteriorates”; instead, we distinguish bias toward easily measurable work, gaming, and actual improvement to design robust operations that resist breakdown.
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RAG accuracy is determined not only by the model but also by how quickly document version, expiration, source, permissions, and deletion are reflected in the index; we consider operational design from knowledge benchmarks.
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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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Even if AI assistance improves individual proposals, if the organization's overall proposals become similar, the exploration range narrows. Based on creativity research, we consider methods to manage quality and collective diversity separately.
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Solving a problem with generative AI is not the same as solving it independently. Drawing on meta-analyses and educational experiments, we examine learning designs that separate supported performance, independent reproduction, and long-term retention.
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A meta-analysis of 106 experiments and management studies helps distinguish extension—outperforming humans alone—from true synergy, where collaboration exceeds the best human or AI performance, and identifies when collaboration is viable.
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Distributing tools alone does not build organizational capability. Drawing on field research, this article outlines how to redesign work, learning, expertise, and control.
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