AI to strengthen enterprise software firms, not replace them: Goldman Sachs

Enterprise software companies are likely to emerge stronger rather than be displaced in the artificial intelligence (AI) era, as the growing adoption of AI is expected to increase the value of enterprise data systems and the contextual intelligence built around them, according to a Goldman Sachs report.

The report said concerns that AI agents could replace enterprise software vendors are overstated, arguing that systems of record remain essential because AI applications rely on them for training, grounding, permissioning and contextual execution.

Highlighting the changing role of enterprise software, the report said, "Systems of action, the applications that execute tasks and drive workflows, depend on systems of record for training, grounding, permissioning, and contextual execution. As AI usage expands, the importance of systems of record should therefore rise rather than fall."


According to Goldman Sachs, the shift towards AI is also increasing the need for integrated and well-governed data, as fragmented datasets and isolated silos limit the performance of AI models, particularly as AI agents increasingly operate across multiple business functions.

This is expected to support continued migration towards cloud and data-platform architectures despite some near-term customer churn.

The report further argued that raw enterprise data alone is unlikely to provide a lasting competitive advantage. Instead, it said the real differentiator in the AI era will be the contextual layer built on top of enterprise data, comprising workflows, metadata, permissioning, auditability, embedded business logic and cross-functional reconciliation.

Emphasising this point, Goldman Sachs said, "In our view, the contextual layer built on top is a defensible moat in the AI era that contains the processes, workflows, metadata, permissioning, auditability, embedded business logic and cross-silo reconciliation."

The report added that this contextual layer enables AI applications to move beyond general intelligence to deliver actionable, domain-specific business outcomes, while also being difficult to replicate because it reflects years of implementation, operational controls, business exceptions and institutional knowledge.

Goldman Sachs said industry-specific or vertical software is also likely to benefit from AI rather than be replaced, as such platforms are built around specialised workflows, regulatory requirements, proprietary data and high switching costs. At the same time, AI-driven productivity gains could accelerate adoption in sectors that have historically lagged due to limited technology talent.

The report noted that some large enterprises are increasingly choosing to develop AI applications in partnership with large language model providers and systems integrators while continuing to retain their existing enterprise software platforms. It cited collaborations involving Stellantis, Mistral, Accenture, Palantir, Prometheus and Schindler as early examples of this trend.

Looking ahead, Goldman Sachs said governance, auditability, trust, embedded customer relationships, domain expertise and distribution networks are expected to remain key competitive advantages for incumbent enterprise software companies as AI adoption accelerates.