# mollick-making-ai-work-leadership-lab-crowd-2025-05-22

## Veille

Organizational AI adoption, transformation of work, innovation strategy, leadership, productivity, oneusefulthing.org

## Titre Article

Making AI Work: Leadership, Lab, and Crowd

## Date

2025-05-22

## URL

https://www.oneusefulthing.org/p/making-ai-work-leadership-lab-and

## Keywords

AI adoption, organizational transformation, productivity, leadership, ambidextrous innovation, experimentation, human-AI collaboration, competitive advantage, organizational learning

## Authors

Ethan Mollick

## Ton

**Profile:** Academic-practitioner | First person, thought leader | Analytical-prescriptive register | Intermediate technical level

Mollick adopts the voice of a framework builder, synthesizing research findings into actionable organizational strategies. The tripartite structure "Leadership, Lab, Crowd" reveals systematic thinking typical of management frameworks. Language balanced between academic (organizational learning, ambidextrous innovation) and practical (experimentation, productivity gains). Encouraging and pragmatic tone, positioning AI transformation as manageable rather than overwhelming. The Substack format allows for depth of exploration impossible in shorter formats. Typical of academic-practitioners (Rita McGrath, Clayton Christensen style) translating research into executive advice, aimed at organizational leaders facing technological change.

## Pense-betes

- **AI adoption paradox**: large individual gains (up to tripled productivity) but minimal organizational improvement
- **Real performance gains**: studies in Denmark and the United States show substantial time savings
- **Widespread adoption**: 30-40% of US workers use AI professionally (vs 20% in official systems)
- **Untapped potential**: deep research tools, AI agents, and content generation can accomplish major transformations
- **3-part framework**: Leadership, Lab, Crowd
- **Leadership**: clear vision, incentives for transparent adoption vs "shadow" usage
- **Lab**: ambidextrous innovation unit, turns workflows into scalable solutions, AI benchmarking
- **Crowd**: experienced workers discover effective applications through trial and error
- **Key recommendations**:
- Establish explicit experimentation zones (not just vague ethical guidelines)
- Training = hands-on experience (not just prompting techniques)
- Organizational benchmarks on the company's real tasks
- Rethink incentive structures that push people to hide AI usage
- Question the purpose of tasks when efficiency eliminates bottlenecks
- **Key message**: "Competitive advantage belongs to organizations that learn fastest, not those that wait for perfect clarity"
- **Moment to act**: now, while everything is still uncertain and messy

## RésuméDe400mots

Ethan Mollick identifies a major paradox in enterprise AI adoption: while individual workers report significant productivity gains (some claiming AI "tripled their productivity"), organizations see minimal overall performance improvements.

**Four central observations**

First, performance gains are real and documented. Studies in Denmark and the United States show workers achieving substantial time savings across varied domains: product development, sales, consulting, and technical roles.

Second, adoption is widespread. Between 30 and 40% of US workers use AI professionally, though official enterprise systems show lower engagement rates (~20%), revealing significant "shadow" usage.

Third, untapped potential exists. Deep research tools, AI agents, and content generation systems can accomplish transformational work well beyond current organizational implementations.

Fourth, organizations are not capturing this value. Individual productivity gains do not automatically translate into organizational improvements without systemic innovation.

**The Framework: Leadership, Lab, and Crowd**

Mollick proposes a three-component model to resolve this paradox.

**Leadership** must establish a clear vision of AI's future impact on work and create incentives that encourage transparent adoption rather than hidden usage driven by fear of layoffs or diminished recognition.

The **Lab** functions as an ambidextrous innovation unit, turning workflows discovered by the crowd into scalable solutions, establishing AI capability benchmarks on the organization's real tasks, and building experimental prototypes that test emerging possibilities.

The **Crowd** represents experienced workers who organically discover effective AI applications through trial and error, then share successful workflows across the company.

**Practical recommendations**

Mollick stresses the need to move beyond vague ethical guidelines to establish explicit experimentation zones. Training should be reframed as hands-on experience rather than instruction in prompting techniques. Organizations must build benchmarks measuring AI performance on their real tasks.

He also highlights the importance of addressing incentive structures that discourage workers from revealing AI-assisted productivity, and of fundamentally rethinking the purpose of tasks when efficiency gains eliminate previous bottlenecks.

**Strategic vision**

Mollick's critical insight: competitive advantage belongs to organizations that learn fastest, not those that wait for perfect clarity. "The time to start is not when everything becomes clear—it's now, while everything is still messy and uncertain."

## GrapheDeConnaissance

- Ethan Mollick —recommande→ framework Leadership-Lab-Crowd (METHODOLOGIE, 0.99)
- Ethan Mollick —publie→ One Useful Thing (ORGANISATION, 0.99)
- Manus —est_basé_sur→ Claude (TECHNOLOGIE, 0.97)
- Ethan Mollick —utilise→ Manus (TECHNOLOGIE, 0.97)
- Shopify —a_créé→ politique IA prioritaire (CONCEPT, 0.95)
- Duolingo —a_créé→ politique IA prioritaire (CONCEPT, 0.95)
- Andrew Carton —soutient→ importance d'une vision concrète du leadership (CONCEPT, 0.9)
- Ethan Mollick —affirme_que→ les gains individuels IA ne se traduisent pas en gains organisationnels (AFFIRMATION, 0.98)
- travailleurs américains —utilise→ IA (TECHNOLOGIE, 0.93)
- Secret Cyborgs —est_basé_sur→ incitations organisationnelles inadaptées (CONCEPT, 0.92)
- framework Leadership-Lab-Crowd —utilise→ innovation ambidextre (CONCEPT, 0.95)
- Wharton —a_créé→ simulation business complexe (METHODOLOGIE, 0.9)
- Google Veo 3 —permet→ génération de vidéos à partir de texte (CONCEPT, 0.95)

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Canonical: https://www.thekb.eu/en/fiches/mollick-making-ai-work-leadership-lab-crowd-2025-05-22/