[Excerpt] While organizations agree with the importance of AI, only 31% are ready to embrace or have already applied it to their HR process. There are varying levels of acceptance for AI across the HR function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee management decisions (31%), analysis of workplace policies (24%), and automation of tasks previously performed by humans (22%)

Joh, Eunice Eunju

White, William Blake

English

DigitalCommons@ILR

Cornell University ILR School 
DigitalCommons@ILR 
Student Works ILR Collection 
11-2018 
How We Can Apply AI, and Deep Learning to our HR Functional 
Transformation and Core Talent Processes? 
Eunice Eunju Joh 
Cornell University 
William Blake White 
Cornell University 
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How We Can Apply AI, and Deep Learning to our HR Functional Transformation 
and Core Talent Processes? 
Abstract 
[Excerpt] While organizations agree with the importance of AI, only 31% are ready to embrace or have 
already applied it to their HR process. There are varying levels of acceptance for AI across the HR 
function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee 
management decisions (31%), analysis of workplace policies (24%), and automation of tasks previously 
performed by humans (22%). 
Keywords 
human resources, artificial intelligence, AI, robotics, functional transformation, machine learning, deep 
learning, deep neural networks, robotic process automation, chatbots, transactional HR, HR operations, 
talent acquisition, training and development 
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Eunice Eunju Joh        EXECUTIVE SUMMARY   November 2018 
William Blake White      
1 
Research Question 
How we can apply AI, and deep learning to our HR functional transformation and core talent 
processes? 
Background 
While organizations agree with the importance of AI, only 31% are ready to embrace or have 
already applied it to their HR process. There are varying levels of acceptance for AI across the HR 
function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee 
management decisions (31%), analysis of workplace policies (24%), and automation of tasks 
previously performed by humans (22%). (see Appendices A&B) 
Terminology 
First, it is important to understand the many different concepts that fall under the umbrella of 
Artificial Intelligence (AI). AI is an area of computer science where computers are “developed” 
to behave much the way humans do. Machine learning is the science of getting computers to 
“learn and act” like humans, improving learning by amassing and analyzing actual interactions. 
Deep learning, which is regarded as the highest level of AI today, is advanced machine learning 
that “teaches” computers to learn through deep neural networks (DNNs). RPA (Robotic Process 
Automation) is “bot” software which assists with basic, high-volume, repetitive, and rule-based 
tasks. RPAs often improve the accuracy and speed of data processing. RPAs are ‘taught’ by 
humans and can assist in automating back office tasks. Chatbots can be considered as a 
conversational or more tangible version of AI which has potential that can be widely used 
throughout an HR function e.g. answering FAQs on company or HR policies. 
The application of Artificial Intelligence to the HR function 
All organizations, regardless of mission or purpose, seek to become more effective and efficient. 
The utilization of simple rule engines such as RPAs to complex cognitive computing is radically 
reshaping how HR departments function. 
Robotic Process Automation (RPA) Utilization for Transactional HR 
RPA can assist companies in streamlining repetitive tasks such as benefits enrollment and 
payroll, employee data management, and the onboarding process. Typical tasks for RPAs 
include: collecting data from XLS files or systems, running reports, copying data, checking data 
for completeness, reading, processing and submitting emails, entering data in HR or Payroll 
systems and pre-populating forms. Savings can be substantial with cost reductions of 50% to 
70% on average.1
Chatbot Innovations in HR Operations 
Chatbots are commonly used to address the common questions relating to company or HR policy 
that waste time for HR professionals and hamper productivity. However, many companies are 
becoming more innovative and beginning to utilize chatbots in combination with deep learning 
 
 
 
2 
  
and RPAs to produce high-quality interactions for employees and customers. The Chatbot presents 
a friendly text or voice interface to the user while interactions will trigger a RPA that connects to 
back-end applications, retrieves customer data, and executes specific tasks for the user. 
Firms such as NICE Ltd. are leveraging machine learning and big data to predict customer 
demands and enable chatbots to proactively communicate with customers.2 By analyzing, 
interpreting, and understanding large volumes of customer requests, the solution could pitch 
healthcare options to an employee based on his or her background, while RPA robots auto-fill the 
application form to save the employee time.3 
  
Integrating AI and Talent Processes 
Talent Acquisition 
AI is enabling passive candidate sourcing by identifying likely candidates from verified data 
sources. Using recruiter-defined parameters (e.g. location, skill set or industry), AI can return lists 
of potential candidates, predict their readiness for a move, develop and send communications, and 
map responses.4 For example, IBM has created a suite of tools to help candidates personalize their 
job search experience based on the engagement they have with Watson. However, there are clear 
downsides to AI filtering candidates. Amazon recently scrapped an AI driven recruiting engine as 
the tool as algorithms began to show bias against resumes of female candidates.5 
 
Training & Development 
Companies on the cutting edge of AI implementation are using AI to create or adapt training 
programs that fit company and employee needs. Training platforms powered by machine 
learning can analyze an employee’s background, their current role, and career aspirations to 
recommend relevant training programs through services such as Lynda or Udemy.  Learner-
centric training, made more efficient through AI systems, can lead to increased engagement and 
retention.6  
 
Other Applications 
Predictive analytics which relies on human interaction to query data, validate patterns, and test 
assumptions. Machine learning can bring prediction to the next level. When data previously 
validated by data scientists is constantly updated and validated using AI, predictive models 
iterate more frequently and keep HR on the cutting edge of employee insights. More information 
on alternative trends for AI and Deep Learning in HR can be found in “Additional Background 
Research & Trends”. 
 
Conclusion 
As technology becomes more advanced, HR departments should seek to innovate in how RPAs, 
Chatbots, AI/Machine Learning (ML), and Deep Learning (DL) are integrated into day-to-day 
HR operations. HR departments should seek the help of knowledgeable external consultants and 
acquire key talent such as ML/DL engineers to fully understand how the aforementioned 
capabilities can best aid their operations and the organization they serve. 
 
 
 
3 
  
 References  
1 Groothuis, Hans, et al. “Robotic Process Automation for HR & Payroll.” EY.com, Ernst &  
  Young, 2016, www.ey.com/Publication/vwLUAssets/EY-robotic-process-automation- 
  for-hr-and-payroll/$FILE/EY-robotic-process-automation-for-hr-and-payroll.pdf. 
2 Gurwitz, Catherine. “Chatbots & Robotic Automation – A Match Made in Heaven.” NICE  
  Systems, 22 Nov. 2017, www.nice.com/engage/blog/Chatbots--Robotic-Automation--A 
 -Match-Made-in-Heaven-2315. 
3 Karev, Oded. “RPA and Chatbots - The Future of Customer Self-Service.” RPA and Chatbots -    
  The Future of Customer Self-Service - Smart Customer Service, 26 Mar. 2018,  
  www.smartcustomerservice.com/Columns/Vendor-Views/RPA-and-Chatbots-The-Future 
 -of-Customer-Self-Service-124069.aspx. 
4 Hrd. “Is AI the New HR?” HR Online, Aug. 2018, www.hcamag.com/features/is-ai-the-new- 
  hr-253660.apx 
5 Dastin, Jeffrey. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias against   
            Women.” Reuters, Thomson Reuters, 10 Oct. 2018, www.reuters.com/article/us-amazon- 
  Com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias- 
  against-women-idUSKCN1MK08G. 
6 Singh, Dakshdeep. “With AI & ML Blending in New Age HCM Systems, How Can  
          Organizations Increase Business Productivity with Big Data?” PeopleStrong, 20 Feb.  
          2018, www.peoplestrong.com/with-ai-ml-blending-in-new-age-hcm-systems-how-can- 
          organizations-increase-business-productivity-with-big-data/. 
7 Meister, Jeanne. “AI Plus Human Intelligence Is The Future Of Work.” Forbes, Forbes   
   Magazine, 21 June 2018, www.forbes.com/sites/jeannemeister/2018/01/11/ai-plus-human 
  -intelligence-is-the-future-of-work/#421a06a42bba. 
8 “The New Age: Artificial Intelligence for Human Resource Opportunities and Function.”  
  EY.com, Ernst & Young, 2018, www.ey.com/Publication/vwLUAssets/EY-the-new-age- 
  artificial-intelligence-for-human-resource-opportunities-and-functions/$FILE/EY-the- 
  new-age-artificial-Intelligence-for-human-resource-opportunities-and-functions.pdf. 
9 Charlier, Robert, and Sander Kloppenburg. “Artificial Intelligence in HR: a No-Brainer.”  
  Pwc.at, PricewaterhouseCoopers, www.pwc.at/de/publikationen/verschiedenes/ 
 Artificial-intelligence-in-hr-a-no-brainer.pdf. 
10 Agarwal, Dimple, et al. “AI, Robotics, and Automation: Put Humans in the Loop.” Deloitte  
  Insights, 28 Mar. 2018, www2.deloitte.com/insights/us/en/focus/human-capital-trends/ 
  2018/ai-robotics-intelligent-machines.html. 
11 “The Littler Annual Employer Survey 2018.” Littler Mendelson P.C., 2 May 2018, 
  www.littler.com/publication-press/publication/littler-annual-employer-survey-2018. 
 
 
 
 
 
 
 
 
 
4 
  
 
Additional Background Research & Trends  
 
Tips to bring AI & ML innovations to the HR function: 
1. Identify which roles on your team will be transformed by AI or inventing new role.6 
2. Capitalize on the digitization and datafication of the HR function. Keeping and collecting 
corrected data are keys in this process of continuous change. 
3. Hack HR to identify new HR solutions. Jet.com recently used to a hackathon to improve 
the candidate experience by bring HR hiring leaders together with software designers and 
graphic designers to create new candidate process. 
4. Every major human capital management cloud provider is now implementing algorithms, 
making it important for organizations to maintain accurate data and carefully review these 
tools for accuracy and potential bias.   
5. The success of any organization depends on how effectively it combines people, process 
and technology intelligently to deliver transformational value at optimized cost. AI will 
help to efficiently automate many back-office functions for reliable HR transactions and 
service delivery. 
6. Increasing the digital IQ and EQ through algorithm’s learning themselves enables AI to 
deeper step into what is referred to as an “unconscious level” of information. By 
consolidating and comprehensively analyzing people’s statements, mood and intentions on 
social media, along with other public-data sources, human behavior can be simulated by 
autonomously learning machines. This makes it possible to validate the employee 
experience on a day-to-day basis. HR performance and succession data provide 
information on which employees are engaged and challenged. That gives a new dimension 
to strategic workforce planning to reduce employee attrition. It is a helpful tool to find the 
right mix of man and machine in the workplace, which skills and talents are key to maintain 
balance, and the best-fit candidates for the internal or external hiring process.9 
7. AI helps employees to navigate their careers by creating internal career mobility platform 
using machine learning based on employees’ choice and action. Cisco employee can 
choose their new adventure and career in the company with AI. 
8. Software can now recognize faces and identify gender, listen to voices and identify mood, 
and decode video interviews to identify education level, lying, and cognitive ability. 
Analytics tools are intelligently selecting candidates, identifying employees’ career 
options, and coaching managers on improving their leadership skills. And the potential 
doesn’t end there: AI is even being used to create chatbots that can interact with job 
candidates, identify and score video interviews, and understand the sentiment of 
engagement surveys.10 
 
 
 
 
 
 
5 
  
 
 
 
Appendix A 
 
 
Source : Deloitte Global Human Capital Trends, 2018 
 
 
 
 
 
 
 
 
6 
  
 
Appendix B 
 
Source : THE LITTLER® ANNUAL EMPLOYER SURVEY MAY 2018 11 
 


How We Can Apply AI, and Deep Learning to our HR Functional Transformation and Core Talent Processes?

[Excerpt] While organizations agree with the importance of AI, only 31% are ready to embrace or have already applied it to their HR process. There are varying levels of acceptance for AI across the HR function. Top areas of implementation are: recruiting and hiring (49%), HR strategy and employee management decisions (31%), analysis of workplace policies (24%), and automation of tasks previously performed by humans (22%).How_Can_AI_be_Applied_to_HR_Functional_Transformation_and_Core_Talent_Processes.pdf: 1373 downloads, before Oct. 1, 2020

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