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Enterprise AI Analysis: Losing Our Voice? Generative AI and the Degradation of Human Expression

AI Research Analysis

Losing Our Voice? Generative AI and the Degradation of Human Expression

This paper examines the implications of generative AI (GenAI) emulating human expression, i.e. human communication and human creative expression. While GenAI seems to offer benefits such as increased efficiency and productivity, its use raises significant practical and conceptual concerns: GenAI comes with the increased efforts of prompting, verification and editing, and causes the deskilling of its users. It also comes at a monetary cost and causes various ethical issues e.g., a lack of authenticity. We further show that GenAI's fundamental issue is that it is by design not able to output human expression but only human-like expression. Using AI for tasks that are fundamentally about communicating is replacing communication with something that is not communication. Finally, we show that the consequences of the use of GenAI cannot be avoided on an individual level by those individuals avoiding the use of GenAI and will necessarily lead to an erosion of human expression in general. This is because GenAI: will lead to a distrust in human expression when the authenticity of authorship over time becomes unclear; will cause a devaluation of human expression when human expression can be mimicked with less effort by GenAI; and, will discourage human expression altogether when GenAI has set the bar too high.

Authors: Scott Robbins, Inga Blundell

Publication: Minds and Machines (2026) 36:2

Key Takeaways for Enterprise Leaders

Generative AI is transforming how we work and communicate. While it promises efficiency, leaders must navigate its inherent limitations and profound societal impacts to harness its power responsibly without degrading authentic human engagement.

0% Americans Using GenAI
0% Daily GenAI Use in US Work
0 Projected Economic Value Add
0% Task Time Reduction Potential
0% Companies Planning Workforce Reduction due to AI

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

The Core Problem: Inauthenticity & Ethics
Practical Pitfalls & Societal Erosion

Generative AI outputs, while human-like, fundamentally lack the genuine desires, emotions, and intentions that define authentic human expression. This conceptual distinction is critical for enterprises seeking to deploy AI ethically.

Aspect Human Expression GenAI Emulation
Origin of Output Rooted in genuine emotions, desires, opinions, and intent. Mimics patterns from training data; lacks intrinsic desires or emotions.
Effort & Commitment Demonstrates care, builds trust, and signifies personal investment. Reduces perceived effort for user; can lead to ethical concerns regarding disclosure and authenticity.
Control & Authorship Humans are meaningfully in control of their original output and accountable for it. Outputs are generated, not truly authored; human oversight for verification and editing is critical.
Purpose & Value To communicate genuine feeling, intent, or perspective; valued for its unique human origin. To produce human-like content efficiently; risks being a "category mistake" when called true art or communication.

The Cost of Unverified AI Output: Legal Ramifications

In a striking example highlighting the need for rigorous verification, two lawyers at the law firm Morgan and Morgan made headlines when a judge found that their lawsuit against Walmart contained fictitious case citations. The lawyers admitted these errors were caused by their reliance on an LLM, underscoring the critical importance of human oversight and factual validation even when using AI for seemingly straightforward tasks. This incident demonstrates that 'time-saving' AI tools can introduce new forms of effort and risk, particularly when used in contexts demanding high accuracy and accountability. Enterprises must implement robust verification protocols to mitigate significant reputation and legal risks.

Reference: Merken, 2025

Beyond the conceptual, GenAI introduces practical challenges like increased user effort and deskilling, alongside broader societal concerns such as distrust, devaluation, and the potential discouragement of human expression.

Enterprise GenAI Integration: The Hidden Workflow

Prompting & Iteration
Output Verification (Factual/Ethical)
Personalization & Editing
Human Oversight Required
92% Students Using GenAI for University Essays (Preliminary Data)

A preliminary study revealed that a staggering 92% of students surveyed admitted to using Generative AI for their university essays (Weale & correspondent, 2025). This high adoption rate highlights not only the perceived utility of GenAI among students but also raises concerns about academic integrity, the future of human writing skills, and the potential for widespread reliance on AI that could discourage authentic human expression.

The ubiquitous presence of human-like AI outputs fosters distrust in genuine human creations, leading to a devaluation of human expression and potentially discouraging individuals from engaging in authentic creative or communicative acts.

Advanced ROI Calculator: Human-Centric AI Deployment

Estimate the potential return on investment while considering the unique challenges of integrating GenAI into human-centric processes. Balance efficiency gains with the imperative to preserve authentic expression and human skill.

Estimated Annual Savings $0
Annual Human Hours Reclaimed 0

Strategic AI Implementation Roadmap

Our phased approach ensures responsible and effective GenAI integration, prioritizing human authenticity, ethical deployment, and sustainable value creation.

Phase 1: Discovery & Strategy Alignment

Initial consultations to understand current expressive workflows, identify key AI application areas, and assess your organization's AI readiness. We define strategic objectives for GenAI integration that align with your values.

Phase 2: Pilot Program & Ethical Framework Development

Implement GenAI in a controlled pilot environment. Develop robust ethical guidelines, disclosure protocols, and verification processes to maintain authenticity, trust, and prevent deskilling.

Phase 3: Integration & Skill Augmentation

Roll out GenAI across relevant departments, focusing on augmenting human capabilities rather than replacing core expression tasks. Provide comprehensive training to upskill employees in advanced prompting, critical evaluation, and ethical AI use.

Phase 4: Continuous Monitoring & Value Optimization

Regularly monitor GenAI outputs for quality, authenticity, and ethical compliance. Optimize prompts, fine-tune models, and ensure GenAI serves to enhance, not degrade, human expression, driving long-term value.

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