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.

Recommender engines for hiring platforms

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.

Chatbots & virtual assistants

HR departments can complement their managers with NLP-powered bots in various stages of the hiring and onboarding process to improve candidate engagement and recruiting experience. For instance, chatbots can collect information from job applicants via human-like interactions instead of using traditional surveys and questionnaires, or answer their questions and provide them with further details on open positions.

Employee benefits & well-being management

AI-powered solutions help companies monitor employee workload for effective shift scheduling, provide personalized benefits, like health insurance options or retirement plans, and analyze the turnover rate to optimize talent retention and churn prevention strategies. This results in a more pleasant work environment and superior employee satisfaction.

HR administration

From providing credentials to new hires during onboarding to managing payrolls, leave applications and expense reimbursements, AI-enhanced RPA systems can automate a multitude of HR-related clerical tasks, minimizing human mistakes and spent efforts and ensuring compliance.

Personalized career development paths

Companies can use AI-driven adaptive learning solutions to plan tailored professional paths and training initiatives (including personalized elearning content, employee handbooks, and other resources) based on each employee’s current capabilities and skill gaps. This helps foster career progression and internal mobility and prioritize talent redeployment over layoffs.

HR budgeting

Thanks to the AI-based analysis of business outcomes and workforce performance data, organizations can assess the cost-effectiveness of their HR functions to prioritize the most beneficial options. For example, HR managers can evaluate the results of mentoring programs and upskilling initiatives to decide whether to hire new specialists or retrain existing employees for new positions. They can also plan future salaries and bonuses to maximize talent retention while keeping an eye on budget constraints.

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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.

    LinkedIn Recruiter

      Recruiter is a hiring platform by LinkedIn featuring a talent search and recommendation system powered by ML algorithms. This HR solution can compile lists of candidates matching a search request or job posting and rank them based on their expertise, current location, and other factors. It also considers the mutual interest between candidates and recruiters (for instance, if the former positively reply to HR managers' InMail messages).

      1Position, location2Candidates for position & location3Translate into Queries for Ranking6CandidateArm 1 Cand 1
Cand 2
Cand 3Cand 1
Cand 2
Cand 3Arm 2 Cand 1
Cand 2
Cand 3Arm 3 4Choose next arm (intent cluster)5Recommend next candidate from next arm7Response8Update arm parameters utilized for selection of next arm9Re-rank candidates for each arm by updating query weights

      Scheme title: LinkedIn Recruiter’s operation
      Data source: LinkedIn

      Eightfold AI

        Eightfold AI is a deep learning-powered talent intelligence platform covering recruitment processes, upskilling, employee retention, and workforce diversity. This tool automates resume screening and minimizes bias via profile data masking. It can also aggregate employee data and leverage AI to match staff members with the most suitable internal jobs, projects, career paths, and development opportunities based on their performance.


          HireVue is a talent experience platform focusing on recruitment at scale through AI-based video interviewing. The system provides candidates with a list of questions to be answered autonomously via video. Then, it transcribes the answers and issues a score based on the use of keywords, tone of voice, and other parameters, combined with a game-based test and a technical skills assessment to rank the applicants.


          Image title: HireVue’s interface
          Image source: — Professional hiring software

          Phenom AI

            Phenom AI is a comprehensive tool leveraging ML to enhance talent acquisition, employee development, and HR administration. The platform can automate routine tasks, such as candidate screening and scheduling, and provide job seekers with recommendations tailored to their interests and browsing history. It also relies on data analytics to help hiring managers identify suitable candidates based on an AI-generated fit score.

            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 model reliability

            While AI can certainly lead to better decisions based on data-driven insights, no algorithm is 100% accurate, and the models they generate to make analyses and predictions can still suffer from bias. For instance, some recruiting engines have been shown to disadvantage female candidates in the past.

            of organizations using artificial intelligence in HR have experienced AI systems accidentally overlooking or excluding qualified applicants


            Train AI algorithms on reliable, high-volume datasets from selected sources, including properly integrated corporate systems.

            Consider using built-in algorithms and pre-trained AI models provided by cloud platforms and services, such as Amazon SageMaker or Azure Machine Learning.

            Divide the data used in the modeling phase into training, validation, and test sets to address overfitting (when the model was overtrained on specific data and performed poorly with other sets).

            After deployment, perform multiple retraining iterations according to MLOps' best practices to fine-tune the model with new data and mitigate model drift (progressive changes in input data and related variables that reduce model performance).

            Security & compliance

            AI-based HR software solutions typically handle a fair amount of sensitive data, including employees' personal information and financial reports. This makes them ideal targets for hacking, breaches, or leaks. Integrations with other systems multiply the potential points of vulnerability, aggravating cybersecurity and compliance issues.

            of enterprises surveyed identify cybersecurity as a major concern preventing them from adopting AI and other disruptive technologies in HR


            Use obfuscated data without sensitive information in all cases where it’s possible.

            Deploy HR software designed in strict compliance with applicable data management standards and regulations, such as GDPR, FISMA, and HIPAA.

            Protect your software and corporate data assets with a range of cybersecurity features encompassing, for instance, encrypted data exchange via cryptographic protocols, identity and access management based on a zero-trust approach, multi-factor authentication, and user activity monitoring.

            Adopt company policies for data governance establishing how data should be managed and shared across your organization.

            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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            What is the future of AI in HR?

            In the near future, according to a report by Eightfold AI, the HR managers surveyed expect to use AI technology in a wide range of functions. These will include upskilling and reskilling (30.8%), workforce equity and inclusion (30.4%), turnover and retention (27.2%), talent acquisition (20.1%), talent redeployment (16.1%), diversity in hiring (15.6%), and more. Furthermore, McKinsey highlighted the growing role of generative AI in writing job descriptions and requirements, powering chatbots, and preparing performance reviews.

            Will HR specialists be replaced by AI?

            McKinsey pointed out that while AI tools may not completely replace HR managers, they are likely to reshape their job. If supported with proper retraining initiatives, HR professionals will progressively shift from administrative duties to employee-facing roles and supervision tasks (the so-called "human-in-the-loop" model).

            What are the benefits of AI in recruiting?

            According to a study by the SHRM, surveyed organizations use AI in human resource management as it increases recruiting efficiency (85%), improves their ability to identify top candidates (44%), and reduces potential bias in hiring decisions (30%).

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