Generative artificial intelligence may spur headcount growth as tech c-suite expands

Generative artificial intelligence is reshaping business models and could also drive increases in headcount as technology executive ranks broaden.

The article examines how generative artificial intelligence is changing the way companies operate and suggests that those changes could lead to net increases in employment within technology functions. Rather than only automating tasks and reducing roles, the piece argues that adoption of generative artificial intelligence may create new responsibilities, expand strategic programs and require additional specialists to manage tooling, integration and oversight.

It highlights implications for corporate leadership, noting that technology executive ranks may broaden as organizations respond to the complexity and opportunity of generative artificial intelligence. The article explores the prospect of an expanded tech c-suite, with more senior roles focused on model governance, security, data stewardship and cross-functional deployment. Those shifts at the executive level could cascade into hiring across engineering, product, compliance and operations to support scaled use of generative artificial intelligence.

The reporting frames these developments as part of a broader reconfiguration of business models. As companies incorporate generative artificial intelligence into workflows and customer offerings, they may rethink team structures, talent strategies and investment priorities. The article raises questions about how firms will balance automation gains with the need for new capabilities and leadership, and it suggests that hiring trends and organizational design will be central to how enterprises capture value from generative artificial intelligence.

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Artificial intelligence detects suicide risk missed by standard assessments

Researchers at Touro University report that an Artificial intelligence tool using large language models detected signals of perceived suicide risk that standard multiple-choice assessments missed. The study applied Claude 3.5 Sonnet to audio interview responses and compared model outputs with participants’ self-rated likelihood of attempting suicide.

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