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🤖 Accelerating Research with AI by Kate Moran and Maria Rosala for Nielsen Norman Group | NN/g map where AI genuinely earns its place across the research process — strongest in planning (ideation, drafting study docs, generating survey and interview questions) and in analysis (transcription, first-pass coding) — and where it falls down, especially during live behavioural sessions like usability testing. Their throughline is to treat AI as an accelerator for specific, well-scoped tasks, never as a stand-in for the researcher. The piece is blunt that you should not hand your whole analysis to a model, because it lacks the contextual judgment to synthesise meaning. A practical map for deciding which parts of your workflow to speed up this week without trading away rigour.

🎯 Judgment, Taste, and the Parts of Research We Struggle to Define by Saeideh Bakhshi | Bakhshi names two abilities that separate experienced researchers but rarely make it into a methods deck: judgment, which is deciding well when information is scarce (trading speed against rigour, or stakeholder pressure against research independence), and taste, which is discernment when information is abundant (knowing which findings deserve emphasis and which threads are worth pulling). She argues both are built through accumulated tacit knowledge, creative reframing, and honest reflection on real outcomes, not through methodological skill alone. The sharpest reframe: expertise shows up less in technique and more in sensing which explanations still feel incomplete even when the analysis is technically sound. A timely counterweight to the rush to automate the analysis.

🧭 The Market Wants Its Hands-On Leaders Back by Marina Krutchinsky | Krutchinsky argues the 2026 market has swung back toward player-coach leaders who set direction and still do the work — a shift sharpened by shrinking teams and AI absorbing the production layer. She draws a clear line between an IC portfolio (proving you can execute) and a leadership portfolio (proving judgment exercised through others), and warns that polished personal craft can actually read as over-attachment to your own output. For research leaders, the takeaway is to show taste applied at scale — through systems, criteria, and what your team ships — rather than a gallery of your own deliverables. Sits naturally alongside this week's pieces on judgment and on where AI fits.

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