This study shows that top-down decomposition is problematic in the early stages of design. Instead, an opportunistic decomposition is better suited to handle the ill-structuredness of design problems. Designers are observed interleaving decisions at various levels of abstraction in the solution decomposition. The verbal protocols of three professionals designing a software system of realistic complexity are analyzed to determine the frequency and causes of opportunistic decompositions. The sudden discovery of new requirements and partial solutions triggered by data-driven rules and associations, the immediate development of solutions for newly discovered requirements, and drifting through partial solutions are shown to be important causes of opportunistic design. A top-down decomposition appears to be a special case for well-structured problems when the designer already knows the correct decomposition. Two cognitive models are briefly discussed in relation to opportunistic design. Finally, implications for training, methods, and computational environments to support the early stages of design are outlined.