Consolidating systems for AI with iPaaS

For CIOs and technology leaders, the key question is no longer whether to modernise integration, but who owns the integration standard and how aggressively it should be enforced. An iPaaS consolidation move is not just a tooling choice; it is a governance decision about common patterns, approved connectors, data movement controls, and the degree to which business units may keep their own โ€œshadowโ€ integrations.

The management trade-off is clear: consolidation can reduce duplicated effort, improve visibility, and make AI initiatives easier to operationalise, but it also concentrates dependency in a smaller number of platforms and teams. That raises the bar for platform reliability, vendor oversight, and internal capability. Leaders should ask whether the organisation is prepared to run integration as a product with service levels, roadmaps, and demand intake, rather than as a collection of project-by-project fixes.

For portfolio leaders, the real test is sequencing. AI programmes often fail when they are funded as model experiments while the data and process plumbing remains fragmented. A useful next question is which integration work should be treated as foundational to the AI roadmap versus which legacy interfaces can safely remain in place for now. The answer should be guided by business criticality, process volume, and the cost of delay, not by architecture preference alone.

There is also a resilience angle. More standardisation can improve monitoring, troubleshooting, and compliance, but only if organisations define clear controls for exception handling, access management, and change approval. Before expanding AI use cases, executives should pressure-test three things: which integrations are strategic chokepoints, who can authorise new connections, and how technical debt will be retired without disrupting operations. If those questions are not answered, consolidation may simply replace one form of sprawl with another.

For decades, enterprises reacted to shifting business pressures with stopgap technology solutions. To rein in rising infrastructure costs, they adopted cloud services that could scale on demand. When customers shifted their lives onto smartphones, companies rolled out mobile apps to keep pace. And when businesses began needing real-time visibility into factories and stockrooms, they layered on IoT systems to supply those insights.   Each new plug-in or platform promised better, more efficient operations. And individually, many delivered. But as more and more solutions stacked up, IT teams had to string together a tangled web to connect themโ€”less an IT ecosystem and more of a make-do collection of ad-hoc workarounds. That reality has led to bottlenecks and maintenance burdens, and the impact is showing up in 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 havenโ€™t delivered as expected. Achim Kraiss, chief product officer of SAP Integration Suite, elaborates on the wide-ranging problems inherent in patchwork IT: โ€œA fragmented landscape makes it difficult to see and control end-to-end business processes,โ€ he explains. โ€œMonitoring, troubleshooting, and governance all suffer. Costs go up because of all the complex mappings and multi-application connectivity you have to maintain.โ€   These challenges take on new significance as enterprises look to adopt AI. As AI becomes embedded in everyday workflows, systems are suddenly expected to move far larger volumes of data, at higher speeds, and with tighter coordination than yesterdayโ€™s architectures were built
to sustain. As companies now prepare for an AI-powered future, whether that is generative AI, machine learning, or agentic AI, many are realizing that the way data moves through their business matters just as much as the insights it generates. 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. Download the report. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Reviewโ€™s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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