Requirements work has a reputation problem. It is rarely the part of an AI project that receives the keynote slide, the demo video, or the executive applause. Nobody opens a budget meeting by saying, “What we really need is a better way to ask the system what it must know.” They should, but apparently civilization still has limits. ...
Questions look cheap. That is why they are dangerous. In most enterprise AI projects, the visible work arrives late: dashboards, RAG demos, knowledge graphs, compliance assistants, workflow copilots, and executive slides with arrows pointing to a “semantic layer.” The invisible work arrives earlier and is less glamorous: deciding what the system must actually know, answer, retrieve, distinguish, reject, and explain. ...
Factory AI has an old communication problem. The model can say, “this screw-placement attempt is likely to fail.” The operator then asks the obvious follow-up: “Because of what?” A dashboard answers with a probability. A SHAP plot answers with colored bars. A feature-importance chart answers with something that looks scientific enough to intimidate the meeting room into silence. None of these answers necessarily tells the worker, engineer, or manager what is connected to what: the screw geometry, the robot arm, the training dataset, the preprocessing step, the model, the task, and the explanation artifact. ...
Documents are rarely wrong in the same way. A project proposal can be relevant but obsolete. A meeting note can be accurate but non-binding. A market-size estimate can be useful but contradicted by later due diligence. A regulatory question can be unanswered and still more important than a polished paragraph that sounds certain. This is the small, boring, expensive problem hiding inside many enterprise AI deployments: the system finds the right files, then treats unlike things as if they had the same authority. ...
Enterprise data has a familiar bad habit: it looks organized until someone asks a question that requires moving across it. A supplier is connected to a factory, the factory is connected to a product line, the product line is connected to a delayed shipment, and the shipment is tied to a contract clause that nobody wants to read at 11:40 p.m. The graph exists. The relationships exist. The answer is somewhere inside the structure. Then an LLM pipeline retrieves a subgraph, pastes it into a prompt, and asks the model to “reason carefully.” ...
Airports are where ordinary corporate coordination problems go to become expensive. A delayed data update is not just an “alignment issue.” A vague handoff is not just “cross-functional friction.” A misunderstood phrase can move aircraft, ground crews, gates, passengers, baggage, and regulatory responsibility in the wrong order. Aviation has a talent for making management consultants’ favorite words suddenly physical. Very inconsiderate of it. ...
Memory is the boring word that decides whether an AI agent is useful or merely theatrical. A familiar business scene: a team builds an AI workflow to scan documents, generate ideas, produce drafts, and recommend next actions. The demo looks clever. The first week feels magical. Then the cracks appear. The system repeats discarded ideas. It forgets why an option was rejected. It summarizes a project but cannot explain how one failure in March should change a decision in April. Its “memory” is really a longer chat transcript wearing a lab coat. ...
Documents change. That sounds too obvious to deserve a research paper. Product documentation changes. Compliance rules change. APIs are deprecated. Security policies are replaced. A customer support article says one thing in January, a release note quietly reverses it in March, and the enterprise search system confidently retrieves both as if time were just a decorative metadata field. ...
Prescription checks are a good way to humble AI. Not because the language is impossible. Drug labels, clinical notes, dosage instructions, contraindications, and interaction warnings are all text-heavy. LLMs are good at text. That part is not the problem. The problem is that prescription verification is not a writing task. It is a safety task disguised as a reading task. A pharmacist is not merely asking, “Does this paragraph sound medically reasonable?” The real question is narrower and harsher: given this patient, this drug, this dose, this route, this timing, this interaction profile, and this missing or available clinical data, is there a specific safety issue that must be raised? ...
Memory is a boring word until the diagnosis is wrong. A pathologist does not look at a whole-slide image as a flat picture. They see morphology, compare it with disease categories, recall grading criteria, filter out misleading patterns, and decide which pieces of old knowledge deserve attention in the current case. That last part is easy to understate. Expertise is not only having knowledge. It is knowing when to activate it. ...