Twenty years ago we published a piece focussed on Mark Daskin’s Everyday Lessons from Operations Research that he delivered at INFORMS 2006. They are just as relevant today, if not more so, in the age of AI (Hype).

1. Service and Performance Gets Worse as Utilization and Variability Increase
   This is true in so many ways. Dumb users asking stupid questions with poorly formed prompts that don’t match the training prompts flooding the internet with poor and hallucinatory answers that are fed into the next AI model in the collective race to the bottom. Response times decreasing as the models need to get bigger to process ever more mediocre data and, hopefully, create better responses (even though that won’t happen). Costs going through the roof as a result of the constant need for more computing power.
2. … But Variability is necessary.
   Because every request is fundamentally different, even if phrased the same as the lack of standardization, and training on that standardization, ensures every user has a different intent with their request.
3. Expect the unexpected in today’s world.
   Most responses will be okay, some will be bad, a good number will be completely hallucinatory, and every now and again the hallucination will provide a revolution insight — which will go unnoticed because most users now lack the cognitive power, and possibly the intelligence, to even detect a hallucination in the first place due to the mass cognitive atrophy Gen-AI is, provably, inducing.
4. If it is too good to be true, it probably is not.
   But yet we continue to believe the lies, damn lies, and hallucinatory hype spewed by the purveyors and pushers of Gen-AI and the garbage it spits out.
5. Life is full of errors.
   And Gen-AI multiplies those errors tenfold to ten-thousandfold!
6. A good decision can result in a bad outcome.
   Even when the AI gets it right on the data, the data is always backward looking, not forward looking, and the right decision based on a past trend can still be wrong if a key variable or assumption just changed and the data does not yet reflect that. So even when the AI actually gets it right, it can still be wrong!
7. If you are not using all you have, don’t pay for additional quantity.
   If you only have a small amount of data to train the model on, don’t pay for the largest model available — not only will it not help your case, it will hurt as there won’t be enough data to “train” the model even to reasonable levels of accuracy (relative to what the model is theoretically capable of). Always go with the smallest model that will theoretically meet your needs — and, moreover, if you have multiple distinct needs, multiple distinct models will perform much better than trying to create one big mega model.
8. You can never do better by adding a constraint.
   Never, ever, ever. Whatever you are trying to minimize will increase. Constraints restrict options and that will always result in increased cost, increased risk, etc.
9. Keep it Simple
   The best AI, especially in the age of Gen-AI, is no AI. More specifically, don’t use AI where AI is not needed. Deterministic automation, when properly applied, has worked well for decades and now, in an age where we can encode adaptive workflows, suggest exception resolutions, have the rules automatically update when a resolution is accepted or created, and improve over time.
10. Think about problem formulation.
    The formulation dictates the potential solutions … and the better you define a problem, and a stepwise algorithm to resolve that problem, the less need you will actually have for (Gen-) AI.
11. Look for compromise solutions.
    If a problem ends up being defined particularly constrained or complicated when multiple parties get involved, work together to simplify the constraints or complexity where it truly isn’t needed. There’s a big difference between preference and requirement, and understanding that allows for the creation of good comprise models and solutions.
12. Data is not information.
    It never has been, and never will be. Moreover, data pulled out of an LLM might not even be accurate, so the chance of it producing usable information is even less. Always remember that.