Transcending AI Apathy: Why All HR Professionals Should be Gaining Ground with Artificial Intelligence
While Artificial Intelligence (AI) is transforming industries across the globe, many HR departments at companies both large and small don’t fully comprehend how this next-gen technology can benefit their own operation. While AI is often generally thought of in terms of customer service chatbots and customized marketing, the reality is that its potential to benefit other key aspects of a business operation is seemingly boundless. This indubitably includes Human Resources and staffing.
Data management and data analytics play a critical role in making HR departments more efficient and driving growth for the company at large. Those doing it well by leveraging AI in the HR function can gain actionable insights and efficiencies that result in a significant informational edge. If your HR department isn’t fully integrating AI into its strategy, it’s time to rethink your approach to digital transformation and unlock the powerful opportunities AI has to offer.
Natural Language Processing
When it comes to natural language processing (NLP), each HR department has unique requirements that must be thoughtfully considered to get the most out of your data. While uses for NLP greatly, some examples of highly useful applications include the following:
• Sentiment Analysis
These tools can help you understand the emotions and opinions expressed in text data. Understanding the sentiment in data can be used to monitor and analyze feedback, as well as to understand social media trends.
• Document Processing
This can analyze text, provide semantic search or semantic reasoning, and automate the tedious and time-consuming tasks of manual data entry and processing. Document processing tools can extract information from various document types, including PDFs, images, and scanned documents resumes and staffing reports.
• Chatbots and Virtual Assistants
These tools can interact with your workforce and provide them with the necessary information. Chatbots and virtual assistants can be used to handle queries, solve issues, provide recommendations, and much more.
• Text Classification
Text classification and topic modeling solutions automatically classify and organize your text data. It involves the process of automatically assigning texts to one or more predefined categories and can be used to sort emails, articles, staff feedback, and other types of text data.
• Entity Recognition & Extraction
Use this to identify and extract entities from text data. Entity recognition is a process of identifying and classifying named entities in text and can be used for advanced text extraction and text mining tasks.
• Natural Language Generation
These solutions can automatically generate text from data and can be used to create reports and provide text summaries, descriptions, and other types of text data.
Predictive Analytics
Predictive analytics is a field of data science that uses historical data and machine learning algorithms to make predictions about future events and unlock the value in data. This technology is used across industries for various applications, such as identifying trends, predicting hiring surges and shortfalls, and making decisions about hiring strategies.
• Demand Prediction
Predictive models for demand prediction can help you forecast future demand for your job opportunities and make better decisions about employer branding and hiring strategies.
• Churn Prediction
Custom predictive models can help you identify which employees are at risk of quitting their job. With this information, you can target at-risk staffers with personalized offers and benefits to entice them to stay.
• Predictive Maintenance
Machine learning (ML)-driven predictive maintenance solutions can detect potential operational equipment, hardware and software failures before they happen. By using predictive analytics, you can avoid costly downtime and improve the efficiency of your HR operations.
• Predictive Manufacturing
Predictive models can also optimize protocols and processes, predict yield, and identify problematic issues. With predictive analytics solutions, you can improve the efficiency of your operations.
Computer Vision
Custom image and video analysis solutions can be tailor-built to your business’s unique needs. Sophisticated algorithms and data processing techniques can help you gain deeper insights into your data and optimize your processes for maximum efficiency. Some examples of computer vision applications include:
• Image Analysis and Segmentation
Algorithms for image analysis and segmentation can help automatically detect and classify entities in images, extract specific features from images, and develop biometrics systems for facial recognition.
• Object Detection, Tracking, and Labeling
Custom computer vision solutions can automatically detect, track, and label objects in images and videos. Object tracking can help businesses identify and analyze the movement of specific targets over time to aid their surveillance capabilities, build intelligent systems for activity recognition or develop traffic monitoring systems.
• Visual Search
Visual search capabilities can power recommendation engines, search engines, and various types of catalogs. Using deep learning techniques, image recognition models can be trained to accurately identify unique images or even search through large databases to find similar objects based on visual similarity.
• Intelligent Text Recognition
Intelligent character recognition (ICR) solutions can automatically recognize text or handwritten characters in images and videos. This can be used to develop systems for intelligent document processing automation tools, to build optical character recognition (OCR) engines for scanned documents, or even to search for specific text in videos.
• Image Generation with GANs
Using Generative Adversarial Networks (GANs) to develop custom image generators is helpful for producing unique images or even realistic-looking video sequences. Various applications include data augmentation, art generation, marketing automation, and more.
Recommendation Systems
Tailored recommendation systems can help you better understand customer preferences and suggest the best products or services for them. Based on deep learning algorithms and employing natural language processing, image recognition and other machine learning techniques can deliver highly accurate recommendations you can gain actionable insights into customer behavior and create targeted campaigns to boost customer engagement and satisfaction.
• Recruitment
Intelligent recruitment and job-pairing solutions can help employers find the best candidate for a job. The system works by analyzing the profile of the job and the profile and resumes of the potential candidates, then uses machine learning algorithms to identify the best potential matches for the job based on experience and qualifications. With a powerful recommendation system, employers can quickly and easily identify the most qualified candidates for a job.
• News and Information Delivery
Recommendation system frameworks can apply data from previous user engagements to learn each employee’s preferences and interests, and tailor engagement opportunities to meet the preferences of each unique user. Viewers can be best enabled to quickly find relevant and engaging content curated specifically to their profile. Additionally, systems can provide insights into user behavior, preferences, and trends so that entities can better target their audience.
• Online Shopping
Improve the user experience for shopping the company’s line of corporate-branded products by providing personalized and tailored recommendations based on past user activities and preferences. The system can be integrated into existing online shopping systems and use data mining and machine learning algorithms to track and analyze user behavior and generate product, service and content recommendations. The system can also be used to display personalized offers and promotions to users, boosting customer engagement and increasing sales.
Data Management and Analytics
Data has become the biggest asset for any business and. Some of a company’s most pressing and strategic questions can often be answered with the data, itself. A robust data strategy and road map includes rapid prototyping and tool evaluation, data acquisition planning, data quality measurement and an understanding of data compliance needs.
• Data Transformation and Connectivity
Considerations include data scraping ad extraction, data matching across systems, scalable data processing and storage as well as extract-transform-load functions. When the latest technologies, such as AI, ML and Deep Learning are applied on data available from internal or external sources, a powerhouse for strategic decision-making is created.
• Data Analytics Strategy
The approach is often multifaceted with components including platform selection, AI/ML model application, comprehensive dashboard delivery for decision-making, and product development considerations like cloud data/AI platforms as well as automated data integration. Artificial Intelligence and analytics play a vital role in making organizations more efficient, bringing visibility, and accelerating growth. With it, organizations can gain an informational edge using data and actionable insights.
Digital transformation is top of mind for all businesses, from start-ups to Fortune 500 groups. Technology continues to advance at a breakneck pace, as innovations such as Blockchain, Artificial Intelligence, Machine Learning, Deep Learning, Advanced Analytics, Internet of Things, Talk Technology, Virtual Reality, and Augmented Reality continuously make new waves. Thus, it is critical for HR leaders to define a technology roadmap that meets overarching business goals—one that improves the department’s operational productivity through workflow digitization, process automation, and enterprise integration. One that helps you understand disruptive HR technology trends and recommends opportunities to help you gain that elusive competitive edge.
Data scientist Angel Vossough, CEO and Co-Founder of BetterAI, leads the creation of innovative AI and other nextgen tech solutions for other organizations and her own, including the “VinoVoss”—an AI Sommelier smartphone app and web-based semantic wine search and recommendation system. A serial entrepreneur with a deep tech background, Angel holds dual Bachelor’s in Mathematics and Computer Engineering and a Master’s with honors in Software Engineering and Data Science from UC Berkeley. Angel’s diverse experience includes roles at Cisco Systems, DiverseUp, and Caspian Capital. Connect with Angel at
Thank you,
Glenda, Charlie and David cates
