Speaker
Description
This study explores AI integration maturity and task-level AI usage in workplace settings across industries and organization sizes. Rather than treating adoption as a simple yes/no measure, it studies how frequently professionals use AI, which tasks are supported, the typical depth of assistance (from suggestions to automation), and how governance and readiness mechanisms (official AI policies, training provision, approved tool lists, and confidential-data rules) relate to adoption and perceived impact. A quantitative, cross-sectional survey design was employed to capture self-reported practices and organizational context in a cross-industry sample. Data were collected via an anonymous online questionnaire administered via Microsoft Forms and open for approximately one month (20 January 2026 – 19 February 2026). Convenience sampling through online distribution channels yielded an international respondent pool, with the largest shares from the United States (20.37%, n=44), Romania (17.13%, n=37), and the United Kingdom (14.81%, n=32). After data screening and quality controls (including removal of AI-user responses with completion times under 2 minutes 30 seconds, exclusion of non-valid country entries, and manual review of straight-lined or internally inconsistent cases), the final dataset included 216 valid respondents, of whom 161 reported using AI tools for work in the past three months. Descriptive statistics were used to characterize adoption and task-level patterns. Chi-square tests examined associations between categorical variables (industry, company size, policy presence, training, approved tools, confidential-data rules), and Spearman correlations assessed relationships among ordinal variables (usage frequency, assistance depth, time saved, perceived productivity/efficiency impact). Results indicate high overall AI adoption (74.5%), with significant variation across industries and company sizes. Adoption was positively associated with the presence of an official AI policy and with training provision. AI policy was strongly linked to operational governance mechanisms such as approved tool lists and clearer confidential data rules, suggesting that organizational structures support more mature integration. Task-level findings show that AI is most frequently used for search/summarization and writing/communication, while customer interaction and meetings show lower assistance. Across tasks, AI use is predominantly augmentation-oriented (suggestions and drafting), with fully automated steps rare. More frequent AI use correlates with greater self-reported time savings and more favourable perceptions of productivity, work quality, and organizational efficiency. Reported barriers are dominated by confidentiality, trust/accuracy, and compliance concerns, underscoring the importance of governance and risk management for responsible AI integration.
