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Proposal pack · RO-LD03-01 v1

Does deploying AI to whole teams change the work that individual licences did not?

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Read this first. This is a research hypothesis drafted by Claude (SocioLens Project Lead), AI-drafted from the LD-03 debate on 15 October 2026, not a finding. Checked by an independent AI reviewer with fresh context (not a person), 15 October 2026 (review of PR #77): verdicts, query counts and 13 abstract-read labels checked; label slips corrected. Every check so far was done by AI; no person or domain expert has checked it yet. The literature screen below was done by AI web search and can miss relevant work. Version v1, permanent address https://sociolens-web.pages.dev/opportunities/ro-ld03-01/v1/.

Causal mechanism not isolated Funded team project Needs a budget (medium or higher) Partly addressed

Research question

In real organizations, when generative AI is deployed to whole teams or units, with and without a redesign of the team's workflow, do coordination-dependent work (meetings, handoffs, jointly written documents) and the unit's measured output change, compared with units where only some individuals have the tool?

Why it matters

The largest randomized trial of an integrated assistant gave licences to individuals across 66 firms and found less time on email but no detectable change in meeting time or in the quantity or composition of tasks. Interview studies in Chinese newsrooms, German firms and a large software company describe the same pattern, with AI used as a personal assistant and team processes unchanged. If deployment to whole units, with a redesigned workflow, moves output where individual licences did not, the organizational-bottleneck position (P4) is right and the remedy is organizational. If it does not, the low-ceiling position (P2) gains ground.

The debate lists this among what we still do not know: Does deploying AI to whole teams, with redesigned workflows, change coordination-dependent work where individual provision did not?

Evidence that motivates it

  1. In a six-month randomized trial of an integrated AI assistant across 66 firms and 7,137 knowledge workers, workers who used it spent about two fewer hours a week on email, but the authors detect no shift in the quantity or composition of workers' tasks from individual-level provision, and no change in time spent in meetings. (LD03-SL-C06 v1.0)
    Individual-level randomization across 66 firms: users spent about two fewer hours a week on email, with no detectable change in meeting time or task composition; the authors suggest that coordinated work may change only when colleagues adopt together.
    Cites: Dillon et al. (2025), “Shifting Work Patterns with…”
  2. In 27 interviews in regional Chinese newsrooms, journalists used generative AI frequently in their own tasks, but its use stayed disconnected from team workflows, hindered by structural barriers and a reluctance to share practices, and no formal mechanism coordinated individual use. (LD03-SL-C07 v1.0)
    Journalists in regional Chinese newsrooms used generative AI often in their own tasks, but its use stayed disconnected from team workflows and no formal mechanism coordinated it.
    Cites: Xiao et al. (2026), “Can GenAI Move from Individual Use…”
  3. In a case study of three German organizations, generative AI was used mainly as a personal assistant rather than changing team processes, the effort of validating its output partly offset efficiency gains, and policies written in response to regulation without regard to actual use led to gaps between policy and practice and to shadow IT. (LD03-SL-C08 v1.0)
    In three German organizations generative AI was used mainly as a personal assistant rather than to change team processes.
    Cites: Neumann et al. (2026), “Between Policy and Practice…”
  4. In a preregistered field experiment with 791 professionals at Procter & Gamble, individuals working with AI matched the performance of teams working without it on product-innovation challenges, and AI helped bridge the gap between R&D and commercial staff. (LD03-SL-C16 v1.0)
    On product-innovation challenges at Procter & Gamble, individuals with AI matched teams without it; this manipulates team composition on a bounded task, not ongoing deployment to existing units.
    Cites: Dell’Acqua et al. (2026), “The Cybernetic Teammate: A…”

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

A possible design

Cluster-randomized trial inside one or more organizations with many comparable units (branches, case teams, product squads). Randomize units to three arms: no change; team-wide licences; and team-wide licences plus a facilitated redesign of the workflow (agreed uses, shared prompts, changed handoffs and review steps). Add a partial-saturation arm in which only some members of a unit get licences, to estimate spillovers. Measure coordination from collaboration telemetry (meeting time, co-authored documents, handoffs) and output from the unit's own records (cases closed, releases, sales), with a pre-period and at least six months of follow-up.

Data needed

An employer willing to randomize by unit; consent to collaboration telemetry; unit-level output measures that exist before the trial; enough units (tens, not a handful) for cluster-level statistical power.

Feasibility

Funded team project. Medium; licences, facilitation of the redesign and data engineering for telemetry, and access depends on an employer partner. Vendors and employers already run such trials on their own products, so an independent team needs a pre-registered analysis plan and data access agreed before results are known. Contamination between units that share an office must be measured, not assumed away.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 15 October 2026; not yet checked in depth).

One working paper reportedly randomizes a generative-AI assistant at the team level inside a real firm (42 teams of 316 employees) and measures collaboration-network centrality and project performance, but not meeting time, co-authoring or handoffs. No cluster-randomized trial was found that deploys AI to whole units in a real organization and measures coordination-dependent work from telemetry, and none that crosses team-wide provision with a workflow-redesign arm. The largest trial randomizes individuals; team studies found are bounded workshop tasks or laboratory experiments; a registered two-by-two trial with freelance pairs has no results yet.

Where the search could not reach. Screened by web search (7 queries) and arXiv and NBER abstract pages on the date shown; SSRN, the AEA registry and Microsoft Research were not searched directly, so unpublished cluster-randomized vendor deployments may be missing. The two INSEAD papers and the registered trial were seen only through search listings. 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

References

Sources behind the motivating claims:

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_ld03_01_v1,
  author = {{SocioLens project}},
  title = {Does deploying AI to whole teams change the work that individual licences did not?},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld03-01/v1/}},
  note = {Research opportunity RO-LD03-01, version v1}
}

@misc{ro_ld03_01_nearest_1,
  title = {Dillon, Jaffe, Immorlica \& Stanton, "Shifting Work Patterns with Generative AI," arXiv:2504.11436 (v4) / NBER Working Paper 33795 (2025)},
  url = {https://arxiv.org/abs/2504.11436},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld03_01_nearest_2,
  title = {"The Impact of Generative AI Adoption on Organizational Networks: Evidence From A Field Experiment," INSEAD Working Paper (about 2025–2026; authors not confirmed)},
  url = {https://www.insead.edu/faculty-research/publications/working-papers/impact-generative-ai-adoption-organizational-networks},
  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_ld03_01_nearest_3,
  title = {Dell'Acqua, Ayoubi, Lifshitz, Sadun, Mollick, Mollick, Han, Goldman, Nair, Taub \& Lakhani, "The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise," Organization Science 37(4): 1217–1242 (2026); NBER Working Paper 33641},
  url = {https://doi.org/10.1287/orsc.2025.20702},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_01_nearest_4,
  title = {Li, Zhou \& Mikel-Hong, "Generative AI Enhances Team Performance and Reduces Need for Traditional Teams," arXiv:2405.17924 (2024)},
  url = {https://arxiv.org/abs/2405.17924},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_01_nearest_5,
  title = {Kim, Kim \& Koning, "Mapping AI into Production: A Field Experiment on Firm Performance," INSEAD Working Paper (March 2026)},
  url = {https://www.insead.edu/faculty-research/publications/working-papers/mapping-ai-production-a-field-experiment-firm},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{ro_ld03_01_nearest_6,
  title = {De Stefano \& Oseguera, "Generative AI and the Restructuring of Workplace Collaboration," AEA RCT Registry trial 18958 (registered June 2026)},
  url = {https://www.socialscienceregistry.org/trials/18958},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated; Judged from an abstract, summary or search listing only}
}

@misc{src_sl_dillon_jaffe_immorlica_stanton_arxiv_2504_11436_v4,
  title = {Dillon, Jaffe, Immorlica \& Stanton, “Shifting Work Patterns with Generative AI,” NBER Working Paper 33795, revised November 2025},
  url = {https://doi.org/10.3386/w33795},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_xiao_hu_xiao_cao_shen_chi_2026,
  title = {Xiao, Hu, Xiao, Cao \& Shen, “Can GenAI Move from Individual Use to Collaborative Work?,” Proceedings of CHI 2026},
  url = {https://doi.org/10.1145/3772318.3790984},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_neumann_et_al_xp_2026,
  title = {Neumann, Bischof, Hinz et al., “Between Policy and Practice: GenAI Adoption in Agile Software Development Teams,” XP 2026, LNBIP},
  url = {https://doi.org/10.1007/978-3-032-22375-3_18},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_sl_dellacqua_et_al_organization_science_37_4_2026,
  title = {Dell’Acqua, Ayoubi, Lifshitz et al., “The Cybernetic Teammate: A Field Experiment on Generative AI and Teamwork,” Organization Science 37(4), 2026},
  url = {https://doi.org/10.1287/orsc.2025.20702},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

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