State-space abstraction. Human-scale poker games are far too large to exactly compute optimal strategies. Instead, a technique is used to simplify the game down to a size where we can compute good (but no longer perfect) strategies. This usually means finding decision points in the game that are strategically similar and merging them together. Automated techniques for doing this will become more and more important as we move from two-player limit Texas hold'em to larger games, like no-limit or multi-player games.
Multi-player games. The most successful approach to date for making strong computer poker agents uses the mathematics of game theory to decide how to pick actions so that the strategy cannot lose much, or at all, against a perfect opponent. In games with more than one opponent, however, the mathematical guarantees of this approach are no longer useful. Instead of using a single precomputed strategy, a good computer poker agent will instead have to observe its opponents and choose a strategy that works well in response. This is largely an open problem.
Adapting to opponents during a match. The goal of poker is to maximize the amount of money won from the opponents. However, the game theoretic approach to the game pursues a different goal, by trying to minimize how much money is lost to a perfect adversary. In order to win as much as possible from an opponent, a player has to observe the opponent's actions, find out what mistakes the opponent is making, and change their strategy in response to take advantage of those mistakes. Computer poker research has led to effective techniques that work when the opponent is observed for millions of games, but human players are able to adjust their strategy after only tens or hundreds of games.