GPGPU in the Real World
Most of GPU Gems 2 is structured in a "gem"-style fashion, detailing best practices and cutting-edge uses of the GPU—particularly advanced graphics techniques. But the book also provides numerous examples of how GPUs have excelled at GPGPU tasks.
A particularly interesting chapter (and perhaps profitable to certain readers) discusses computational finance and the pricing of options. GPUs are demonstrated to be far superior to CPUs for the task of efficiently determining the fair value of options (in this case, tens of thousands of options simultaneously). This task concerns Wall Street institutions intimately, in part due to a need to hold sufficiently large cash reserves to cover market fluctuations. Because this requirement necessitates borrowing large sums at overnight interest rates, accurate, timely calculations lead to less conservative, more accurate borrowing, and therefore less interest paid. This example from the world of financial engineering shows how GPUs can be extremely useful for decidedly non-graphical problems. Other examples touch on computational biology (protein structure prediction), partial differential equations (such as those used in physics simulation), sorting algorithms, fluid flow (the Lattice Boltzmann Method), and fast Fourier Transforms (FFTs) on the GPU.
The International Technology Roadmap for Semiconductors (ITRS) compiles the industry trends and has forecast a 71% increase in capability of chips year after year.  That incredible growth rate, compounded with the ability to build configurations with multiple GPUs operating in parallel via SLI, makes it likely that the GPU will continue to subsume functionality (previously belonging to CPUs) for increasingly mainstream computation. Chances are that opportunities exist in your own business for GPGPU computation. It's well worth exploring this field and jumping on the train soon, because it's only getting faster as GPUs increase in performance, functionality, and programmability.