Adoption is where the technology meets an existing process. This topic tracks how that goes.
How organisations are rolling out AI tooling: workflows, guardrails, training, and the results they report.
24 stories
AI Industry & Models
Direct PDF input is now practical for many multimodal models. A test packet shows why tables, footnotes, diagrams, and cross-page references still need a parsing pipeline.
Tobias Lund · October 5, 2026 · 7 min
AI Governance & Ethics
Before testing whether an attention score is right, employers need to know what the software extracts from workers, where it goes, and whether participation can be voluntary.
Irene Vasko · 7 min read
AI Governance & Ethics
Selection-rate testing can reveal disparities in one hiring workflow. It cannot certify accuracy, fairness or compliance when the software, applicant pool or deployment has changed.
Irene Vasko · 8 min read
AI Industry & Models
One AI request can have different boundaries for inference, logs, abuse review, and support. The deployment setting and access controls matter more than the API hostname.
Tobias Lund · 8 min read
AI Industry & Models
Native PDF support removes a conversion step, but it does not guarantee correct rows, footnotes, or chart labels. Use a structured parser when those relationships determine the answer.
Tobias Lund · 7 min read
AI Industry & Models
Mirror sampled production requests to a candidate model, hide its outputs, and compare each run. The method exposes task-specific regressions before a model swap reaches users.
Tobias Lund · 8 min read
AI Industry & Models
Native PDF input removes a preprocessing step, but it does not remove layout errors. The right input format depends on whether the document contains tables, columns, scans or citation-sensitive text.
Tobias Lund · 8 min read
AI Governance & Ethics
A New Jersey employer’s obligations can change when an applicant crosses a state line or encounters a different screening tool. Build the notice map around each decision point.
Irene Vasko · 8 min read
AI Governance & Ethics
A resume model is not covered merely because a vendor calls it AI. Employers need evidence showing what it outputs, how recruiters use it, which jobs it affects, and whether required audits and notices exist.
Irene Vasko · 8 min read
AI Governance & Ethics
Before an AI scribe records a visit, pin down who handles the data, whether it trains models, when recordings disappear, and how corrections reach the chart.
Irene Vasko · 8 min read
AI Governance & Ethics
New Jersey’s discrimination law already reaches automated hiring. Before a scoring tool goes live, employers need evidence for its job relevance, accommodation route, vendor controls and candidate notices.
Irene Vasko · 8 min read
AI Governance & Ethics
A Newark employer recruiting in Manhattan, Chicago, or Colorado can inherit different audit, consent, and appeal duties. The compliance trigger often follows the job or applicant, not headquarters.
Irene Vasko · 8 min read
AI Governance & Ethics
An applicant may encounter four automated systems before a recruiter calls. Mapping those systems shows which discrimination duties, notices, bias reviews, and vendor records may apply.
Irene Vasko · 8 min read
AI Governance & Ethics
A hiring score can trigger different duties in Colorado, Illinois and New York City. The workable unit of compliance is one system, one use and one jurisdiction per inventory row.
Irene Vasko · 8 min read
AI Governance & Ethics
A language model can make decision codes readable, but fluency is not evidence. The safe workflow binds every sentence to factors the decision system recorded as principal reasons.
Irene Vasko · 8 min read
AI Governance & Ethics
A clean fairness result may describe only the applicants who survived résumé parsing and knockout rules. Auditors need the population entering each gate, not merely the group an AI model scored.
Irene Vasko · 8 min read
AI Governance & Ethics
Adding a generated brief or ranking to recruiting software can create compliance duties when recruiters rely on it. The tool’s influence, not its “copilot” label, controls the analysis.
Irene Vasko · 8 min read
AI Governance & Ethics
A provider can document its model, restrictions, tests, and updates. It cannot prove that your hiring workflow treats candidates fairly or that recruiters use its output as intended.
Irene Vasko · 7 min read
AI Governance & Ethics
A useful employment AI inventory follows one candidate through each decision, records how software changes the outcome, and separates enforced rules from enacted or proposed duties.
Irene Vasko · 8 min read
AI Governance & Ethics
New Jersey’s discrimination law already reaches algorithmic hiring decisions, while more specific proposals remain unsettled. An inventory shows which tools require scrutiny and which merely hold records.
Irene Vasko · 8 min read
AI Governance & Ethics
When email, chat, or meeting summaries become performance evidence, a generic AI notice is inadequate. Workers need access to the record, correction paths, retention limits, and a real appeal.
Irene Vasko · 8 min read
AI Industry & Models
Reasoning models help when spreadsheet cleanup requires judgment across several cells. Straight category rewrites and formula templates rarely justify the wait.
Tobias Lund · 7 min read
AI Governance & Ethics
Start with one sensitive workflow, then require evidence for every model, storage system, evaluation, incident path, and opt-out behind it.
Irene Vasko · 8 min read
Consumer AI Hardware
Dedicated recorders captured a noisy three-person meeting more reliably only when placed well. Speaker labels, names, and action items still demanded a human cleanup pass.
Devin Oyelaran · 7 min read
Other impacts
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