A product manager asks for reinforcement learning. When should you talk them out of it?

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Answer

$Whenever the problem is really supervised prediction with a decision rule on top, which covers most business cases.$
$If the action does not change the future state of the world, you do not need sequential decision making: predict the outcome and optimize the decision analytically.$
$Reinforcement learning also needs either a fast, faithful simulator or the willingness to let a bad policy act on real users, and most organizations have neither.$
$Sparse rewards, long delays between action and outcome, and no ability to explore safely all push the difficulty up sharply.$
$The honest recommendation is usually a contextual bandit, which keeps the exploration benefit without credit assignment over time, or a predictive model plus an optimizer.$

Check yourself — multiple choice

Never, RL is always stronger
When actions do not change future state, when there is no faithful simulator or safe exploration, or when rewards are sparse — a contextual bandit or predict-then-optimize usually fits better
Only when data is small
When the state space is discrete

Sequential decision machinery is only justified when actions genuinely affect future states and exploration is feasible.

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