Technology & innovation
A global survey of 11,000 finance and accountancy professionals finds the industry divided on AI's growing role in recruitment, with 48% saying they lack confidence in AI-driven hiring processes and 43% expressing trust in the technology. Skepticism runs highest among senior leaders and older workers.
More than half of board-level, senior executive, and partner-level respondents lack confidence in AI hiring tools; among broader leadership groups, the figure rises to 57%.
Gen Z is the outlier: 55% of respondents under 29 said they were confident in AI-enabled hiring, compared to 37% of millennials, 26% of Gen X, and 35% of baby boomers.
Top concerns include loss of human touch and feedback (29%), AI discrimination based on resume data (29%), and lack of transparency in AI-driven decisions (13%).
Read more via Staffing Industry Analysts
As AI becomes more embedded in biotech research and development, the talent companies most need is neither traditional data scientists nor traditional biologists, but professionals who can work across both disciplines. That combination is increasingly scarce.
64% of life sciences organizations surveyed late last year were actively recruiting, with AI among the top priorities, per the BioSpace 2026 U.S. Life Sciences Employment Outlook report.
Among 100 biotech and biopharma organizations using AI, the top talent priority was data scientists with life sciences domain knowledge, followed by computational biologists with AI expertise and AI engineers who can build and deploy models, per Benchling's 2026 Biotech AI Report.
Computational biology is becoming more engineering-heavy; AI engineering in biotech is becoming more domain-specific, as biological data presents challenges, including noise, sparsity, and expensive generation, that differ from other AI applications.
Companies are advised against assuming all needed capability must come from external hires: a computational biologist already on staff who deepens machine-learning skills may ultimately be more valuable than a newly hired AI specialist who needs years to develop domain knowledge.
Read more via BioSpace
Fewer than 1 in 10 organizational codes of conduct explicitly address artificial intelligence or technology ethics, leaving employees without formal guidance on one of the most significant changes to how work gets done, according to a report from advisory firm LRN.
Nearly all of 2,000 full-time employees surveyed said their employers require them to certify or acknowledge a conduct code, but a declining share had actually used their code as a resource; nearly 1 in 5 said their code lacked practical guidance.
55% of employees either experienced or witnessed workplace misconduct in 2025, up from 41% in 2024; just 66% felt they could report misconduct without retaliation, down from 71% the prior year.
LRN says most employers won't need entirely new sections to address AI, but will need to connect the technology to existing ethical principles such as accountability, fairness, and transparency.
Read more via HR Dive
A new study drawing on Swedish census and employment data from 1880 to 2019 finds that roughly 70% of workers today are employed in occupations whose core functions already existed before 1900, suggesting that technological waves reshape job content far more often than they eliminate jobs entirely. The research raises a different, quieter concern about AI.
The study found that when prior automation-exposure studies estimated mass job losses, they were counting automatable tasks, not occupations; the labor market prices and retains whole occupations, which have proven far more durable than task-based models predict.
Durable new employment has historically come not from technology-specific roles, which tend to be volatile and often disappear, but from the broader specialization that productivity gains make possible across the wider economy.
The digital wave's technology-specific occupations, including computer programmers, have aged to roughly the workforce average, a demographic pattern that historically signals an occupation that has stopped pulling in new workers.
The open question for AI, the researchers argue, is not whether it will displace workers from tasks, but whether it will generate enough productivity and scale to spin off the durable, specialized new work that earlier technological waves did.
Read more via CEPR/VoxEU
