Strategizing AI-powered middleware system design for Human Resources
Data Management
Abstract
The growing adoption of iPaaS (Integration Platform as a Service)
solutions in organisations has led to an increased need for efficient
and tailored middleware systems to manage the various data types,
including various use cases of artificial intelligence and automation.
While many iPaaS solutions offer similar core utilities, the differences
in configuration options, the availability of connectors, range of
features and the ease-of-use can greatly impact their efficacy while
handling specific types of data. Most iPaaS solutions try to fit the
one-size-fits-all model so that all kinds of data can be manipulated
through a single iPaaS medium. Differences in data types poses a
limitation to such a model. This paper aims to explore the challenges
faced during best practices of the current middleware systems focussing
on HR (Human Resources) data, as well as potential AI applications in
the design of the iPaaS. The study also highlights the importance of
considering factors such as data security, data governance, and user
friendliness when selecting an iPaaS solution for HR data management and
possible AI-driven strategies.