It is the reference textbook on algorithms: sorting, data structures, dynamic programming, graphs, complexity. Written for a university course, with formal proofs, and used the way a dictionary is used — opened at the entry you need.
What I took from it
The habit of asking what happens when the input grows tenfold. It is the one idea from this book I use most weeks. The difference between a process that works on a thousand records and dies on a hundred thousand is almost never the server; it is that somebody wrote something that grows quadratically and small data hides it. Before I accept that a slow process needs a bigger machine, I want to know how it grows.
The second useful thing is the vocabulary of graphs. Plenty of business problems that arrive dressed as lists are really dependencies: what has to happen before what, what blocks what, where there is a cycle nobody has spotted. Having the names — topological order, cycle, path — changes the conversation with a technical team, because it stops being about the symptom.
The third is a warning against myself: most problems that look like they want a clever algorithm are solved by sorting first, or by keeping an index. Elegance is usually surplus.
What I left out
The proofs, the amortised analysis and virtually all of the theory. And something the book does not say is worth adding: in business software the bottleneck is almost never the algorithm, it is the query that runs once per row of a list. That problem, the one you actually meet, does not appear in these pages, because the book's cost model assumes reading a value is free.