Consolidating systems for artificial intelligence with integration platforms

Enterprises are shifting from ad hoc integration tools to consolidated platforms as fragmented systems strain under the demands of artificial intelligence driven workloads. Integration complexity, data quality issues, and governance gaps are pushing organizations toward end to end approaches that can handle higher data volumes and speed.

Enterprises have spent decades responding to new business pressures with point solutions, from cloud services for scalable infrastructure to mobile apps for smartphone centric customers and internet of things systems for real time operational visibility. Each addition often improved a specific function, but over time these disconnected tools formed a tangled and fragile environment that required complex workarounds to keep data flowing. Instead of a coherent technology foundation, many organizations now operate a patchwork of loosely connected applications and platforms.

The consequences of this fragmented approach are increasingly visible in business performance. Today, fewer than half of CIOs (48%) say their current digital initiatives are meeting or exceeding business outcome targets. Another 2025 survey found that operations leaders point to integration complexity and data quality issues as top culprits for why investments haven’t delivered as expected. Achim Kraiss, chief product officer of SAP Integration Suite, notes that a fragmented landscape obscures end to end business processes, weakens monitoring, troubleshooting, and governance, and drives up costs because of complex mappings and multi application connectivity that must be continually maintained.

These longstanding integration problems are becoming more urgent as enterprises embed artificial intelligence into everyday workflows. Systems are now expected to move much larger volumes of data, at higher speeds, and with tighter coordination than earlier architectures were designed to support. As organizations prepare for an artificial intelligence powered future, including generative artificial intelligence, machine learning, and agentic artificial intelligence, they are recognizing that the way data travels across the business is as critical as the insights produced. In response, many are moving away from scattered integration tools toward consolidated, end to end platforms that rationalize connections, improve control, and provide a more sustainable foundation for artificial intelligence enabled operations.

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