All posts tagged: iPaaS

Why supply chains are the proving ground for automation‑led iPaaS

Why supply chains are the proving ground for automation‑led iPaaS

Presented by Edgeverve Supply chains are where legacy integration models reach their limits. As partner networks expand and operational volatility increases, traditional middleware is buckling under costs and complexity. That’s why supply chain has emerged as a proving ground for automation‑led integration Platform as a Service (iPaaS), a next-generation model designed to absorb constant change without rewriting the stack. This article takes a look at today’s supply chains, the limits of legacy integration, how automation changes the iPaaS model, possible downsides to an upgrade, and questions leaders should be asking about whether next-gen iPaaS makes sense for them. Why now? Supply chains have outgrown their integration models Supply chains have always been complex. What’s new is the pace of change. Networks now span hundreds of suppliers, logistics providers, and distributors, each running different systems and data standards. At the same time, expectations for real‑time visibility and rapid response continue to rise. The global supply chain visibility software market, which is the problem space that automation-led iPaaS aims to address, was estimated at about $3.3 billion …

Consolidating systems for AI with iPaaS

Consolidating systems for AI with iPaaS

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 haven’t delivered as expected. Achim Kraiss, chief product officer of SAP Integration Suite, elaborates on the wide-ranging 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 all suffer. Costs go up because of all the complex mappings and multi-application connectivity you have to maintain.” 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 yesterday’s architectures were builtto sustain. As companies now prepare for an AI-powered future, whether that is generative AI, machine learning, or …