Do French firms that sign AI agreements then hire and train differently?

Research question

In French firms, does signing a company-level agreement that addresses AI change subsequent hiring (junior versus experienced, AI-related versus other roles) and training compared with similar non-signing firms in the same sector, and do effects differ by type of commitment?

Why it matters

The debate's early evidence comes from countries with very different labour institutions, and Norwegian and Finnish authors suggest unions and employment protection may explain why their results differ from the US, without testing it. Adoption effects in the Danish data run mainly through recruitment. If negotiated agreements steer firms toward retraining and away from hiring freezes, the "which margin moved" answer depends partly on labour institutions, not only on the technology. Researchers have catalogued AI clauses, but no study found asks whether they change what firms then do.

The debate lists this among what we still do not know: How do collective bargaining, worker voice and labour regulation alter adoption strategies?

Evidence that motivates it

1. Danish firms that began using AI in 2023 had roughly 11% lower relative employment growth by late 2025 than comparable non-adopters, mainly through lower recruitment, while economy-wide employment remained relatively unaffected. (LD02-HQ-C03 v1.1)
   Danish adopters show lower relative employment growth mainly through recruitment, not layoffs; recruitment is therefore the margin on which agreements would need to make a difference, and the claim does not license reading it as job loss.
   Cites: Danmarks Nationalbank Working Paper No. 223, “AI Adoption…”
2. Exposure, adoption, actual use, task productivity, firm productivity, labour demand, hiring, employment stock and wages are distinct empirical objects; evidence at one stage does not establish the next. (LD02-HQ-C01 v1.0)
   Adoption, use and labour demand are distinct stages; an agreement can act on any of them, so the design measures hiring and training separately from the fact of signing.
   Cites: R05 — HQ Cross-Batch Synthesis & Adjudication v0.1

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Two parts. (1) Text-as-data: download French company agreements from the public ACCO open-data base (agreements concluded since 1 September 2017), identify those addressing AI, and hand-code a sample for commitments (consultation before deployment, training, job-security guarantees, limits on monitoring, hiring commitments); validate any automated classifier against the hand-coded sample. (2) Event study: match signing firms to non-signers in the same sector and size band, and compare job postings (junior versus experienced, roles naming AI tools) and, where accessible, administrative hiring and training records, before and after signature. Report the results by type of commitment. Interviews with a few union and HR negotiators can explain the mechanisms. Part (1) builds on the existing CEET-Cnam and IRES classifications of AI agreements in ACCO (checked against a hand-coded sample) rather than starting afresh; the matched event study in part (2) is the core contribution.

Data needed

ACCO company agreements (free, published by the French government under the Licence Ouverte; the dataset page does not say whether the files carry firm identifiers or only names, so this needs checking before linkage); job-posting data by firm (commercial postings data, or the national employment service's data if a research agreement can be reached); French administrative firm and worker data through the secure-access centre (CASD) if the project can obtain access.

Feasibility

One PhD chapter or paper. Low for part (1), which uses only open data; medium for part (2), depending on postings-data licences and secure-access fees. Part (1) alone is a strong master's thesis. Signing an AI agreement is not random (larger, unionised firms sign more often), so part (2) compares signers with closely matched non-signers and should describe its findings as associations unless a credible source of variation, such as a sector-level agreement that binds some firms, can be found.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

French company agreements that mention AI have already been mapped (CEET-Cnam for 2017-2024 and in a working paper covering 2017-2026; IRES in 2025), and a European report codes 108 agreements with AI provisions; US inventories also exist. None of those read links clause content to adoption, hiring or training outcomes. An Oxford project (2025-2028) studies German works councils and AI adoption and lists no results yet; it concerns works-council presence rather than agreement content. A Dutch study links technology adoption and union strength to re-employment, before generative AI, and German establishment-panel work on AI and training uses coverage, not agreement content, with findings that changed sign between the 2024 discussion paper and the 2025 published version. No study was found that links the content of French AI agreements to subsequent hiring and training. The ACCO dataset page does not say whether agreements carry a firm identifier such as a SIRET, so linkage remains to be checked.

Literature check. The text-as-data half of the design is already under way in France: CEET-Cnam has mapped French company agreements mentioning AI signed from 2017 (Connaissance de l'emploi no. 200, 2024, and a working paper covering 2017-2026), IRES (working paper 03.2025) has mapped AI clauses in French agreements, and an FES report (Kahancova et al., October 2026) analyses 108 European agreements with AI provisions. None of these, on the pages read, links agreement content to later hiring or training outcomes, and no study found does the event-study half. The opportunity should be narrowed to the outcome link, building on the existing French coding rather than proposing it as new. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown, mostly in English with one French query; French-language academic work, Google Scholar, SSRN and paywalled journals were not systematically searched, and one article was read at abstract level only. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: The CEET-Cnam working paper page blocks automated fetching and its full text was not read, so its corpus, method and whether it touches outcomes are unconfirmed; the Connaissance de l'emploi no. 200 note was read only through a third-party summary. The SSRN page of Agnolin & Gonzalez-Rostani, 'When Technology Manages: Workers, Unions, and Collective Bargaining in the Age of AI' (SSRN 6761358), could not be opened and was not used. Cairn.info returned no relevant hit; DARES publications were not found directly. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

• Barop (principal investigator), "Worker Voice and AI Adoption," Oxford Internet Institute research project funded by Schmidt Sciences (November 2025 – September 2028)
  Project page: examines whether German works councils steer AI toward complementing workers, combining administrative firm records with new employer surveys. No publications or results listed. Closest in aim; differs in country, institution (works councils, not agreement content) and outcome.
• Kerckhofs, "Collective bargaining on artificial intelligence at work," Eurofound (26 September 2025)
  Article reviewing 2024 developments in about a dozen European countries; cites a CNAM analysis finding AI mentioned in 242 French company agreements and reports clauses on training, transparency, oversight, limits on substitution and job security. Describes clauses; does not measure effects on adoption or employment.
• Kresge, "Negotiating Tech: An Inventory of U.S. Union Contract Provisions for the Digital Age," UC Berkeley Labor Center (last updated 2 July 2025)
  Provisions from more than 175 US union agreements, drawn from over 500 contracts, grouped by type. Informational inventory; no link to adoption or employment outcomes.
• ten Berge & Lippényi, "Firm-level technology implementation and finding a new job: The moderating role of industry unionization," Economic and Industrial Democracy 47(1), 271–303 (2026), DOI 10.1177/0143831X251326346 Judged from an abstract, summary or search listing only
  Abstract only read: Dutch matched employer–employee data 2001–2016; job searches after job loss are longer for workers leaving firms implementing new technology, and the association is weaker in more unionised industries. Technology in general, before generative AI; re-employment rather than hiring or adoption strategy.
• Muehlemann, "AI Adoption and Workplace Training," IZA Discussion Paper No. 17367 (October 2024)
  German BIBB establishment panel 2019–2021, AI and machine learning in general: AI adoption is associated with less continuing training, mainly in smaller firms and firms without a collective agreement. Uses only whether a firm is covered by an agreement, not what it says, and is associational. The published version (JEBO 2025, listed below) uses a longer panel and reports a modest increase in continuing training instead; whether the split by agreement coverage survives there was not checked.
• CEET-Cnam, "Négocier l'intelligence artificielle ? La mise en objet de l'IA et ses modes de problématisation dans la négociation collective d'entreprise (2017-2026)," CEET working paper (2026; authors and number not confirmed) Judged from an abstract, summary or search listing only Some details not confirmed
  Seen in search results only (the page blocks automated fetching). The title shows an analysis of how AI is framed in French company-level bargaining from 2017 to 2026, the same corpus period as ACCO. Likely overlaps part (1) of the design; whether it links agreements to outcomes could not be checked, and authors, corpus size and method are unconfirmed.
• CEET-Cnam, Connaissance de l'emploi no. 200, on French company agreements mentioning AI, 2017-2024, as summarised by Centre Inffo (Quentric, 17 October 2024) Judged from an abstract, summary or search listing only
  Read through the Centre Inffo summary only: AI agreements are a small but fast-growing share of company agreements signed 2017-2024 and span nearly all sectors and bargaining topics. Content mapping only; the summary reports no link to hiring or training. Authors and corpus source not stated on the page.
• IRES, "Dialogue social, Accords collectifs et Intelligence artificielle," Document de travail de l'IRES no. 03.2025 (2025) Judged from an abstract, summary or search listing only Some details not confirmed
  Abstract page read (authors not named there): maps French collective agreements on AI and finds very few address AI explicitly; clauses cover pay, working time, employment, training and AI-specific rights (data, explainability, disconnection); innovative agreements remain marginal. No firm-outcome analysis.
• Kahancová, Zholdasbay, Phonskaningtyas, Medas, Šarközi & Dragstra, "Artificial Intelligence, Algorithmic Management and Collective Bargaining in European services: Fast track or slow lane?," FES Competence Centre on the Future of Work (8 October 2026) Judged from an abstract, summary or search listing only
  Publication page read: survey of 195 UNI Europa union representatives in 43 countries (2023 and 2025) plus analysis of 108 collective agreements with AI provisions. Clause inventory across Europe; the abstract reports no link to firm hiring or training outcomes.
• Muehlemann, "Artificial intelligence adoption and workplace training," Journal of Economic Behavior & Organization 238 (2025), doi:10.1016/j.jebo.2025.107206 Judged from an abstract, summary or search listing only
  Published version of the IZA paper already listed (IDEAS abstract read): German establishment panel 2019-2023, staggered difference-in-differences; sustained AI adoption is associated with about 14% more new apprenticeships at training firms and a modest increase in continuing training shifted toward high-skilled workers. The abstract does not mention collective agreements. AI in general, not agreement content.

References

Sources behind the motivating claims:

• Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026 (SRC-LD02-NATIONALBANK-WP-223-2026, original)
• R05 — HQ Cross-Batch Synthesis & Adjudication v0.1 (SRC-R05-HQ-CROSS-BATCH-SYNTHESIS, no checked link to the original yet)

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld02_06_v2,
  author = {{SocioLens project}},
  title = {Do French firms that sign AI agreements then hire and train differently?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld02-06/v2/}},
  note = {Research opportunity RO-LD02-06, version v2}
}

@misc{ro_ld02_06_nearest_1,
  title = {Barop (principal investigator), "Worker Voice and AI Adoption," Oxford Internet Institute research project funded by Schmidt Sciences (November 2025 – September 2028)},
  url = {https://www.oii.ox.ac.uk/research/projects/worker-voice-and-ai-adoption/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_06_nearest_2,
  title = {Kerckhofs, "Collective bargaining on artificial intelligence at work," Eurofound (26 September 2025)},
  url = {https://www.eurofound.europa.eu/en/publications/all/collective-bargaining-on-artificial-intelligence-at-work},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_06_nearest_3,
  title = {Kresge, "Negotiating Tech: An Inventory of U.S. Union Contract Provisions for the Digital Age," UC Berkeley Labor Center (last updated 2 July 2025)},
  url = {https://laborcenter.berkeley.edu/negotiating-tech-an-inventory-of-u-s-union-contract-provisions-for-the-digital-age},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_06_nearest_4,
  title = {ten Berge \& Lippényi, "Firm-level technology implementation and finding a new job: The moderating role of industry unionization," Economic and Industrial Democracy 47(1), 271–303 (2026), DOI 10.1177/0143831X251326346},
  url = {https://ideas.repec.org/a/sae/ecoind/v47y2026i1p271-303.html},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_06_nearest_5,
  title = {Muehlemann, "AI Adoption and Workplace Training," IZA Discussion Paper No. 17367 (October 2024)},
  url = {https://docs.iza.org/dp17367.pdf},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld02_06_nearest_6,
  title = {CEET-Cnam, "Négocier l'intelligence artificielle ? La mise en objet de l'IA et ses modes de problématisation dans la négociation collective d'entreprise (2017-2026)," CEET working paper (2026; authors and number not confirmed)},
  url = {https://ceet.cnam.fr/publications/documents-de-travail/negocier-l-intelligence-artificielle-la-mise-en-objet-de-l-ia-et-ses-modes-de-problematisation-dans-la-negociation-collective-d-entreprise-2017-2026--1627949.kjsp},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld02_06_nearest_7,
  title = {CEET-Cnam, Connaissance de l'emploi no. 200, on French company agreements mentioning AI, 2017-2024, as summarised by Centre Inffo (Quentric, 17 October 2024)},
  url = {https://www.centre-inffo.fr/site-centre-inffo/lia-dans-les-entreprises-que-revelent-les-accords-negocies},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_06_nearest_8,
  title = {IRES, "Dialogue social, Accords collectifs et Intelligence artificielle," Document de travail de l'IRES no. 03.2025 (2025)},
  url = {https://ires.fr/publications/documents-de-travail-de-lires/n03-2025-dialogue-social-accords-collectifs-et-intelligence-artificielle/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only; Some details not confirmed}
}

@misc{ro_ld02_06_nearest_9,
  title = {Kahancová, Zholdasbay, Phonskaningtyas, Medas, Šarközi \& Dragstra, "Artificial Intelligence, Algorithmic Management and Collective Bargaining in European services: Fast track or slow lane?," FES Competence Centre on the Future of Work (8 October 2026)},
  url = {https://wageindicator.org/ai/what-we-do/publications/2026/artificial-intelligence-algorithmic-management-collective-bargaining-europe/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld02_06_nearest_10,
  title = {Muehlemann, "Artificial intelligence adoption and workplace training," Journal of Economic Behavior \& Organization 238 (2025), doi:10.1016/j.jebo.2025.107206},
  url = {https://doi.org/10.1016/j.jebo.2025.107206},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_ld02_nationalbank_wp_223_2026,
  title = {Danmarks Nationalbank Working Paper No. 223, “AI Adoption and Firm Size: Employment Effects in Monthly Administrative Data,” 16 September 2026},
  url = {https://www.nationalbanken.dk/en/news-and-knowledge/publications-and-speeches/working-paper/2026/ai-adoption-and-firm-size-employment-effects-in-monthly-administrative-data},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_r05_hq_cross_batch_synthesis,
  title = {R05 — HQ Cross-Batch Synthesis \& Adjudication v0.1},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}