That reality has created bottlenecks and maintenance burdens, and its impact on performance is visible. Today, less than half of CIOs (48%) say their current digital initiatives are meeting or exceeding business outcome goals. Another 2025 survey found that operational leaders point to integration complexity and data quality issues as the top culprits for why investments have not panned out as expected.
Achim Kraus, chief product officer of SAP Integration Suite, elaborates on the broader problems inherent in patchwork IT: “A fragmented landscape makes it difficult to see and control end-to-end business processes,” he explains. “Monitoring, troubleshooting, and governance are all impacted. Costs increase because of all the complex mapping and multi-application connectivity you have to maintain.”
As enterprises consider adopting AI, these challenges are taking on new importance. As AI becomes embedded in everyday workflows, systems are suddenly expected to move large amounts of data at higher speeds and with tighter coordination than yesterday’s architectures.
To sustain
As companies are now preparing for an AI-driven future, whether it’s generative AI, machine learning, or agentic AI, many are realizing that the way data moves through their business matters as much as the insights it produces. As a result, organizations are moving away from scattered integration tools and toward consolidated, end-to-end platforms that restore order and streamline how systems interact.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes writing surveys and collecting data for the surveys. The AI ​​tools that may have been used were limited to secondary production processes that underwent thorough human review.
