Artificial intelligence in HR:
use cases, platforms & adoption challenges

Artificial intelligence in HR: use cases, platforms & adoption challenges

December 20, 2023

6 use cases of AI in HR

HR leaders across all industries can deploy artificial intelligence in a multitude of use cases. Here are some examples.

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Major HR-oriented online platforms and social media feature ML-based recommendation systems that efficiently segment, rank, and suggest potential candidates for an open position based on their experience and skill set, making recruiting faster and cheaper.

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Real-life examples of AI adoption in HR



hours of data gathering and entry in one quarter

IBM adopted an AI-powered automation system called HiRo to help its HR staff perform a variety of time-consuming tasks. For instance, it can collect and analyze data to compile lists of employees eligible for promotion.



in response times to HR queries

A multinational bank partnered with Deloitte to complement its service desk agents with an AI-powered smart assistant. The chatbot can handle common HR help desk queries and even improve its skills by observing how human employees interact.



time spent to process sick leave submissions

Covestro, one of the world's leading suppliers of polymers, opted for an AI-enhanced RPA solution by UiPath to automate its HR processes. The solution uses OCR and ML to extract data from sick leave documents and enter it into corporate systems.

Top AI platforms for HR


    Talenteer is an AI-based internal talent marketplace developed by Itransition to improve the transparency of open positions and facilitate employee redeployment while minimizing staff turnover and external over-hiring. The solution acts as a single point of truth, centralizing employee data (roles, expertise, etc.) and helping HR managers track in-house experts across the internal talent pool based on a set of given parameters.

    Current limitations of AI solutions for HR

    Organizations willing to enhance their HR processes with AI should address various implementation considerations. Consider the following best practices to follow.


    Given the highly data-driven nature of AI, any HR solution leveraging this technology for analytical or operational purposes should be fueled with historical data and ongoing streams of real-time information. This requires an integrated tech ecosystem with multiple types of corporate software capable of exchanging data with each other.

    of companies surveyed consider integration complexity as a key barrier to implementing AI and other disruptive technologies in HR


    Enable communication between your HR software and ERP, LMS, or third-party tools and platforms by setting up suitable APIs. Cloud-based services like Azure API Management or Amazon API Gateway can help with this.

    If the tools to integrate rely on different communication protocols, you may need to build a middleware architecture, such as an enterprise service bus (ESB), to convert them. You can also opt for data virtualization techniques.

    Configure an ETL pipeline to integrate heterogeneous data from multiple sources and consolidate it into a unified data storage, such as a NoSQL database, a time-series database, or a data lake. Cloud data integration tools like AWS Glue and Azure Data Factory can streamline this process.

    AI for a more human approach to HR

    AI for a more human approach to HR

    Often regarded as a depersonalizing force, artificial intelligence in HR has proven capable of amplifying the most exquisitely human facets of our jobs and workplaces. AI-powered solutions can help organizations ensure a fairer career progression, improve employee engagement, create positive working environments, and ease HR managers' bureaucratic burden. At the same time, the architectural complexity, data voracity, and sometimes inscrutable cognitive processes of these systems call for prudent and reasoned implementation. Consider relying on Itransition to maximize the benefits of AI for HR and overcome the challenges of its adoption.

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