Decision Support

Guidelines are not novels. That sounds obvious until we remember how most retrieval-augmented generation systems treat them. A clinical guideline becomes text. The text becomes chunks. The chunks become embeddings. The embeddings become “context.” Somewhere in that mechanical conversion, a dosing table, a referral pathway, or a threshold hidden inside a flowchart quietly loses its shape. Then everyone acts surprised when the answer is fluent but clinically thin. Very mysterious. ...

A dashboard says the model is accurate. The pilot team says the interface is clear. The post-training survey says users trust the system. Everyone nods, because this is the part of AI deployment where organizations prefer numbers that look clean and verbs that sound finished: validated, launched, adopted. Then the system enters a real workflow. ...

A flooded street is not only a drainage problem. It is a transport problem, a budget problem, an insurance problem, a public-trust problem, and, if the city waits long enough, a very expensive lesson in pretending that yesterday’s weather statistics are still a planning manual. Copenhagen is a useful place to begin because the paper’s case is not imaginary. In 2011, the city experienced a major cloudburst that flooded streets, disrupted roads and rail, and caused damage estimated at around 6 billion Danish kroner. The new research paper, Artificial Intelligence for Climate Adaptation: Using Reinforcement Learning for Climate Change-Resilient Transport, uses Copenhagen’s inner city as the testbed for a larger question: how should a city decide where, when, and how much to invest in flood adaptation between 2024 and 2100?1 ...

Ambiguity is not a rare managerial defect. It is Tuesday. A senior manager asks for a “highly effective” plan. A product team is told to “maximize adoption” without being told whether adoption means revenue, users, engagement, retention, or the investor’s favorite dashboard number this quarter. An operations team receives the instruction to review “all new and underperforming channels,” which may mean channels that are both new and underperforming, or all new channels plus all underperforming channels. Excellent. Everyone can now attend three meetings and pretend the sentence was clear. ...

Schedule three people, one car, two children, five afternoon activities, and several goals that quietly hate each other. Then ask a normal person to find the best plan. That is already a planning problem. Now ask the same person to understand why a plan failed, which goals caused the failure, what could be added without breaking the plan, and what must be sacrificed if one more constraint is enforced. ...

Data is usually incomplete. That is not a philosophical statement; it is Tuesday. A clinical study may record which treatment a patient received but miss one biomarker. A compliance system may know that two entities are connected but not know the contract terms. An environmental monitoring project may have sensor readings for some locations, at some times, under some weather conditions, and then a heroic spreadsheet pretending this is a dataset. ...

Diagnosis has a simple business problem hiding inside a clinical one: nobody wants a black box that is confident for the wrong reason. That is especially true in medical imaging. A brain MRI classifier that says “tumour” or “non-tumour” is not automatically useful because it crosses a respectable accuracy threshold. The difficult question comes next: did the model look at the clinically relevant region, or did it discover some convenient artefact in the image pipeline? A single heatmap may answer that question. It may also merely look persuasive, which is not quite the same thing. Medicine, regrettably, is one of those domains where aesthetic confidence is still not a validation method. ...

A simulation model is not a chatbot with a spreadsheet attached. That sounds obvious until a project team starts treating the LLM as if it were the entire modeling stack: the analyst, the programmer, the validator, the documentation clerk, the statistical package, and occasionally the intern blamed when the result changes on Tuesday. The convenient story is that better prompting will tame the system. Add more examples. Add a RAG. Set temperature to zero. Smile at the demo. ...

Back office teams rarely suffer from a shortage of opinions. They suffer from a shortage of completed cases. A bank wants to know whether a loan application will require costly rework. A hospital wants to know whether an emergency-department case will need laboratory processing. An operations manager wants to know how long a running case will take before it becomes tomorrow’s apology email. Predictive Process Monitoring, or PPM, is supposed to help with exactly this kind of question. It looks at event logs and predicts what will happen next: total completion time, future activities, process outcomes, delays, exceptions. ...

A PDF is supposed to sit quietly. It may contain claims, equations, tables, and occasionally an appendix long enough to test a reviewer’s commitment to science. It is not supposed to negotiate with the system judging it. That assumption becomes unreliable once a document enters an LLM-based workflow. To the human reader, a sentence rendered in white text may be invisible. To a text-extraction pipeline, it can remain perfectly legible—and potentially indistinguishable from an instruction the model is expected to follow. ...