Financial institutions evaluating their technology stacks increasingly encounter frameworks designed to contextualize where legacy systems fit alongside emerging AI capabilities. One such model breaks software evolution into three distinct but potentially complementary layers. The Three Stages Software 1.0 encompasses the deterministic, rule-based systems that form the backbone of most financial infrastructure today—core banking APIs, ERP platforms, and compliance workflows. These function as systems of record where stability and predictability remain paramount. Software 2.0 represents the shift toward data-driven, probabilistic approaches. Machine learning models for credit risk scoring, fraud detection, and market forecasting exemplify this layer. Unlike their predecessors, these systems derive behavior from training data rather than explicit rules. Software 3.0 describes the emerging landscape of AI agents and copilots—systems capable of contextual reasoning and responding to natural language prompts. Proponents of this framework position these as potential "systems of action" that could handle decision-making and orchestration tasks. Integration Over Replacement The framework's central argument is that these stages need not compete. Rather than viewing each as superseding the last, the model suggests that combining all three—stable deterministic workflows, predictive intelligence, and AI-driven orchestration—may represent the most practical architectural approach for institutions navigating digital transformation. Practical Considerations How this conceptual model translates into any specific organization's architecture will depend on existing infrastructure, regulatory constraints, and strategic priorities. The framework appears intended as a conversation starter rather than a prescriptive blueprint—a way to categorize capabilities and identify potential integration points across technology generations. For institutions already deep into machine learning implementations, the question becomes how agent-based systems might augment rather than replace existing investments in both legacy systems and data science infrastructure. References