Table 1.

Participant profile

CodeRoleLevelSectorOrg. sizeAI exposure
P1HR directorSeniorManufacturingMediumMedium
P2HR business partnerMid-levelServicesLargeHigh
P3HR managerSeniorTechnologyLargeHigh
P4HR technicianMid-levelManufacturingMediumMedium
P5HR business partnerMid-levelProfessional svcsLargeMedium
P6HR directorSeniorAutomotiveLargeMedium
P7Chief human resources officer (CHRO)/corporate HR directorSeniorConstructionLargeMedium
P8HR managerSeniorManufacturingMediumLow
P9Regional HR directorSeniorTechnologyMultinationalHigh
P10People and talent managerSeniorIndustrial servicesMediumLow
P11HR admin manager (corporate)SeniorServicesMultinationalHigh
P12HR product managerMid-levelTechnologyLargeMedium
P13HR directorSeniorProfessional svcsMediumMedium
P14HR technician/generalistJuniorManufacturingMediumLow
P15HR directorSeniorAutomotiveLargeMedium
P16People director (shared services Centre, SSC)SeniorMulti-sector groupLargeHigh
P17HR managerMid-levelAquaculture/foodLargeLow
Note(s):

Org. size: Medium = 100–999 employees; Large = 1,000–9,999; Multinational = 10,000+. AI exposure level reflects self-reported engagement with AI tools in HR at the time of interview. CHRO = Chief Human Resources Officer; SSC = Shared Services Centre. AI exposure refers to participants’ self-reported engagement with AI-enabled tools in HR work: low exposure indicates exploratory or occasional use; medium exposure indicates regular use of digital or AI-supported tools in specific HR processes; high exposure indicates direct involvement in AI-enabled analytics, automation, generative AI tools or HR technology implementation. The technologies participants referred to spanned generative AI and large language models (e.g. ChatGPT, Microsoft Copilot, Gemini and internally developed GPT-based assistants), predictive or analytics-oriented HR systems (e.g. turnover prediction and workforce dashboards) and rule-based automation embedded in HR information systems (e.g. SAP, Workday, SuccessFactors)

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