All field notes

Mining is capital-intensive, asset-heavy, and safety-critical. Where AI improves uptime, safety, and yield—and why real operational data is the foundation.

Oil and gas is asset-heavy, data-rich, and safety-critical. Where AI improves production, maintenance, and safety across the value chain—and what it takes.

Aerospace demands precision and safety above all. Where AI improves maintenance, quality, and operations—within the rigorous validation the industry requires.

Food and beverage runs on thin margins, perishability, and safety. Where AI cuts waste, protects quality, and forecasts demand across the value chain.

AI is reshaping both attack and defense. Where AI strengthens cybersecurity—detection, triage, response—and the new risks AI systems themselves introduce.

Accounting is document- and data-heavy, and clients increasingly want advisory. How AI removes the grind so accountants deliver higher-value work—accurately.

Transportation is a constant optimization problem under real-world conditions. Where AI improves fleets, routes, and safety—and why real data and integration matter.

Sports is awash in data—performance, health, fan behavior. Where AI turns it into an edge, from injury prevention to fan engagement, and what it takes.

Fashion moves fast and wastes a lot. Where AI helps—trend forecasting, demand planning, personalization, visual search—to cut waste and lift sales.

Data scientist is one of the most mis-hired roles in tech. What they actually do, the skills that matter, and how to hire one who ships value—not just notebooks.

Data engineers build the pipelines everything else depends on. What they do, the skills to verify, and how to hire the foundation your AI and analytics need.

Most models never reach production—or break once there. MLOps engineers fix that. What they do, the skills to verify, and how to hire one who ships reliably.

Prompt engineering is more than clever wording—it's systematic design, evaluation, and iteration. What the role really is, and how to hire for it.

AI product management is different from standard PM—probabilistic outputs, data dependencies, evaluation. What the role needs, and how to hire for it.

Computer vision looks easy in demos and breaks on real images. What CV engineers do, the skills to verify, and how to hire for production, not benchmarks.

NLP now spans classic text processing and modern LLMs. What NLP engineers do, the skills that matter today, and how to hire one who ships in production.

Everyone can call an LLM API; few build reliable systems on top. What LLM engineers do, the skills to verify, and how to hire one who ships production AI.

Many AI consultants deliver strategy decks and disappear before anything ships. How to hire one who can actually build—and why that's the test that matters.

Modern chatbots are LLM systems, not decision trees. What chatbot developers need to know today, and how to hire one who builds something reliable.

Data analysts turn data into decisions—if you hire the right one. What the role is, how it differs from data science, and the skills that actually matter.

Most companies think they need AI researchers when they need AI engineers. When research is actually required, what to verify, and how to hire for it.

Deep learning is powerful and often overused. What deep learning engineers do, the skills to verify, and when a neural network is—and isn't—the right call.

RAG is the most common way to build reliable LLM apps—and the most commonly done badly. What RAG developers need to know, and how to hire one who ships quality.

AI automation only pays off when it works reliably inside real workflows. What AI automation engineers do, and how to hire one who delivers measurable ROI.