In randomized experiments run by a large cross-border online retail platform in 2023–2024, building generative AI into seven business workflows changed sales by between nothing detectable and 16.3%, depending on how much the AI added to the firm's existing practice; the four workflows with gains imply about $5 a year per consumer.

SocioLens project,

Claim LD03-SL-C15 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with qualifiers: preprint
Verification state: Checked against the primary source; the source is a preprint. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Effects on profits or productivity (labour and capital inputs are not counted); effects in other firms
Limitations: Workflows chosen by managerial judgement, not systematically; two authors were paid consultants to the platform and one was its employee; not peer reviewed
Source (as cited): Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026
Source type: Preprint (recorded as “Preprint”)
Institutional position: Mixed
Maturity of the evidence: Emerging evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026 (Preprint)
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Version

This is the current version, v1.0. Earlier versions stay available, and their records never change.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (PREPRINT) (as printed: “ELIGIBLE WITH PREPRINT QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (PREPRINT)
Source type (coded): PREPRINT
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD03-SL-C15	—
2	PUBLIC-FACING CLAIM	In randomized experiments run by a large cross-border online retail platform in 2023–2024, building generative AI into seven business workflows changed sales by between nothing detectable and 16.3%, depending on how much the AI added to the firm's existing practice; the four workflows with gains imply about $5 a year per consumer.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	B — Firm and executive outcomes	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026	source_ids: SRC-SL-FANG-YUAN-ZHANG-DONATI-SARVARY-ARXIV-2510-12049-V6
7	SOURCE TYPE	Preprint	classes: PREPRINT
8	INSTITUTIONAL POSITION	Mixed	—
9	SCOPE	One unnamed cross-border online retail platform; September 2023 to June 2024	—
10	DESIGN	Randomized field experiments (A/B tests) run by the firm	—
11	SAMPLE / DATASET	Millions of users and products across seven workflows	—
12	MODEL / TOOL / VERSION	Generative AI built into firm workflows (chatbot, search, product descriptions, messages, ad titles, chargeback defence, translation)	—
13	COMPARATOR	Users and products under the firm's existing practice	—
14	MAIN RESULT	Sales effects from no detectable impact (advertising) to +16.3% (pre-sale chatbot); about $4.6–5.2 per consumer a year across the four positive workflows, assuming the gains add up; no worse return rates or ratings	—
15	INFERENTIAL TARGET	Firm-level sales effects of generative AI embedded in workflows	—
16	DOES NOT LICENSE	Effects on profits or productivity (labour and capital inputs are not counted); effects in other firms	—
17	VERIFICATION STATE	CONTENT VERIFIED PREPRINT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: PREPRINT · verified_by: UNKNOWN
18	LIMITATIONS	Workflows chosen by managerial judgement, not systematically; two authors were paid consultants to the platform and one was its employee; not peer reviewed	—
19	CONTRADICTORY / SUPPORTING SOURCES	Contrasts with the absence of task-composition change when tools are given to individuals	—
20	GEOGRAPHY / LANGUAGE	Global platform / English	—
21	DISTRIBUTIONAL LEDGER	Larger gains for less experienced consumers	—
22	R01 / FIELD-MAP LINKS	LD-03, D2	—
23	WHAT WOULD CHANGE THIS CLAIM?	Replication at other firms, or estimates net of the inputs used	—
24	PUBLICATION STATUS	ELIGIBLE WITH PREPRINT QUALIFIER	state: ELIGIBLE · qualifiers: PREPRINT
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-03 (LIVING_DEBATE); D2 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026” → Fang et al. (2026), “Generative AI and Sales…” (SRC-SL-FANG-YUAN-ZHANG-DONATI-SARVARY-ARXIV-2510-12049-V6--v1.0) — Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026; class PREPRINT (explicit public provenance pin)

Cited by Living Debate versions

Organizational absorption: Why micro gains do not become macro outcomes (v0.1) LD-03

Cite this page

SocioLens project. (2026, October 15). In randomized experiments run by a large cross-border online retail platform in 2023–2024, building generative AI into seven business workflows changed sales by between nothing detectable and 16.3%, depending on how much the AI added to the firm's existing practice; the four workflows with gains imply about $5 a year per consumer [Claim LD03-SL-C15, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld03-sl-c15/v1.0/

@misc{sociolens_ld03_sl_c15_v1_0,
  author = {{SocioLens project}},
  title = {In randomized experiments run by a large cross-border online retail platform in 2023–2024, building generative AI into seven business workflows changed sales by between nothing detectable and 16.3\%, depending on how much the AI added to the firm's existing practice; the four workflows with gains imply about \$5 a year per consumer.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld03-sl-c15/v1.0/}},
  note = {Claim LD03-SL-C15, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 2decdb49d7612f1719f0260beb7ef9ea46173f97052fa13663ebaf0ace1834d6.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD03-SL-C15 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts. Block “ENTRY 15 — LD03-SL-C15” SHA-256 79c2d7ba0809bb1ac041f1801a9fe63ac8747fcb60352ca78add1401b9743930; fixture fixtures/authored/claims/LD03-SL-C15--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD03-SL-C15 v1.0); the first version of a new Claim of the LD-03 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD03-SL-C15 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-03 debate; a new Claim, first version, written from the project's LD-03 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-FANG-YUAN-ZHANG-DONATI-SARVARY-ARXIV-2510-12049-V6--v1.0 — basis: field 6 of ENTRY 15 — LD03-SL-C15 cites "Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-FANG-YUAN-ZHANG-DONATI-SARVARY-ARXIV-2510-12049-V6--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Fang, Yuan, Zhang, Donati & Sarvary, “Generative AI and Sales Productivity: Field Experiments in Online Retail,” arXiv:2510.12049, v6, June 2026".