Why embedded iPaaS solutions fail to support AI-powered product features

Embedded integration platform as a service (iPaaS) solutions often fail to support AI-powered product features due to their inability to scale across a customer base, normalize integrated data, and provide adequate integration observability features. This can lead to inaccurate outputs, limited performance, and a poor user experience. In contrast, a unified API solution like Merge can address these challenges by allowing for hundreds of cross-category integrations, normalizing integrated data, and providing robust integration observability features. By using such a solution, businesses can power best-in-class AI features and improve their overall product performance. The limitations of embedded iPaaS solutions can be significant, requiring engineers to spend considerable time configuring each integration for every customer, which can come at the expense of building and improving core product features. Furthermore, the lack of normalization and observability features in embedded iPaaS solutions can result in inconsistent vectors, incorrect outputs, and limited AI feature performance.

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