This is Jensen's inequality at work.
First of all, note that the first line can be rewritten as an expectation
$$\sum_{s} p(s \mid \mathbf{a}) \log p(\mathbf{y} \mid s, \mathbf{a}) = \mathbb{E}_{p(s|a)}[\log p(\mathbf{y} \mid s, \mathbf{a})]$$
Then Jensen's inequality gives (Note that a log function is a concave function so gives the opposite inequality to what is normally given when explaining Jensen's inequality with respect to convex functions):
$$\mathbb{E}_{p(s|a)}[\log p(\mathbf{y} \mid s, \mathbf{a})] \leq \log\mathbb{E}_{p(s|a)}[ p(\mathbf{y} \mid s, \mathbf{a})] $$
and then finally you can rewrite the Expectation as a summation.
$$\log\mathbb{E}_{p(s|a)}[ p(\mathbf{y} \mid s, \mathbf{a})] = \log \sum_{s} p(s \mid \mathbf{a}) p(\mathbf{y} \mid s, \mathbf{a})$$