We consider a Lévy process $Y(t)$ that is not permanently observed, but rather inspected at Poisson($ω$) moments only, over an exponentially distributed time $T_β$ with parameter $β$. The focus lies on the analysis of the distribution of the running maximum at such inspection moments up to $T_β$, denoted by $Y_{β,ω}$. Our main result is a decomposition: we derive a remarkable distributional equality that contains $Y_{β,ω}$ as well as the running maximum process $\bar Y(t)$ at the exponentially distributed times $T_β$ and $T_{β+ω}$. Concretely, $\overline{Y}(T_β)$ can be written the sum of the two independent random variables that are distributed as $Y_{β,ω}$ and $\overline{Y}(T_{β+ω})$. The distribution of $Y_{β,ω}$ can be identified more explicitly in the two special cases of a spectrally positive and a spectrally negative Lévy process. As an illustrative example of the potential of our results, we show how to determine the asymptotic behavior of the bankruptcy probability in the Cramér-Lundberg insurance risk model.