New adoption data shows the biggest productivity gains go to people using purpose-built AI tools, not general chatbots. The difference isn't model quality — it's context and integration.
A field guide to choosing between deterministic automation and goal-seeking agents — with a decision tree, cost math, and the honest cases where a plain script still wins.
Model quality isn't your bottleneck anymore — context portability is. We map where the time actually goes in AI-assisted work, and what to fix first.
Every agent touching your inbox, files, and calendar is a non-human user with credentials and blast radius. Most orgs provision them like scripts and hope for the best.
Memory, not model size, decides whether an agent finishes a task or loops forever. Here's how planning, working memory, and retrieval actually fit together.
A practitioner's guide to when LLM-based forecasting earns its keep and when it's confidently extending a trend line into thin air.
Averaged benchmark scores hide the subgroup failures that break real workflows. Here's how to build disaggregated, reproducible evals for triage, summaries, and spreadsheets.
Chasing the single 'best' LLM is a losing game. Here's a practical framework for routing each task — code, extraction, drafting, reasoning — to the model that actually fits.
Reasoning models solved 'can it answer.' Agentic training moved the hard part to 'can it act reliably' — and that shift changes how everyday work gets done.
Confident guessing is the most expensive failure mode in AI workflows. Here's how surfaced confidence and cited sources turn inbox triage and search into something you can actually trust.
Enterprises can't deploy agents they can't review. Email threads already give every agent action a timestamped, human-readable, forwardable paper trail.
Clever prompts don't make agents accurate — verified, fresh sources do. Here are the grounding architecture patterns that keep AI agents honest.
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