{"slug":"ai-adapt-fds-20261105","entry":{"slug":"ai-adapt-fds-20261105","title":"AI Adaptation Research Report — FDS (FACTSET RESEARCH SYSTEMS INC) · WhiteSwan constituent","symbol":"FDS","kind":"ai_adaptation","name":"FACTSET RESEARCH SYSTEMS INC","book":"AMF-WHITESWAN","direction":"long","verdict":"whiteswan","run_date":"2026-11-05","narrative":"deterministic","level":3,"aas":57.7,"date":"2026-11-05","generated_at":"2026-11-05T04:02:09+00:00","description":"FDS (FACTSET RESEARCH SYSTEMS INC) is a WhiteSwan constituent: its own filings document AI inside the product or operations with investment behind it, and its AI Adaptation Score ranks at the top of the software companie","algorithm_release":"1.1.0","rank":9,"composite":65.759,"story_score":50.05,"market":97.66,"weight":0.07355,"pinned":false},"url":"/marr/ai-adapt-fds-20261105","markdown":"# AI Adaptation Research Report — FDS (FACTSET RESEARCH SYSTEMS INC) · WhiteSwan constituent\n\n*EventTrader research desk · generated 2026-11-05 04:02 UTC · report ai-adapt-fds-20261105 · narrative: built from the evidence pack by the platform*\n\n| | |\n|---|---|\n| **Company** | FDS — FACTSET RESEARCH SYSTEMS INC · SIC 7370 Services-Computer Programming, Data Processing, Etc. |\n| **Book** | AMF-WHITESWAN (long) — WhiteSwan |\n| **Composition algorithm** | Swan Composite v1.1.0 — 50% AI Adaptation Score + 25% AI Story Score + 25% market verdict, conviction-weighted |\n| **Anchor filing** | 10-K filed 2026-10-20 (period 2026-08-31) |\n| **Adoption level** | 3 — integrated · model-confirmed |\n| **AI Adaptation Score** | 57.7 / 100 · run 2026-11-05 · sector software percentile 94.2 |\n| **Price** | 342.19 USD · stale quote |\n| **Place in the book** | rank 9 of 10 · composite 65.8 · story 50.0 · market verdict 97.7 · weight 7.4% |\n\n## 1. Verdict\n\n- **FDS (FACTSET RESEARCH SYSTEMS INC) is a WhiteSwan constituent**: its own filings document AI inside the product or operations with investment behind it, and its AI Adaptation Score ranks at the top of the software companies (SIC 7370–7379) on the 2026-11-05 run [S5].\n- The score rests on the company's 10-K filed 2026-10-20, read in full by the platform's scorer [S1].\n- Adoption level **3 — integrated** on the 0–4 rubric, confirmed by a model read with credibility 4.0/10 [S1].\n- AI Adaptation Score **57.7** / 100, percentile 99.1 of 6503 scored filers and 94.2 within its sector (software, 585 filers) [S5].\n- What would move it: a next report that withdraws the deployments, investment or outcomes it now states [S1].\n\n## 2. The company\n\nFACTSET RESEARCH SYSTEMS INC files with the SEC under CIK 1013237, SIC 7370 (Services-Computer Programming, Data Processing, Etc.), listed on NYSE [S4].\nThe filing the score reads is the 10-K filed 2026-10-20 for the period ended 2026-08-31, effective for the books from 2026-10-21 [S2].\n\n## 3. What the filing says about AI\n\nThe scorer found 66 AI-related sentences in the 10-K: 62 in the substantive sections (business, management's discussion) and 4 in the risk factors [S1].\nSignals counted in the substantive text: proprietary-model language 1, deployments 5, measured outcomes 0, investment 9, third-party vendor tools 0, exploratory language 1 [S1].\n\nVerbatim from the filing:\n\n> \"Our ability to develop, commercialize and scale AI-enabled products and services depends in significant part on our ability to obtain and maintain sufficient rights to use data, including our own proprietary data, data licensed from third-party suppliers, and data contributed by or relating to our clients, for the training, fine-tuning, grounding, retrieval and output-generation functions of AI models and applications.\" — Item 1. Business [S1]\n> \"The Hosted Platform is a subscription-based service that provides client access to various combinations of products and services including workstations, portfolio analytics, enterprise data solutions, AI agents, and agentic workflow solutions.\" — Item 1. Business [S1]\n> \"We offer clients comprehensive solutions with a broad set of products delivered through a desktop or mobile user interface, AI-enabled chat and agentic solutions, cloud-based platforms, standardized or bespoke data feeds, and APIs.\" — Item 1. Business [S1]\n> \"We also deliver our content in forms designed for machine consumption, including the metadata and symbology that allow AI agents and other automated systems to interpret and act on it.\" — Item 1. Business [S1]\n> \"Products and Solutions We deliver trusted, connected financial data, software applications, advanced analytics, and AI-powered solutions embedded in the critical workflows that finance professionals rely on every day.\" — Item 1. Business [S1]\n> \"Even where our AI-enabled products and services are adopted, we may be unable to monetize them profitably.\" — Item 1. Business [S1]\n> \"These threats take many forms, including the use of generative and agentic AI, phishing, hacking, viruses, denial of service attacks, tampering, intrusions, physical break-ins, ransomware and malware.\" — Item 1. Business [S1]\n> \"Cyber-attacks continue to evolve in sophistication, frequency and volume, including as threat actors exploit generative and agentic AI to discover and exploit vulnerabilities at unprecedented speed and scale, and may be difficult to detect for long periods of time.\" — Item 1. Business [S1]\n> \"To remain competitive, we must invest, adapt and migrate to new and emerging technologies, applications and processes, including the evolving use of AI technology and agentic AI (see \"Our use of AI technologies may not be successful and may present business, compliance, and reputational risks\" below for further discussion).\" — Item 1. Business [S1]\n> \"If our competitors or other third parties incorporate AI technologies, such as emerging generative and agentic AI, into their products and processes more quickly or more successfully than us, this could impair our ability to compete effectively.\" — Item 1. Business [S1]\n\n## 4. The AI Adaptation Score\n\nThe AI Adaptation Score weighs the adoption level (0–4) at 45%, building with AI from the ground up (greenfield) at 20%, the credibility of the claims at 15%, the trajectory against the previous filing at 12% and the investment signal at 8%, with trajectory and investment measured against the company's own sector; the greenfield reward counts only from level 3 upward [S5].\nFDS's score of 57.7 decomposes into level 33.8, greenfield 4.0, credibility 6.0, trajectory 5.9 and investment 8.0 points [S5].\nIts cluster label for the run is **builder** with a margin of 26.8 points from the sector's other cluster [S5].\n\nThe model read of the filing returned level 3 (integrated) with confidence 0.74 and sub-scores product 7.0, operations 5.0, investment 6.0, data moat 8.0, credibility 4.0, greenfield 2.0 (each 0–10) [S1].\nIts summary: \"FactSet embeds AI across its products and operations, building agentic workstation and workflow solutions atop a proprietary, normalized financial data catalog (a real data moat), with AI R&D and rising model/inference/token spend. Investment and data asset are credible, but outcomes lack hard numbers. AI is retrofitted onto an existing data business, not greenfield. Integrated (level 3).\" [S1].\n> \"FactSet is a global data and artificial intelligence (\"AI\") solutions provider to the financial markets with open and flexible technologies that deliver financial intelligence to finance professionals worldwide.\" — Item 1. Business [S1]\n> \"AI is embedded throughout these offerings to enhance data discovery, automate routine workflows and improve the speed and accuracy of client insights.\" — Item 1. Business [S1]\n> \"The cost of developing, delivering and operating AI capabilities — including model licensing, inference and compute costs and the consumption of AI tokens — can be significant and variable, and has increased as we expand our use of AI.\" — Item 1. Business [S1]\n> \"We aim to be a single, authoritative source by collecting, mapping, normalizing, and structuring data into formats that both professionals and AI systems can rely on.\" — Item 1. Business [S1]\n\n## 5. Trajectory\n\nThe previous periodic report, the 10-Q filed 2026-07-01, scored level 3 (integrated) with 8 AI sentences [S3].\nThe level is unchanged between the two reports [S1].\n\n## 6. Recent developments\n\nNo wire headline tagged FDS in the last 45 days reached the platform's headline store [S5].\n\n## 7. What would change the verdict\n\nFDS leaves the long book if a later report drops the deployments, investment or outcomes it now states, if the model read judges the claims uncredible, or if higher-scoring filers displace it on a later run [S5].\nThe book is recomposed on every scoring run from the ten highest scores that pass the liquidity screen [S5].\n\n## 8. Risks and caveats\n\nThe score reads what the company discloses, not what it does: a company can adopt AI without describing it in a periodic report, and can describe it without adopting it [S5].\nRisk-factor language is counted separately from substantive text and never credited as adoption; boilerplate that resembles generic risk disclosure is excluded [S5].\nOnly the sections the scorer parses are read (business, risk factors, management's discussion for a 10-K or 10-Q; the earnings release of an 8-K is scored but never anchors a level) [S5].\n\n## 9. Watch list\n\n- The next periodic report of FDS (expected around 2027-10-20) and its AI sentences [S4].\n- The model read of the current filing (done) [S5].\n- The next scoring run's AI Adaptation Score and the book's recomposition [S5].\n## Place in the book\n\n- Rank 9 of 10 in AMF-WHITESWAN (Swan Composite v1.1.0, scoring run 2026-11-05): the book holds the highest composites [S5].\n- Composite: 0.50 × 57.7 (AAS) + 0.25 × 50.0 (story) + 0.25 × 97.7 (market) = 65.8 [S5].\n- Market verdict 97.7: over the last 90 days FDS rose more and fell less than the software universe's median daily return (down-capture 1.40, up-capture 3.31, L = 1.91; 32 down / 19 up days) — the Failure-To-Adapt fund's selection signal, 50 = moves with the software universe's median daily return [S5].\n- AI Story Score 50.0: no AI story or a neutral one in the AI stories written about the company [S5].\n- Weight: rank factor 1.11 (rank 9 of 10) × story factor 1.00 × market factor 1.48, normalised across the book and clamped to 5–20% → 7.4% [S5].\n\n## Sources\n\n- **S1** — FDS 10-K filed 2026-10-20 — SEC primary document (the anchor filing the score reads) — \n- **S2** — EDGAR filing index for accession 0001628280-26-066391 — \n- **S3** — FDS 10-Q filed 2026-07-01 — the previous periodic report (trajectory) — \n- **S4** — SEC EDGAR company submissions — FACTSET RESEARCH SYSTEMS INC (CIK 1013237) — \n- **S5** — EventTrader AI Adaptation Score, scoring run 2026-11-05 — the AMF-WHITESWAN fund page (method, composition) — \n- **S6** — EventTrader real-price feed — FDS quote — \n\n## Method and disclaimer\n\nThe AI Adaptation Score reads each company's own periodic SEC reports (10-K, 10-Q, 20-F, 40-F): deterministic features (AI sentences by section, proprietary-model, deployment, outcome, investment and vendor language, boilerplate and negation excluded) set an adoption level on a 0–4 rubric; a model read of the filing's AI sentences confirms the level and scores credibility and greenfield (the business built with AI from the ground up rather than retrofitted); the score combines level, greenfield, credibility, trajectory against the previous report and investment, measured against the company's sector, with greenfield carrying the highest reward. The books are composed by Swan Composite v1.1.0: each company's composite is 50% AI Adaptation Score + 25% AI Story Score + 25% market verdict — the AI Story Score is the sentiment of the AI stories written about it, the belief network of companies named in the same stories and the price reaction to those stories (no story scores neutral); the market verdict is the Failure-To-Adapt fund's selection signal, the signed down/up capture ratio of the stock against the software universe's median return over 90 days (a stock that falls more and rises less than its peers scores below 50). AMF-BLACKSWAN holds the ten lowest composites that pass the liquidity screen and the new-listing gate and AMF-WHITESWAN the ten highest, weighted by conviction (rank, story agreement and market agreement, each leg 5–20%), recomposed on every scoring run. Every quote in this report is the filing's text verbatim; every factual sentence names its source; a source the platform could not reach is reported as missing, never filled in.\n\n*This report is information, not investment advice. Figures come from the cited sources as of their dates. Nothing here is a recommendation to buy, sell or hold any asset.*\n","html":"

AI Adaptation Research Report — FDS (FACTSET RESEARCH SYSTEMS INC) · WhiteSwan constituent

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EventTrader research desk · generated 2026-11-05 04:02 UTC · report ai-adapt-fds-20261105 · narrative: built from the evidence pack by the platform

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CompanyFDS — FACTSET RESEARCH SYSTEMS INC · SIC 7370 Services-Computer Programming, Data Processing, Etc.
BookAMF-WHITESWAN (long) — WhiteSwan
Composition algorithmSwan Composite v1.1.0 — 50% AI Adaptation Score + 25% AI Story Score + 25% market verdict, conviction-weighted
Anchor filing10-K filed 2026-10-20 (period 2026-08-31)
Adoption level3 — integrated · model-confirmed
AI Adaptation Score57.7 / 100 · run 2026-11-05 · sector software percentile 94.2
Price342.19 USD · stale quote
Place in the bookrank 9 of 10 · composite 65.8 · story 50.0 · market verdict 97.7 · weight 7.4%
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1. Verdict

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    \n
  • FDS (FACTSET RESEARCH SYSTEMS INC) is a WhiteSwan constituent: its own filings document AI inside the product or operations with investment behind it, and its AI Adaptation Score ranks at the top of the software companies (SIC 7370–7379) on the 2026-11-05 run [S5].
    • \n
  • The score rests on the company's 10-K filed 2026-10-20, read in full by the platform's scorer [S1].
    • \n
  • Adoption level 3 — integrated on the 0–4 rubric, confirmed by a model read with credibility 4.0/10 [S1].
    • \n
  • AI Adaptation Score 57.7 / 100, percentile 99.1 of 6503 scored filers and 94.2 within its sector (software, 585 filers) [S5].
    • \n
  • What would move it: a next report that withdraws the deployments, investment or outcomes it now states [S1].
    • \n
\n

2. The company

\n

FACTSET RESEARCH SYSTEMS INC files with the SEC under CIK 1013237, SIC 7370 (Services-Computer Programming, Data Processing, Etc.), listed on NYSE [S4].\nThe filing the score reads is the 10-K filed 2026-10-20 for the period ended 2026-08-31, effective for the books from 2026-10-21 [S2].

\n

3. What the filing says about AI

\n

The scorer found 66 AI-related sentences in the 10-K: 62 in the substantive sections (business, management's discussion) and 4 in the risk factors [S1].\nSignals counted in the substantive text: proprietary-model language 1, deployments 5, measured outcomes 0, investment 9, third-party vendor tools 0, exploratory language 1 [S1].

\n

Verbatim from the filing:

\n
\n

\"Our ability to develop, commercialize and scale AI-enabled products and services depends in significant part on our ability to obtain and maintain sufficient rights to use data, including our own proprietary data, data licensed from third-party suppliers, and data contributed by or relating to our clients, for the training, fine-tuning, grounding, retrieval and output-generation functions of AI models and applications.\" — Item 1. Business [S1]\n\"The Hosted Platform is a subscription-based service that provides client access to various combinations of products and services including workstations, portfolio analytics, enterprise data solutions, AI agents, and agentic workflow solutions.\" — Item 1. Business [S1]\n\"We offer clients comprehensive solutions with a broad set of products delivered through a desktop or mobile user interface, AI-enabled chat and agentic solutions, cloud-based platforms, standardized or bespoke data feeds, and APIs.\" — Item 1. Business [S1]\n\"We also deliver our content in forms designed for machine consumption, including the metadata and symbology that allow AI agents and other automated systems to interpret and act on it.\" — Item 1. Business [S1]\n\"Products and Solutions We deliver trusted, connected financial data, software applications, advanced analytics, and AI-powered solutions embedded in the critical workflows that finance professionals rely on every day.\" — Item 1. Business [S1]\n\"Even where our AI-enabled products and services are adopted, we may be unable to monetize them profitably.\" — Item 1. Business [S1]\n\"These threats take many forms, including the use of generative and agentic AI, phishing, hacking, viruses, denial of service attacks, tampering, intrusions, physical break-ins, ransomware and malware.\" — Item 1. Business [S1]\n\"Cyber-attacks continue to evolve in sophistication, frequency and volume, including as threat actors exploit generative and agentic AI to discover and exploit vulnerabilities at unprecedented speed and scale, and may be difficult to detect for long periods of time.\" — Item 1. Business [S1]\n\"To remain competitive, we must invest, adapt and migrate to new and emerging technologies, applications and processes, including the evolving use of AI technology and agentic AI (see \"Our use of AI technologies may not be successful and may present business, compliance, and reputational risks\" below for further discussion).\" — Item 1. Business [S1]\n\"If our competitors or other third parties incorporate AI technologies, such as emerging generative and agentic AI, into their products and processes more quickly or more successfully than us, this could impair our ability to compete effectively.\" — Item 1. Business [S1]

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4. The AI Adaptation Score

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The AI Adaptation Score weighs the adoption level (0–4) at 45%, building with AI from the ground up (greenfield) at 20%, the credibility of the claims at 15%, the trajectory against the previous filing at 12% and the investment signal at 8%, with trajectory and investment measured against the company's own sector; the greenfield reward counts only from level 3 upward [S5].\nFDS's score of 57.7 decomposes into level 33.8, greenfield 4.0, credibility 6.0, trajectory 5.9 and investment 8.0 points [S5].\nIts cluster label for the run is builder with a margin of 26.8 points from the sector's other cluster [S5].

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The model read of the filing returned level 3 (integrated) with confidence 0.74 and sub-scores product 7.0, operations 5.0, investment 6.0, data moat 8.0, credibility 4.0, greenfield 2.0 (each 0–10) [S1].\nIts summary: \"FactSet embeds AI across its products and operations, building agentic workstation and workflow solutions atop a proprietary, normalized financial data catalog (a real data moat), with AI R&D and rising model/inference/token spend. Investment and data asset are credible, but outcomes lack hard numbers. AI is retrofitted onto an existing data business, not greenfield. Integrated (level 3).\" [S1].

\n
\n

\"FactSet is a global data and artificial intelligence (\"AI\") solutions provider to the financial markets with open and flexible technologies that deliver financial intelligence to finance professionals worldwide.\" — Item 1. Business [S1]\n\"AI is embedded throughout these offerings to enhance data discovery, automate routine workflows and improve the speed and accuracy of client insights.\" — Item 1. Business [S1]\n\"The cost of developing, delivering and operating AI capabilities — including model licensing, inference and compute costs and the consumption of AI tokens — can be significant and variable, and has increased as we expand our use of AI.\" — Item 1. Business [S1]\n\"We aim to be a single, authoritative source by collecting, mapping, normalizing, and structuring data into formats that both professionals and AI systems can rely on.\" — Item 1. Business [S1]

\n
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5. Trajectory

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The previous periodic report, the 10-Q filed 2026-07-01, scored level 3 (integrated) with 8 AI sentences [S3].\nThe level is unchanged between the two reports [S1].

\n

6. Recent developments

\n

No wire headline tagged FDS in the last 45 days reached the platform's headline store [S5].

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7. What would change the verdict

\n

FDS leaves the long book if a later report drops the deployments, investment or outcomes it now states, if the model read judges the claims uncredible, or if higher-scoring filers displace it on a later run [S5].\nThe book is recomposed on every scoring run from the ten highest scores that pass the liquidity screen [S5].

\n

8. Risks and caveats

\n

The score reads what the company discloses, not what it does: a company can adopt AI without describing it in a periodic report, and can describe it without adopting it [S5].\nRisk-factor language is counted separately from substantive text and never credited as adoption; boilerplate that resembles generic risk disclosure is excluded [S5].\nOnly the sections the scorer parses are read (business, risk factors, management's discussion for a 10-K or 10-Q; the earnings release of an 8-K is scored but never anchors a level) [S5].

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9. Watch list

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    \n
  • The next periodic report of FDS (expected around 2027-10-20) and its AI sentences [S4].
    • \n
  • The model read of the current filing (done) [S5].
    • \n
  • The next scoring run's AI Adaptation Score and the book's recomposition [S5].
    • \n
\n

Place in the book

\n
    \n
  • Rank 9 of 10 in AMF-WHITESWAN (Swan Composite v1.1.0, scoring run 2026-11-05): the book holds the highest composites [S5].
    • \n
  • Composite: 0.50 × 57.7 (AAS) + 0.25 × 50.0 (story) + 0.25 × 97.7 (market) = 65.8 [S5].
    • \n
  • Market verdict 97.7: over the last 90 days FDS rose more and fell less than the software universe's median daily return (down-capture 1.40, up-capture 3.31, L = 1.91; 32 down / 19 up days) — the Failure-To-Adapt fund's selection signal, 50 = moves with the software universe's median daily return [S5].
    • \n
  • AI Story Score 50.0: no AI story or a neutral one in the AI stories written about the company [S5].
    • \n
  • Weight: rank factor 1.11 (rank 9 of 10) × story factor 1.00 × market factor 1.48, normalised across the book and clamped to 5–20% → 7.4% [S5].
    • \n
\n

Sources

\n\n

Method and disclaimer

\n

The AI Adaptation Score reads each company's own periodic SEC reports (10-K, 10-Q, 20-F, 40-F): deterministic features (AI sentences by section, proprietary-model, deployment, outcome, investment and vendor language, boilerplate and negation excluded) set an adoption level on a 0–4 rubric; a model read of the filing's AI sentences confirms the level and scores credibility and greenfield (the business built with AI from the ground up rather than retrofitted); the score combines level, greenfield, credibility, trajectory against the previous report and investment, measured against the company's sector, with greenfield carrying the highest reward. The books are composed by Swan Composite v1.1.0: each company's composite is 50% AI Adaptation Score + 25% AI Story Score + 25% market verdict — the AI Story Score is the sentiment of the AI stories written about it, the belief network of companies named in the same stories and the price reaction to those stories (no story scores neutral); the market verdict is the Failure-To-Adapt fund's selection signal, the signed down/up capture ratio of the stock against the software universe's median return over 90 days (a stock that falls more and rises less than its peers scores below 50). AMF-BLACKSWAN holds the ten lowest composites that pass the liquidity screen and the new-listing gate and AMF-WHITESWAN the ten highest, weighted by conviction (rank, story agreement and market agreement, each leg 5–20%), recomposed on every scoring run. Every quote in this report is the filing's text verbatim; every factual sentence names its source; a source the platform could not reach is reported as missing, never filled in.

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This report is information, not investment advice. Figures come from the cited sources as of their dates. Nothing here is a recommendation to buy, sell or hold any asset.

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