# sfeir-ia-frontieres-metiers-skill-based-organisation-2026-08-01

## Veille

In-depth op-ed published on **sfeir.com** on August 1, 2026, authored by **SFEIR** (the firm's editorial voice). It brings together **two July 2026 publications** with opposite methodologies — the preregistered field experiment **"The Cybernetic Teammate"** at **Procter & Gamble** (Dell'Acqua, Ayoubi, Lifshitz, Sadun, **Ethan Mollick** et al., *Organization Science* 37(4), 2026) and the first report in **OpenAI Economic Research**'s **"Work at the Frontier"** series (Jul. 27, 2026, >800,000 messages from US ChatGPT users) — into a single thesis: *"generative AI doesn't just speed up existing work, it redistributes who does what."* The architecture unfolds in four stages: **the mechanism** (P&G: AI acts as a *boundary-spanning* device, erasing functional silos — an individual + AI reaches the level of a pair without AI, **+0.37 σ**), **the scale** (OpenAI: **43.5%** of profession-specific messages fall outside the user's own profession), **the agenda** (Mollick: the walls are thinning, the division of labor must be rethought, and well-orchestrated recomposition "pays off handsomely"), then **the firm's response** — **Skill Based Organisation (SBO)**, adopted at SFEIR under the impetus of **Rosalie Zandona** (VP People & Culture): **actually operational skill** replaces the job description as the unit of organization (**up to 13 skills identified per role**), shifting from a **status-based identity** ("I am a manager") to an **operational identity** ("I know how to design complex architectures"). The rhetorical move is proof by internal example: *"we made the shift in-house before recommending it."* **Three caveats are noted**: the SBO shift dates back to **February 2026**, hence *predating* the diagnosis it is supposed to resolve (the argumentative order reverses the chronological order); **nothing in the data demonstrates** that a skill-based organization absorbs crossover better than a role-based one (an untested design hypothesis); the P&G result has been circulating **since March 2025** (NBER w33641) — the "a few weeks earlier" applies to the peer-reviewed publication, not to the result itself.

## Titre Article

L'IA fait tomber les murs entre les métiers

## Date

2026-08-01

## URL

https://www.sfeir.com/articles/ia-frontieres-metiers-skill-based-organisation/

## Keywords

Skill Based Organisation, SBO, skill-based organization, operational skill, operational identity, status-based identity, job description, unit of organization, task crossover, task spillover, porosity of professions, functional silos, boundary-spanning, The Cybernetic Teammate, Procter & Gamble, preregistered experiment, Dell'Acqua, Ethan Mollick, Karim Lakhani, Raffaella Sadun, Organization Science, Work at the Frontier, OpenAI Economic Research, How AI is Expanding What People Do at Work, 800,000 messages, ChatGPT, standard deviation, top 10%, evaluative judgment, best-idea selection, task import/export, financial calculation, technology troubleshooting, organization size, generalist, early signal, division of labor, recomposition of professions, functional org chart, Rosalie Zandona, VP People & Culture, HR, CHRO, Deloitte, places to grow, AI transformation, SFEIR, thought leadership

## Authors

**SFEIR** — ESN française « AI Only » (~850 ingénieurs, 8 agences France & Benelux). Voix éditoriale du cabinet (byline « SFEIR »).

Sources mobilisées et créditées par l'article :
- **Fabrizio Dell'Acqua, Charles Ayoubi, Hila Lifshitz, Raffaella Sadun, Ethan Mollick, Lilach Mollick, Yi Han, Jeff Goldman, Hari Nair, Stew Taub, Karim R. Lakhani** — « The Cybernetic Teammate », *Organization Science*, vol. 37, n° 4, 2026, p. 1217-1242.
- **OpenAI Economic Research** — « How AI is Expanding What People Do at Work », 1er rapport de la série *Work at the Frontier*, 27 juillet 2026.
- **Rosalie Zandona** (VP People & Culture, SFEIR) — « SBO : le grand saut vers des compétences réellement opérationnelles », sfeir.dev, 19 février 2026.

## Ton

**Profile**: a firm point of view (thought leadership) aimed at **CHROs / CEOs / CIOs**, grounded in two strong external sources and closed with **proof by internal example**. **Organizational and HR** register — unusual for SFEIR's editorial line, which is normally centered on the SDLC and engineering.

**Style**: a **four-stage funnel** structure signaled by thesis-subheadings (*At P&G, AI behaves like a teammate → OpenAI measures task crossover at scale → Ethan Mollick's view: the walls are thinning → Skill Based Organisation, the response SFEIR settled on → Key takeaways*). The structuring figure is **convergence through opposite methods**: a randomized experiment and massive usage-trace data, *"two opposite methods, one same result"* — a robustness argument set up right from the standfirst. The studies are presented **faithfully and with figures**, including the results that are inconvenient (human pairs without AI remain better at choosing their own best idea). Then an **editorial pivot**: the external diagnosis becomes a question of organizational design, one the firm has already answered. Signature phrases: *"AI redistributes who does what,"* *"without AI, everyone stays in their own lane,"* *"whoever encounters the problem handles it, instead of delegating it,"* *"informal tinkering in the shadow of the org chart,"* *"the division of labor is already being recomposed, in your employees' prompts,"* *"what remains is deciding what to replace the job description with,"* *"we made the shift in-house before recommending it."*

## Pense-betes

- **Date / source**: **August 1, 2026**, SFEIR. Framing article built on two third-party publications — the P&G experiment and the OpenAI Economic Research report.
- **Key framing**: AI doesn't just speed up work, it **redistributes who does what**. The blurring of professional boundaries is a measurable organizational fact; the conclusion drawn is that the job description can no longer be the unit of work organization. ### The robustness argument Two opposite methods converge: a **preregistered field experiment** (causal, small N, controlled context) and **massive usage trace data** (scale, correlational, no context). Neither would suffice alone; together they cover each other's blind spots. It's this combination that matters, more than any single isolated figure. ### P&G — the mechanism **791 experienced professionals** (R&D and sales), a full day on real product-innovation challenges drawn from their business units, with the best proposals presented to executives. 2×2 protocol: solo / cross-functional pair × with / without AI. | Result | Value | |---|---| | Individual + AI vs. individuals alone | **+0.37 σ** | | Team without AI vs. individuals alone | +0.24 σ | | Team + AI, probability of a **top-10%** solution | roughly **×3** | | Correct selection of one's best idea | **~50%** for a human pair without AI, **37%** with AI | The effect plays out in the **tail of the distribution**, not in the average. Without AI, sales people produce sales-oriented output and R&D people produce technical output; with AI, the distinction disappears — solutions balanced across the entire technical-commercial spectrum, without any loss of quality. The authors call the mechanism **boundary-spanning**: employees far removed from product development, alone with AI, reach the level of teams that include a domain expert. Emotional dimension measured: more enthusiasm and energy, less anxiety and frustration. The nuance that holds up: AI raises the **average** quality of ideas, but human pairs without AI remain better at **identifying their own best idea**. Human evaluative judgment retains its place — the same generate/verify asymmetry as in [[sfeir-code-review-anneau-contraintes-2026-07-30]], a different domain. ### OpenAI — the scale **43.5%** of **profession-specific** messages fall outside the user's own profession, once generic tasks are set aside. The reprocessing matters: this is not 43.5% of all usage, but 43.5% of profession-specific usage. | Function | Share out-of-profession | Import | Export | |---|---|---|---| | Customer experience | 77% | — | — | | Design | 75% | **35.2%** | **1.7%** | | HR | 69% | — | — | | Legal | 56% | — | — | | Marketing | 53% | 24.3% | **8.9%** | | Sales / finance | 40% | — | — | | Engineering | **28%** | 18.5% | 7.4% | Two tasks appear in the top 3 borrowed tasks across all groups: **financial calculation** and **technology troubleshooting**. Claimed status: an **early signal**, making the recomposition visible before job descriptions and labor-market statistics do. **The result the article makes least use of**: engineering is the **least porous and most export-heavy** profession. The porosity described is thus largely one-way, toward the technical side. For a firm whose core business is engineering, this is the most actionable result, and the article draws nothing from it. ### The weak link in the argument The organization-size effect rests on a **2.6-point gap** (18.9% for 2-5-role spaces → 16.3% above 100), figures not commensurate with the 43.5%. Verification against the primary source [[openai-work-at-the-frontier-task-crossover-2026-07-27]] shows the gap holds only *"among average users,"* and *"among the heaviest users, we do not see the same monotonic pattern."* OpenAI's conclusion is conditional (*"AI may be especially useful as a generalist tool"*), where SFEIR states it as fact. Not to be cited as a strong result. ### Mollick's three-step agenda Co-author of the P&G study, he links the two publications himself, noting that the cross-functional usage measured by OpenAI is more pronounced than the experiment had suggested. (1) Organizational boundaries are becoming porous. (2) Companies will have to rethink the division of labor, and *"things are getting messy right now."* (3) Properly orchestrated, the recomposition pays off both in employee satisfaction and in performance. ### SFEIR's response: the Skill Based Organisation Under the impetus of **Rosalie Zandona** (VP People & Culture). If tasks circulate, the fixed job description can no longer be the unit of organization: **actually operational skill** becomes the base building block — up to **13 skills identified per role** — deployed wherever the concrete need is, independent of job title. The underlying shift goes from a **status-based identity** ("I am a manager") to an **operational identity** ("I know how to design complex architectures"), made objective and transparent. Claimed benefit: task crossover becomes visible and tooled instead of remaining *"informal tinkering in the shadow of the org chart."* Closing figure: according to Deloitte, skill-based organizations are **98% more likely** to be **perceived** as excellent places to grow. ### Three caveats to raise in pre-sales conversations 1. **Reversed chronology** — SFEIR's SBO shift is documented in Zandona's article of **February 19, 2026**, five months before the two publications it is supposed to "answer point by point." The SBO is reread in their light, not adopted in response to them. 2. **The logical leap is not addressed** — both studies measure **task spillover**; the SBO responds with a **change in the unit of organization**. Nothing in the data says that a skill-based organization absorbs crossover better than a role-based one. It's a design hypothesis, and not the only possible one: the other common response is the **merged role** (SFEIR's own "Product Engineer," April 2026), which goes in the opposite direction. 3. **The Deloitte figure measures perception, not performance.** It covers only the satisfaction side of the dual benefit Mollick announced; the performance side of the SBO is quantified nowhere. ### Watch points on the sources
- **P&G freshness**: the result has been circulating since **March 2025** (NBER w33641 / HBS WP). The article dates it to its publication in *Organization Science* (2026): true for the peer-reviewed journal, not for the result itself. For a watch note, this counts as a consolidation.
- **Diverging N**: 791 professionals stated by the article, **776** in the 2025 working paper. Unexplained discrepancy — to be checked against the *Organization Science* version.
- **Title variant**: *"… on Generative AI and Teamwork"* (SFEIR) vs. *"… on Generative AI Reshaping Teamwork and Expertise"* (working paper).
- **OpenAI base**: US ChatGPT users, neither a representative sample of the active population nor multi-assistant. And it is OpenAI measuring OpenAI's own effect — a bias SFEIR flags elsewhere, but not noted here.
- **Missing figure**: the report gives **two** headline figures — **16.8%** of work-related messages and 43.5% of profession-specific messages, the latter calculated after removing 61.5% of generic usage. SFEIR keeps only the second one. **Disambiguation**: "skill" in the SBO sense (an HR unit of organization) has no relation to the *Agent Skills* of the corpus — an FR/EN homonym that must not be allowed to merge in the graph.

## RésuméDe400mots

In this op-ed published on sfeir.com on August 1, 2026, **SFEIR** brings together two July 2026 publications with opposite methodologies to make the same point: *"generative AI doesn't just speed up existing work, it redistributes who does what."*

**The mechanism (P&G).** The preregistered field experiment **"The Cybernetic Teammate"** (Dell'Acqua, Mollick, Lakhani et al., *Organization Science* 2026) engaged **791 professionals** from R&D and sales for a full day on real product-innovation challenges, crossing two variables: solo or cross-functional pair, with or without AI. Performance result: an **AI-equipped individual reaches the level of a pair without AI** (**+0.37 σ** versus **+0.24 σ**), and a **team + AI roughly triples** the probability of a **top-10%** solution. Organizational result: without AI, everyone stays in their own lane; **with AI, the distinction disappears** — both groups produce solutions balanced across the entire technical-commercial spectrum, without any loss of quality. The authors describe AI as a **boundary-spanning** mechanism. One nuance closes out the picture: human pairs without AI remain **better at identifying their own best idea** (~50% versus 37%) — **human evaluative judgment stays in the loop**.

**The scale (OpenAI).** Drawing on more than **800,000 messages** from US ChatGPT users, **OpenAI Economic Research** measures **task crossover**: once generic tasks are set aside, **43.5%** of profession-specific messages **fall outside the user's own profession** — up to 77% in customer experience, 75% in design, 69% in HR, versus **28% in engineering**. Flows are asymmetric: design imports (35.2%) without exporting (1.7%), engineering does the opposite. This usage data is presented as an **early signal**, visible before job descriptions and employment statistics catch up.

**The agenda (Mollick).** Co-author of the P&G study, he links the two publications in three steps: boundaries are becoming porous; companies will have to rethink the division of labor, and *"things are getting messy right now"*; but **properly orchestrated, the recomposition pays off handsomely** — in both satisfaction and performance.

**SFEIR's response.** The firm shifted to a **Skill Based Organisation** under the impetus of **Rosalie Zandona** (VP People & Culture): if tasks circulate, the fixed job description can no longer serve as the unit of organization. **Operational skill** becomes the base building block — **up to 13 per role** — shifting from a **status-based identity** to an **operational identity**. Task crossover then becomes *"visible, tooled, and valued"* instead of *"informal tinkering in the shadow of the org chart."* *"What remains is deciding what to replace the job description with. SFEIR answered with skill."*

## GrapheDeConnaissance

- SFEIR —publie→ L'IA fait tomber les murs entre les métiers (DOCUMENT, 0.98)
- L'IA fait tomber les murs entre les métiers —référence→ The Cybernetic Teammate (DOCUMENT, 0.98)
- L'IA fait tomber les murs entre les métiers —référence→ Work at the Frontier (DOCUMENT, 0.98)
- SFEIR —affirme_que→ l'IA générative ne se contente pas d'accélérer le travail existant, elle redistribue qui fait quoi (AFFIRMATION, 0.96)
- IA générative —permet→ boundary-spanning (CONCEPT, 0.93)
- The Cybernetic Teammate —mesure→ un individu équipé d'IA atteint +0,37 σ vs individus seuls, contre +0,24 σ pour les équipes sans IA (MESURE, 0.95)
- The Cybernetic Teammate —mesure→ équipe + IA triple environ la probabilité d'une solution classée dans le top 10 % (MESURE, 0.9)
- IA générative —réduit→ les silos fonctionnels (les solutions cessent d'être marquées par le métier d'origine) (AFFIRMATION, 0.92)
- The Cybernetic Teammate —mesure→ les binômes sans IA identifient mieux leur meilleure idée (~50 % contre 37 % avec IA) (MESURE, 0.92)
- jugement évaluatif humain —s_oppose_à→ la délégation complète de la sélection des idées à l'IA (AFFIRMATION, 0.85)
- Work at the Frontier —mesure→ 43,5 % des messages spécifiques à un métier sortent du métier de l'utilisateur (MESURE, 0.95)
- Work at the Frontier —mesure→ task crossover par fonction : 77 % expérience client, 75 % design, 69 % RH, 28 % ingénierie (MESURE, 0.92)
- Work at the Frontier —mesure→ le design importe 35,2 % des tâches et n'en exporte que 1,7 %, l'ingénierie fait l'inverse (MESURE, 0.9)
- task crossover —observé_dans→ les données d'usage de ChatGPT, comme signal avancé de la recomposition des métiers (AFFIRMATION, 0.9)
- OpenAI Economic Research —publie→ Work at the Frontier (DOCUMENT, 0.95)
- Ethan Mollick —affirme_que→ les frontières organisationnelles deviennent poreuses et les entreprises devront repenser la division du travail (AFFIRMATION, 0.93)
- Ethan Mollick —affirme_que→ correctement orchestrée, la recomposition du travail rapporte gros en satisfaction et en performance (AFFIRMATION, 0.9)
- Skill Based Organisation —remplace→ la fiche de poste comme unité d'organisation du travail (AFFIRMATION, 0.93)
- SFEIR —utilise→ Skill Based Organisation (METHODOLOGIE, 0.95)
- Rosalie Zandona —travaille_chez→ SFEIR (ORGANISATION, 0.95)
- Rosalie Zandona —dirige→ Skill Based Organisation (METHODOLOGIE, 0.9)
- Skill Based Organisation —permet→ de rendre le task crossover visible, outillé et valorisé au lieu d'un bricolage informel (AFFIRMATION, 0.88)
- compétence opérationnelle —remplace→ l'identité statutaire par une identité opératoire (AFFIRMATION, 0.88)
- Deloitte —mesure→ les organisations par compétences ont 98 % de chances supplémentaires d'être perçues comme d'excellents lieux de croissance (MESURE, 0.85)
- Procter & Gamble —observé_dans→ The Cybernetic Teammate (DOCUMENT, 0.93)

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Canonical: https://www.thekb.eu/en/fiches/sfeir-ia-frontieres-metiers-skill-based-organisation-2026-08-01/