Artificial Intelligence (AI) is poised to disrupt our world. With intelligent machines enabling high-level cognitive processes like thinking, perceiving, learning, problem-solving, and decision-making, coupled with advances in data collection and aggregation, analytics, and computer processing power, AI presents opportunities to complement and supplement human intelligence and enrich the way people live and work.
India, being the fastest-growing economy with the second largest population in the world, has a significant stake in the AI revolution. When it comes to HR, AI has the potential for an enormous impact.
Learning styles of people can be quite unique, and with diverse generations in the workforce, embracing modern training practices, AI is helping to personalize corporate learning, by capturing meaningful employee data relating to a wide range of learning experiences and behaviors.
The same machine learning computer algorithms that “learn and recommend” by analyzing choices of where to shop or what to eat, now “learn and recommend” when it comes to employee training. In fact, these systems will continue to parse and analyze as more and more employee interactions occur, and be able to tweak training programs accordingly, making training more efficient, and training outcomes more effective.
AI is poised to be a game-changer when it comes to workflow problems. The next few years should see software that automates certain employee lifecycle processes that include hiring processes like interview scheduling, employee performance reviews, employee onboarding, answering basic HR questions, and handling operational requirements.
This is one of the functions where we will see the impact of AI relatively faster. Running queries on resumes during bulk hiring, matching resumes to job descriptions, and presenting a quick snapshot of the resume… all of these will create a lot of process efficiency.
The software driving natural language processes and predictive language analysis will help speed up recruitment by allowing the TA team to weed out irrelevant resumes faster and with fewer mistakes.
Predicting future turnover rates, reduced (or increased) employee engagement levels, concerns about internal employee communications, project completion problems, and any other unexpected hidden issues that would usually take years to surface, can be brought up at the decision-making level, by AI. This can certainly lead to cost savings and overall enhancement of organizational efficiencies.
Some HR areas where Datamatics has a strong AI footprint
- Datamatics has done an AI-driven CV search to pick up the top 10 matches, based on the requirements for a specific post advertised by a bank. The bank receives thousands of CVs in response to specific posts advertised. The AI-enabled system takes inputs for required features in text form for a post and then objectively/semantically searches them in the CVs to rank them in order of preference or suitability of CVs for the specific post.
- Datamatics has been involved in managing human resource requirements of large ocean engineering projects (deep sea exploration, oil rigging platforms, etc). Key elements of the Project Plan for engineering activities, machines, and operational requirements, timelines were extracted and the right resources (from HR records) were mobilised based on the best match and availability. The goal was to achieve optimum performance for a specific project – right resource mobilisation, finding out redundant skills, skills in high demand and short supply, Cross skilling
- Email Analytics: Datamatics has developed and deployed a customer feedback analysis system, where the system reads all emails to look for appreciation, concerns, or complaints in their communication. The goal is to capture such sentiments, classify, perform root causes, and create a plan for improvement. Such a system can be extended to employee emails as well to look for specific patterns of concern. Internally, climate survey exercise uses this tool to analyze employee sentiment.
- Other areas include employee turnover prediction with artificial neural networks, HR sentiment analysis with text mining, Employee self-service with interactive voice response – Chat Bots for FAQs
There are also success stories in multiple industry domains
- Enhancing customer experience for banks – Auto-analysis and auto-routing of service request emails to concerned departments for quick response
- Document management for banks – Auto-analysis of large documents coming in a queue to classify and retrieve key information for easy access and searchability.
- Customisable platform for ESG ratings and analytics – Users can see all relevant ESG data on a specific company at the click of a button
- Optimising Sales for consumer products companies – Implemented TruAI to show the price variation and analyze the future price trends on different brands
- Developing efficient self-learning fraud detection tool – Implemented TruAI Pattern that identified high-risk accounts and placed them on a watch list to monitor their transactions. The tool periodically scans for anomalies in the data and identifies transaction patterns that are potentially fraudulent
- Accelerate customer conversion for travel and tourism companies – Implemented TruAI to help collect, prepare, and manage data in an efficient manner and derive conversion patterns and actionable insights from the data
- Viewership predictions and segmentation for a leading broadcasting company – Customer demographic, statement, and channel play-out data were integrated and reconciled using powerful and automated Big data algorithms
- Optimize business operations by Identifying anomalies in highly skewed data – Implemented TruAI to help collect, prepare, and manage data in an efficient manner and derive anomalies and actionable insights from the data
- Applicant verification process for Loan approval/rejection has become a tedious data-intensive process – RPA-based automatic data scraping from the data sources that include both named sources like Dow Jones and unnamed external web sources (from search engines) – AI helped save the scraped data, extract text using techniques like Topic Modeling and Named Entity extraction to name a few, make a semantic analysis of the keywords to be searched for in addition to the lexicon analysis to ensure the search results are more deterministic and accurate, prepare an automated report of the credit worthiness of the loan application based on the data analysis. So, no manual intervention is needed.
The pace of progress has been incredibly rapid, and to stay ahead of the curve, organisations would have to adapt fast. Change is, ultimately, the only constant.