Consolidating systems for AI with iPaaS

2026-02-05 · Source: MIT Technology Review · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

Enterprise IT infrastructure has become fragmented and brittle due to decades of layering new, often ad-hoc, technology solutions onto existing systems. This approach, driven by evolving business pressures like cloud adoption, mobile app development, and IoT integration, has resulted in a tangled web of connections rather than a cohesive IT ecosystem. This fragmentation leads to bottlenecks, high maintenance burdens, and underperforming digital initiatives, with only 48% of CIOs reporting their current projects meet or exceed business outcome targets. Integration complexity and data quality issues are cited as primary reasons for investment shortfalls. As enterprises prepare for an AI-powered future, these challenges are exacerbated, demanding higher data volumes, speeds, and coordination, pushing organizations towards consolidated, end-to-end integration platforms like iPaaS to streamline system interactions.

Key takeaway

For CTOs and VPs of Engineering assessing their organization's readiness for AI, your fragmented IT landscape is a critical bottleneck. You should prioritize consolidating disparate integration tools into a unified platform like iPaaS to ensure the necessary data velocity and coordination for AI-powered workflows. This move will mitigate performance issues, reduce maintenance costs, and improve the success rate of your digital initiatives.

Key insights

Fragmented IT landscapes hinder AI adoption and business outcomes, necessitating consolidated integration platforms.

Principles

• Integration complexity impedes digital initiative success.
• AI adoption demands robust, high-speed data coordination.

Method

Enterprises are shifting from scattered integration tools to consolidated, end-to-end platforms to streamline system interactions and prepare for AI-driven workflows, improving data movement and governance.

In practice

• Evaluate current integration complexity.
• Prioritize data quality initiatives.
• Explore iPaaS for system consolidation.

Topics

• iPaaS
• Enterprise IT Integration
• AI Adoption
• Fragmented IT Landscapes
• SAP Integration Suite

Best for: CTO, VP of Engineering/Data, Executive, IT Professional, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by MIT Technology Review.