AI Layoffs Register
Jana Boháčiková
2026-10-22
Analýza prepúšťania v súvislosti s umelou inteligenciou
udaje <- read.csv("ai-layoffs-register.csv")
head(udaje, 10)## Company Date
## 1 Bank of America 2026-10-14
## 2 KLM 2026-10-12
## 3 Kroger 2026-10-12
## 4 One Zero 2026-10-11
## 5 HoneyBook 2026-10-08
## 6 HSBC 2026-10-07
## 7 DNB 2026-10-06
## 8 FICO (Fair Isaac) 2026-10-06
## 9 HubSpot 2026-10-06
## 10 CWT Italia (Carlson Wagonlit Travel) 2026-10-05
## Sector
## 1 Banking
## 2 Airline
## 3 Grocery retail
## 4 Digital banking
## 5 Small-business management software
## 6 Banking (wealth management)
## 7 Banking
## 8 Analytics software and credit scoring
## 9 Software (CRM and marketing platform)
## 10 Business travel management
## Country
## 1 United States
## 2 Netherlands
## 3 United States
## 4 Israel
## 5 Israel (company headquartered in the US)
## 6 United Kingdom
## 7 Norway
## 8 United States
## 9 United States
## 10 Italy
## Functions
## 1 Managers (down about 5% year over year, per CEO Brian Moynihan, while client-facing staff rose)
## 2 Written customer service (handling Dutch-language questions and complaints), per ANP and De Telegraaf
## 3 Corporate roles across many functions (the memo does not name them)
## 4 Not specified
## 5 Not specified
## 6 Financial advisers in HSBC UK's wealth business, a reduction of close to 70% (per the Financial Times, as relayed by AFP, PA and Sharecast); Management and specialist roles in the UK wealth business, about half of them (per the Financial Times, as relayed by AFP, PA and Sharecast)
## 7 Software developers in the Technology & Services unit (per kode24, after speaking to Tekna union representative Geir Jarle Ness); Know-your-customer (KYC, 'customer control') staff (per kode24 and FinansFokus); Other Technology & Services roles, mainly in Oslo and Bergen, the rest in Riga (per FinansFokus), not broken down (DNB declined to tell kode24 how the cut splits between developers and other roles)
## 8 Management layers (8-K: 'reducing the number of layers in the organization', paraphrased by HousingWire as 'reduce management layers')
## 9 Management layers (the CEO's memo: 'We will reduce management layers')
## 10 Not specified
## Roles.counted Roles.reported..not.counted.
## 1 NA NA
## 2 NA 126
## 3 NA 3000
## 4 NA 25
## 5 NA 30
## 6 NA NA
## 7 400 NA
## 8 581 NA
## 9 660 NA
## 10 NA 190
## Why.not.counted
## 1
## 2 The company has not named AI as the cause; the number was reported by the press, not stated publicly by the company; the reduction comes through attrition, not layoffs; the number includes roles moved to other teams or locations, not only roles cut.
## 3 The company has not named AI as the cause.
## 4 The number was reported by the press, not stated publicly by the company.
## 5 The number was reported by the press, not stated publicly by the company.
## 6
## 7
## 8
## 9
## 10 The company has not named AI as the cause; the number was given by a union, not stated publicly by the company.
## X..of.workforce
## 1 about 1.5% of headcount year over year (CEO Brian Moynihan's figure)
## 2
## 3 about 37% of roughly 8,200 corporate employees (Reuters, Bloomberg)
## 4 about 7% (about 25 of about 350, per Calcalist)
## 5 about 14% (of 255, per Calcalist)
## 6 No absolute figure and no base on record: about half of management and specialist roles and close to 70% of financial advisers in the UK wealth business, per the FT's anonymous sources; HSBC does not disclose its UK wealth headcount
## 7 about 4% of the group (derived: 400 of 10,829 FTEs at 31 December 2025, DNB Annual Report 2025; E24 printed 10,603, the end-2024 figure); about a fifth of Technology & Services, which DNB told E24 has 2,000 employees (the Tekna union representative puts it at 2,400, so one in six)
## 8 approximately 15% (8-K) of the 3,876 employees at June 30, 2026 (10-Q); 581 derived
## 9 about 7%
## 10 half the workforce, per TravelQuotidiano (no source given)
## Evidence.tier Attribution Execution Counts.in.US.index
## 1 Tier 1 mixed executed no
## 2 Tier 2 blamed announced no
## 3 Tier 2 blamed announced no
## 4 Tier 2 mixed announced no
## 5 Tier 2 mixed announced no
## 6 Tier 2 blamed announced no
## 7 Tier 1 mixed announced no
## 8 Tier 1 mixed partial no
## 9 Tier 1 mixed partial no
## 10 Tier 3 blamed announced no
## Claim
## 1 CFO Alastair Borthwick, prepared remarks, Q3 2026 earnings call (October 14, 2026): "We also invested in technology modernization, AI, client acquisition, marketing, and franchise growth. At the same time, productivity continues to improve, as evidenced by our headcount. Even as we add headcount in our sales forces, our headcount was down nearly 3,500 from a year ago as we automate processes and simplify work across the company. This means we don't need to hire quite as many people to refill roles." CEO Brian Moynihan, same call, answering a question on AI deployment: "If you look year-over-year, the total expense base was up $1.2 billion and the headcount was down 3,000 round numbers." "How do we maintain that kind of 1%-2% expense growth with all the investments we are making. We are open five branches in Milwaukee this month and things like that, more headcount front client facing. That is by engineering both through engineering process, through AI application, et cetera. You see it already in the numbers, and we expect to continue going forward" "AI has helped us." "We are going to continue to invest in driving the growth in the franchise, and AI is a major part of that. Say, $300 million going to $700 million, headcount down by 3,000, 1.5% year-over-year. Managers down by about 5% in that, and production teammates that are doing the client-facing work are actually up year-over-year."
## 2 De Telegraaf (October 12, 2026, summary of its subscriber article): "De schriftelijke klantenservice van KLM wordt volledig verplaatst naar de Filipijnse hoofdstad Manilla. Uit interne documenten die De Telegraaf heeft ingezien, blijkt dat hierdoor zo’n 150 medewerkers hun huidige baan verliezen. Hun werk wordt overgenomen door Filipijnse medewerkers die Nederlandse klantvragen met behulp van een AI-vertaaltool gaan afhandelen." (Translation: KLM's written customer service is being moved entirely to the Philippine capital Manila. Internal documents seen by De Telegraaf show that about 150 employees will lose their current job as a result. Their work will be taken over by Philippine staff who will handle Dutch customer queries with the help of an AI translation tool.) A KLM spokeswoman, confirming the move (ANP, October 13, 2026): "Dit klantcontactcenter handelt al vele jaren vragen en klachten af voor KLM-klanten over de hele wereld. De kwaliteit van de afhandeling van vragen en klachten staat voor KLM voorop en zal voor Nederlandstalige klanten in het Nederlands blijven verlopen." (Translation: This customer contact centre has handled questions and complaints for KLM customers around the world for many years. For KLM the quality of handling questions and complaints comes first, and for Dutch-speaking customers it will continue to be in Dutch.) ANP adds: "Het verplaatsen gaat zoveel mogelijk via natuurlijk verloop. Het gaat om de werkzaamheden van 126 voltijdsbanen." (Translation: The move will happen as far as possible through natural attrition. It concerns the work of 126 full-time jobs.)
## 3 Reuters (2026-10-12): "Kroger plans to cut roughly 3,000 corporate roles over the next three years, CEO Greg Foran told staff in a memo, seen by Reuters on Monday, as the grocer integrates AI into its workflow." Foran's memo (full text published by Business Insider, which says Kroger shared it) gives as reasons: "Our company has more layers, more handoffs and more overhead than we need, so we are addressing this issue." and "Technology is reshaping what's possible. The way work gets done is changing across every industry; we must adapt and take advantage of technology. We need to adopt capabilities, reduce duplicative work and look closely at where work is best fit to be done." The memo does not mention AI.
## 4 One Zero CEO Eyal Gafni, letter to staff as quoted by Globes (2026-10-11): "ההתפתחות המהירה של GenAI (בינה מלאכותית יוצרת, א' ג') משנה כבר היום את הדרך שבה ארגונים עובדים, מפתחים מוצרים ומשרתים לקוחות" (Translation: "The rapid development of GenAI is already changing the way organizations work, develop products and serve customers."). The bank, quoted by CTech by Calcalist (2026-10-11), said adjustments were being made "wherever technology helps us accelerate processes," and Gafni wrote that the bank must remain "a fast, agile, and efficient organization at all times, enabling us to continue growing without expanding our workforce or cost base." CTech reports the bank's position: "The bank has not said that the employees being laid off are being directly replaced by AI. Rather, it says that the expanded use of automation and generative AI tools allows it to operate with a leaner organizational structure."
## 5 HoneyBook, in a statement quoted by Calcalist (CTech, 2026-10-08): "HoneyBook is undergoing a reorganization process aimed at aligning our structure and workflows with the AI era, while integrating AI into our product and value proposition for customers. This move is driven by financial strength and strategic focus, with the goal of becoming a faster, more agile organization."
## 6 AFP (7 October 2026): "HSBC said Wednesday consultations were underway, after the Financial Times reported that hundreds of jobs were at risk from the bank's adoption of artificial intelligence." The FT reported, as quoted by AFP, that HSBC "is planning sweeping job cuts in its UK wealth management business ... as part of a push to use AI to help serve wealthy clients". HSBC's own statement names no AI: it is "continuing to evolve to deliver more digitally-enabled products and journeys, to support our best-in-class wealth service and meet the changing needs of our customers".
## 7 DNB's stock exchange notice (6 October 2026): "In order to deliver better customer experiences, increase efficiency and meet customers' future needs, DNB is carrying out organisational changes and downsizing as part of a comprehensive restructuring of Technology & Services (T&S). During the past few years, DNB has simplified a number of internal processes and routines. At the same time, the Group has adopted agentic AI in several parts of its operations. This technology replaces tasks that were previously handled manually and contributes to faster processes and increased capacity, and it simplifies life for our customers. Control of customer data and the work relating to the Know Your Customer (KYC) process, as well as technology development and coding, are areas where the bank is already seeing that AI agents can contribute substantial efficiency gains. To achieve these gains and exploit the opportunities associated with new technology and new work processes, DNB will adapt the organisation of T&S and its skills mix. The changes entail a reduction in the workforce of about 400 full-time equivalents (FTEs)." Group CEO Kjerstin Braathen, in the same notice: "AI is changing the way we work and how we deliver services to our customers. We are already seeing considerable gains, and are therefore adapting our organisation to a new reality."
## 8 Fair Isaac's Form 8-K, Item 2.05 (filed October 6, 2026): "On October 1, 2026, management of Fair Isaac Corporation (the “Company”) committed to a plan of workforce reduction by reducing the number of layers in the organization, simplifying the operating structure, optimizing processes and tools, and integrating AI-driven product development. This plan involves the elimination of approximately 15% of positions across the Company."
## 9 CEO Yamini Rangan's memo to employees, furnished as Exhibit 99.1 to HubSpot's Form 8-K (October 6, 2026), under 'Why are we taking this step': "Over the past year, we have shifted our strategy from building software that helps customers grow to delivering outcomes for them with AI. That shift is transforming product, pricing and how we serve our customers. But we also need to fundamentally change the way we are organized to compete and win." Under 'What is this not about', the same memo says: "This is not driven by AI-related efficiencies."
## 10 Unions Filcams CGIL, Fisascat CISL and Uiltucs (joint note, October 5, 2026): the 190 CWT Italia dismissals follow changes of ownership and “un significativo investimento nell'applicazione dell'intelligenza artificiale al business travel” (“a significant investment in applying artificial intelligence to business travel”) by its new owner, the US fund Long Lake Management (the unions' description). No statement from CWT was found.
## Source
## 1 The Motley Fool: Bank of America (BAC) Q3 2026 Earnings Call Transcript
## 2 ANP via Transport Online: KLM verplaatst deel schriftelijke klantenservice naar Filipijnen
## 3 Reuters via AOL: Kroger plans to eliminate 3,000 corporate roles, memo shows, as it expands AI use
## 4 CTech by Calcalist: One Zero lays off 25 employees in latest AI-driven efficiency push
## 5 CTech by Calcalist: HoneyBook lays off 14% of workforce in AI-driven restructuring
## 6 AFP: HSBC 'consults' over UK unit job cuts amid AI adoption
## 7 DNB Bank ASA: DNB is making organisational changes in Technology & Services (stock exchange notice, inside information)
## 8 Fair Isaac Corporation: Form 8-K, Item 2.05 (October 6, 2026)
## 9 HubSpot: Form 8-K, Exhibit 99.1, update to employees from CEO Yamini Rangan (October 6, 2026)
## 10 Fisascat CISL: CWT Italia, licenziamento per 190 lavoratrici e lavoratori
## Source.URL
## 1 https://www.fool.com/earnings/call-transcripts/2026/10/14/bofa-bac-q3-2026-earnings-call-transcript/
## 2 https://www.transport-online.nl/141605/klm-verplaatst-deel-schriftelijke-klantenservice-naar-filipijnen/
## 3 https://www.aol.com/articles/kroger-plans-cut-third-corporate-204543000.html
## 4 https://www.calcalistech.com/ctechnews/article/0ggruyjlr
## 5 https://www.calcalistech.com/ctechnews/article/syg9pbhizx
## 6 https://www.afp.com/en/hsbc-consults-over-uk-unit-job-cuts-amid-ai-adoption
## 7 https://ir.dnb.no/press-and-reports/press-releases/dnb-making-organisational-changes-technology-services-2485833
## 8 https://www.sec.gov/Archives/edgar/data/814547/000119312526415732/d127042d8k.htm
## 9 https://www.sec.gov/Archives/edgar/data/1404655/000119312526414965/d183135dex991.htm
## 10 https://fisascat.it/news/cwt-italia
## Event.ID Kind
## 1 bank-of-america-2026-10 workforce statement
## 2 klm-2026-10 cut
## 3 kroger-2026-10 cut
## 4 one-zero-2026-10 cut
## 5 honeybook-2026-10 cut
## 6 hsbc-2026-10 cut
## 7 dnb-2026-10 cut
## 8 fico-2026-10 cut
## 9 hubspot-2026-10 cut
## 10 cwt-italia-2026-10 cut
## What.the.employer.said
## 1 Spoke about AI and headcount; no cut announced
## 2 Named no AI reason; others linked it to AI
## 3 Named no AI reason; others linked it to AI
## 4 Cited AI among other reasons
## 5 Cited AI among other reasons
## 6 Named no AI reason; others linked it to AI
## 7 Cited AI among other reasons
## 8 Cited AI among other reasons
## 9 Cited AI among other reasons
## 10 Named no AI reason; others linked it to AI
## Job.families
## 1 Management layers
## 2 Customer service and support
## 3 Corporate functions
## 4 Not specified
## 5 Not specified
## 6 Consulting and professional services; Management layers
## 7 Software engineering and IT; Operations and back office
## 8 Management layers
## 9 Management layers
## 10 Not specifiedDatabáza obsahuje 20 premenných. Medzi najdôležitejšie patria názov spoločnosti (Company), dátum udalosti (Date), sektor (Sector), krajina (Country) a počet dotknutých pracovných miest (Roles.counted).
Názvy premenných
colnames(udaje)## [1] "Company" "Date"
## [3] "Sector" "Country"
## [5] "Functions" "Roles.counted"
## [7] "Roles.reported..not.counted." "Why.not.counted"
## [9] "X..of.workforce" "Evidence.tier"
## [11] "Attribution" "Execution"
## [13] "Counts.in.US.index" "Claim"
## [15] "Source" "Source.URL"
## [17] "Event.ID" "Kind"
## [19] "What.the.employer.said" "Job.families"Rozmery databázy
Databáza obsahuje 300 pozorovaní a 20 premenných. Premenné poskytujú informácie o spoločnostiach, krajinách, sektoroch a počte pracovných miest ovplyvnených využívaním umelej inteligencie.
dim(udaje)## [1] 300 20Základný prehľad
summary(udaje)## Company Date Sector Country
## Length :300 Length :300 Length :300 Length :300
## N.unique :249 N.unique :225 N.unique :223 N.unique : 48
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 3 Min.nchar: 10 Min.nchar: 5 Min.nchar: 5
## Max.nchar: 39 Max.nchar: 10 Max.nchar: 68 Max.nchar: 87
##
##
## Functions Roles.counted Roles.reported..not.counted. Why.not.counted
## Length :300 Min. : 3 Min. : 4.00 Length :300
## N.unique :255 1st Qu.: 150 1st Qu.: 89.75 N.unique : 32
## N.blank : 0 Median : 307 Median : 250.00 N.blank :144
## Min.nchar: 10 Mean : 857 Mean : 1721.86 Min.nchar: 0
## Max.nchar:524 3rd Qu.: 800 3rd Qu.: 1102.50 Max.nchar:259
## Max. :8000 Max. :21000.00
## NAs :208 NAs :144
## X..of.workforce Evidence.tier Attribution Execution
## Length :300 Length :300 Length :300 Length :300
## N.unique :198 N.unique : 3 N.unique : 3 N.unique : 5
## N.blank : 92 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 0 Min.nchar: 6 Min.nchar: 5 Min.nchar: 7
## Max.nchar:281 Max.nchar: 6 Max.nchar: 8 Max.nchar: 9
##
##
## Counts.in.US.index Claim Source Source.URL
## Length :300 Length : 300 Length :300 Length :300
## N.unique : 2 N.unique : 300 N.unique :291 N.unique :299
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 2 Min.nchar: 168 Min.nchar: 4 Min.nchar: 35
## Max.nchar: 3 Max.nchar:1617 Max.nchar:210 Max.nchar:235
##
##
## Event.ID Kind What.the.employer.said Job.families
## Length :300 Length :300 Length :300 Length :300
## N.unique :300 N.unique : 2 N.unique : 7 N.unique : 68
## N.blank : 0 N.blank : 0 N.blank : 0 N.blank : 0
## Min.nchar: 10 Min.nchar: 3 Min.nchar: 27 Min.nchar: 13
## Max.nchar: 37 Max.nchar: 19 Max.nchar: 51 Max.nchar:127
##
## Pomocou funkcie summary() sme získali základný prehľad o jednotlivých premenných databázy. Vidíme, že databáza obsahuje textové aj číselné premenné, ktoré budú ďalej využité pri analýze a tvorbe grafov.
Databáza obsahuje údaje o 249 spoločnostiach z 48 krajín. Priemerný počet dotknutých pracovných miest predstavuje približne 857, pričom najvyššia zaznamenaná hodnota dosahuje 8000 pracovných miest.
Analýza krajín
V tejto časti skúmam, v ktorých krajinách sa udalosti prepúšťania vyskytujú najčastejšie.
sort(table(udaje$Country), decreasing = TRUE)##
## United States
## 137
## Israel
## 25
## India
## 15
## United Kingdom
## 15
## Canada
## 13
## Australia
## 11
## Germany
## 11
## France
## 10
## Netherlands
## 10
## Sweden
## 5
## Italy
## 4
## Norway
## 3
## Poland
## 3
## Switzerland
## 3
## Singapore
## 2
## Argentina
## 1
## Austria
## 1
## Belgium
## 1
## Denmark
## 1
## Finland
## 1
## Global (Guernsey-registered; about 4,000 to 5,000 of its staff are in Israel)
## 1
## Global (Guernsey-registered; about 5,000 of its staff are in Israel)
## 1
## Global (Ireland-domiciled)
## 1
## Global (Netherlands-based bank)
## 1
## India (roles); Opendoor is a US company
## 1
## Ireland
## 1
## Ireland and United States
## 1
## Ireland, Singapore, Malaysia (global Trust and Safety reorganisation)
## 1
## Israel (company headquartered in the US)
## 1
## Israel / United States (dual headquarters; operates in 25 countries)
## 1
## Italy (the Italian unit of the Ireland-based group)
## 1
## Japan
## 1
## Malaysia, United Kingdom and other countries (global content-moderation reorganisation)
## 1
## Nigeria
## 1
## Nigeria (pan-African; Jumia Technologies AG is incorporated in Germany)
## 1
## Portugal
## 1
## Spain (Spanish-language team of the US publisher G/O Media)
## 1
## Spain (the Madrid subsidiary of the US-headquartered company)
## 1
## United States (cuts in the US, India, the UK and Israel)
## 1
## United States (global firm)
## 1
## United States (global workforce; no country split given)
## 1
## United States (global workforce)
## 1
## United States (global)
## 1
## United States (Los Angeles HQ; operations in several countries)
## 1
## United States (remote-first company with staff worldwide)
## 1
## United States (San Francisco HQ; remote company with staff abroad)
## 1
## United States / Czech Republic (San Francisco HQ, main office in Prague)
## 1
## United States and India
## 1barplot(
sort(table(udaje$Country), decreasing = TRUE)[1:10],
las = 2,
col = "steelblue",
main = "Top 10 krajín podľa počtu udalostí"
)
Graf zobrazuje desať krajín s najvyšším počtom zaznamenaných udalostí prepúšťania súvisiaceho s využívaním umelej inteligencie. Najväčší počet zaznamenaných udalostí sa vyskytol v Spojených štátoch amerických (137). S výrazným odstupom nasledujú Izrael, India a Spojené kráľovstvo. Výsledky naznačujú, že prepúšťanie súvisiace s využívaním umelej inteligencie je najviac evidované v technologicky rozvinutých krajinách.
Analýza sektorov
Ďalej skúmam, ktoré odvetvia sú najviac ovplyvnené prepúšťaním súvisiacim s umelou inteligenciou.
sort(table(udaje$Sector), decreasing = TRUE)##
## Banking
## 16
## Software
## 11
## Education / Software
## 7
## Telecom
## 7
## Telecommunications
## 5
## Big Tech / Cloud
## 4
## Big Tech / E-commerce / Cloud
## 4
## Big Tech / Cloud / Software
## 3
## Business messaging software (CPaaS)
## 3
## Cybersecurity software
## 3
## Fintech
## 3
## Social media
## 3
## AI software for drug discovery
## 2
## Automotive
## 2
## Automotive software
## 2
## Big Tech / Social / AI
## 2
## Consumer apps / AI video models
## 2
## Consumer internet / social media
## 2
## Digital Media
## 2
## E-commerce software
## 2
## Enterprise networking / infrastructure software
## 2
## Freight logistics software and brokerage
## 2
## IT services / BPO
## 2
## Legal
## 2
## Logistics software (SaaS)
## 2
## Media/Journalism
## 2
## Newspaper publishing
## 2
## online grocery technology and warehouse robotics
## 2
## Technology (enterprise software/cloud)
## 2
## Technology / Ridesharing
## 2
## Technology services and software
## 2
## Telecom software and IT services
## 2
## Advertising and marketing services
## 1
## Advertising measurement software
## 1
## AI drug-discovery software
## 1
## AI language technology (translation software)
## 1
## AI transcription and captioning
## 1
## AI translation software
## 1
## Airline
## 1
## Airlines / aviation group
## 1
## Analytics software and credit scoring
## 1
## Appointment booking software (beauty and wellness)
## 1
## Audience measurement
## 1
## autonomous-vehicle testing software
## 1
## B2B sales data software
## 1
## Banking (fintech partner bank)
## 1
## Banking (wealth management)
## 1
## Banking / financial services
## 1
## Bitcoin mining / digital infrastructure
## 1
## Blockchain and AI infrastructure startup (crypto)
## 1
## BPO / Call Centers
## 1
## Broadcast media (local TV stations)
## 1
## Business and trade media
## 1
## Business software (SaaS)
## 1
## Business travel management
## 1
## Chemicals / Materials
## 1
## Cloud data software
## 1
## Competitive intelligence software
## 1
## Connected-car software
## 1
## Consumer Goods (CPG)
## 1
## Consumer goods (small household appliances)
## 1
## Consumer Goods / Tobacco
## 1
## Consumer internet (Dailyhunt, Josh)
## 1
## Consumer software (family safety and location app)
## 1
## Consumer technology
## 1
## Contact centres and business process outsourcing (CRM/BPO)
## 1
## Contact-center software
## 1
## Conversational AI software
## 1
## Creative digital-asset marketplace (Shutterstock subsidiary)
## 1
## Creative technology (stock media and AI video tools)
## 1
## Creator membership platform
## 1
## Crypto / fintech
## 1
## Crypto / Fintech
## 1
## crypto data and analytics
## 1
## crypto exchange
## 1
## Crypto exchange
## 1
## Cryptocurrency exchange (fintech)
## 1
## Customer data software
## 1
## Cybersecurity (managed detection and response / security operations)
## 1
## Cybersecurity (managed detection and response)
## 1
## Cybersecurity (SaaS and managed services)
## 1
## Cybersecurity (SaaS)
## 1
## cybersecurity software
## 1
## Cybersecurity Software
## 1
## Data analytics and digital operations services
## 1
## Data infrastructure software (cloud data warehouse)
## 1
## data observability software
## 1
## Data security and privacy software
## 1
## Design software
## 1
## Dev Tools
## 1
## Developer / Software
## 1
## digital asset custody and infrastructure
## 1
## Digital banking
## 1
## Digital media
## 1
## E-commerce (pan-African marketplace and logistics)
## 1
## E-commerce / fintech
## 1
## E-commerce / Marketplace
## 1
## E-commerce / online resale
## 1
## E-commerce enablement / SaaS
## 1
## E-commerce marketing software
## 1
## Education technology / academic integrity software
## 1
## Enterprise hardware / infrastructure
## 1
## Enterprise information management software
## 1
## Enterprise SaaS (CRM)
## 1
## Enterprise SaaS (developer/collaboration software)
## 1
## Enterprise SaaS (IT workflow software)
## 1
## Enterprise software (digital adoption)
## 1
## Enterprise software (ERP)
## 1
## Financial education (edtech)
## 1
## Financial services / Banking
## 1
## Financial services / Digital banking
## 1
## Fintech (custody for investment advisers)
## 1
## Fintech (digital payments)
## 1
## Fintech (payments)
## 1
## Fintech / consumer banking app
## 1
## Fintech / customer service
## 1
## Fintech / digital payments
## 1
## Fintech / Payments
## 1
## Fintech / Payments (AP/AR automation)
## 1
## Fintech / payments (one-click checkout)
## 1
## Food delivery and quick commerce
## 1
## gambling affiliate marketing and sports data
## 1
## Gaming
## 1
## geolocation and fraud-prevention software
## 1
## Geospatial data engineering services
## 1
## Grocery retail
## 1
## Health tech (Alphabet)
## 1
## Healthcare
## 1
## Healthcare / Revenue cycle
## 1
## Healthcare technology (SaaS)
## 1
## Home interiors (online platform)
## 1
## Hospitality Software (hotel property-management SaaS)
## 1
## Hospitality software (short-term rental management)
## 1
## HR / Payroll Software
## 1
## HR software
## 1
## iGaming / Online gambling technology
## 1
## Infrastructure engineering software
## 1
## Insurance
## 1
## Insurance (digital insurer)
## 1
## Insurance / Travel
## 1
## Insurance Brokerage / Fintech
## 1
## Insurance brokerage and fintech
## 1
## Internet / Marketplace
## 1
## Internet Infrastructure
## 1
## IT management software for managed service providers
## 1
## IT services / consulting
## 1
## IT services / enterprise tech
## 1
## IT services and consulting
## 1
## IT solutions / Value-added reseller
## 1
## Legal technology
## 1
## Live events ticketing
## 1
## Local newspapers
## 1
## Logistics
## 1
## Logistics / Freight Brokerage
## 1
## Management consulting
## 1
## Maps and location technology
## 1
## marketing and customer-engagement software
## 1
## Marketing technology
## 1
## Media (newspapers)
## 1
## Media / Broadcasting
## 1
## Media and entertainment
## 1
## Media and publishing
## 1
## Media monitoring and PR analytics
## 1
## Mobile games
## 1
## mobile games (skill-based competitions)
## 1
## Mobile marketing measurement (ad tech)
## 1
## Music licensing and creative software
## 1
## News publishing
## 1
## News Publishing
## 1
## Online course software
## 1
## Online creative courses (edtech)
## 1
## Online fashion retail
## 1
## Online food delivery
## 1
## online freelance-work marketplace
## 1
## online home-services marketplace
## 1
## Online legal services
## 1
## Online luxury fashion retail
## 1
## Online recruiting (early-career network)
## 1
## Online recruiting / HR technology (job search and employer reviews)
## 1
## Online travel
## 1
## Online travel / E-commerce
## 1
## online travel agency
## 1
## Payments / Fintech
## 1
## Pharmaceuticals
## 1
## Product-management software (SaaS)
## 1
## Productivity software (SaaS)
## 1
## Professional information / Media
## 1
## Public radio broadcasting
## 1
## Real estate software
## 1
## Real estate technology
## 1
## Real estate technology (online home buying)
## 1
## Regional newspapers
## 1
## Retail
## 1
## Semiconductors
## 1
## Small-business management software
## 1
## Smart building / Property technology software
## 1
## Social media (trust and safety, content moderation)
## 1
## Social media (US joint venture; office including content moderation)
## 1
## Software (Apple device management)
## 1
## Software (CRM and marketing platform)
## 1
## Software (incident management / DevOps SaaS)
## 1
## Software (product analytics)
## 1
## Software / Cybersecurity
## 1
## Software / developer tools (open source)
## 1
## Software / Search & observability
## 1
## Software / Social media management
## 1
## Software / Work management
## 1
## Sports data (real-time data feeds for betting, fantasy and media)
## 1
## tech career upskilling (edtech)
## 1
## Technology (AI models)
## 1
## Technology (cloud / managed services)
## 1
## Technology (VR, metaverse and wearables hardware)
## 1
## Technology / Services
## 1
## Technology hardware (PCs and printers)
## 1
## Telecom / BPO
## 1
## Travel (tailor-made tour operator)
## 1
## Video game services (localisation, testing, audio)
## 1
## Video games
## 1
## Wealth-management software (fintech)
## 1
## Web hosting and cloud services
## 1
## Website Builder / SaaS
## 1
## Website-building software (SaaS)
## 1
## Website-building software (WordPress)
## 1barplot(
sort(table(udaje$Sector), decreasing = TRUE)[1:10],
las = 2,
col = "darkgreen",
main = "Top 10 sektorov"
)
Analýza sektorov ukazuje, že najviac zastúpeným odvetvím je bankovníctvo, nasledované softvérovým priemyslom. Významné zastúpenie majú aj telekomunikácie a vzdelávacie technológie.
Počet unikátnych spoločností
length(unique(udaje$Company))## [1] 249Pomocou funkcie unique() bol zistený počet jedinečných spoločností nachádzajúcich sa v databáze.
Chýbajúce hodnoty
colSums(is.na(udaje))## Company Date
## 0 0
## Sector Country
## 0 0
## Functions Roles.counted
## 0 208
## Roles.reported..not.counted. Why.not.counted
## 144 0
## X..of.workforce Evidence.tier
## 0 0
## Attribution Execution
## 0 0
## Counts.in.US.index Claim
## 0 0
## Source Source.URL
## 0 0
## Event.ID Kind
## 0 0
## What.the.employer.said Job.families
## 0 0Skontrolovala som výskyt chýbajúcich hodnôt v jednotlivých premenných databázy. Analýza chýbajúcich hodnôt ukázala, že väčšina premenných je kompletne vyplnená. Najviac chýbajúcich údajov sa nachádza v premenných Roles.counted a Roles.reported..not.counted., čo je potrebné zohľadniť pri ďalšom spracovaní dát.
Záver
Databáza AI Layoffs Register obsahuje 300 pozorovaní a 20 premenných. Analýza ukázala, že najväčší počet zaznamenaných udalostí súvisiacich s prepúšťaním v dôsledku využívania umelej inteligencie pochádza zo Spojených štátov amerických. Z pohľadu sektorov boli najčastejšie zastúpené bankovníctvo a softvérový priemysel. Databáza obsahuje údaje o 249 spoločnostiach a poskytuje vhodný základ pre ďalšie štatistické spracovanie a vizualizáciu dát.