*The Essential AI and ML Glossary for HR Leaders and Business Partners*
By Blaise RadleyStaff Writer, Workday
For organizations to remain competitive in this new world of work, it’s critical that HR leaders understand AI and its value. That’s why we’ve compiled 10 AI terms we believe every HR leader should know.
In our report “AI IQ: Insights on Artificial Intelligence in the Enterprise,” 1,000 senior-level decision-makers were surveyed about AI and machine learning (ML), with 81% agreeing that AI is required to keep their business competitive. Despite that, 74% of leaders say that their organization lacks the skills to fully implement AI and ML.
For HR, AI is at the heart of major changes concerning skills and the employee experience. Whether in recruiting, talent management, internal mobility, or corporate planning, a skills-based approach is required to transform the way businesses manage talent. As HR continues to shift to a skills-based economy, the necessity of AI will only become clearer.
To enable a successful and responsible company-wide deployment, HR leaders must ensure that they’re well informed about AI and its advantages. Organizations that are slow to adopt AI won’t only lose their competitive edge, but they’ll also get left behind entirely. The AI thought leaders of tomorrow will be those who master the basics today.
For HR, AI is at the heart of major changes concerning skills and the employee experience.
AI terminology can often be highly technical, making AI expertise incredibly valuable. The AI glossary below focuses on the essential terms.
In addition, we’ve included an explanation of each term’s relevance to a successful HR organization. Given the breadth of possible AI applications, it’s easy to lose track of the potential benefits at a corporate level. That’s why we’ve focused on what makes AI a critical part of the HR team of the future.
Artificial intelligence is the ability for machines to perform tasks traditionally seen as requiring human intelligence. AI analyzes and learns from data, recognizes patterns, and makes predictions. By performing these tasks at greater speed and scale, AI will enhance intelligent decision-making and human productivity.
This 2022 survey of senior data scientists and technology executives found that 92% of large companies reported returns on their AI investments. That’s up markedly from 48% in 2017—a sign that the business value AI represents is massively on the rise.
Machine learning (ML) is a sub-discipline of AI that, as the name suggests, enables machines to learn through repetition. Machine learning algorithms rely on data and self-modifying methods to identify patterns and make predictions. Machine learning models can then constantly refine themselves to generate stronger pattern recognition and predictive analytics.
ML is essential in analyzing skills and understanding their relationship to each other, and then mapping them to a skills-centric workforce at scale. Any attempt to run a skills-based HR without ML will result in high‑cost, time-consuming manual efforts with an incomplete understanding of your workforce.
Responsible AI refers to the idea that AI deployers have a responsibility to ensure that AI systems are developed and used ethically. For AI and ML to be responsible, we believe trust must be designed into them—and expected. This is why we are committed to the ethical, transparent, and accountable use of AI at Workday. You may also hear people refer to “trustworthy AI,” defined by the National Institute of Standards and Technology (NIST) as follows:
“Valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy enhanced, and fair with harmful bias managed.”
Our report “AI IQ: Insights on Artificial Intelligence in the Enterprise” shows that only 29% of senior business leaders are very confident that AI and ML is currently applied ethically. Decision-makers must prioritize partnering with companies that are committed to the ethical and responsible use of AI.
Deep learning is a subset of machine learning that is commonly used to model complex patterns and relationships within data sets. Mirroring our brain’s networks of neurons, deep learning uses multiple layers of processing to analyze large amounts of information. This is particularly useful in enabling computer vision, the process by which machines decode visual imagery.
For HR teams at enterprise companies, the ability to process large amounts of data swiftly is crucial. Whether it’s utilized in creating a comprehensive skills taxonomy, tracking employee data from onboarding to exit, or the categorization and administration of employee benefits, deep learning will have a major impact on the quality of your employee experience.
Natural language processing (NLP) enables machines to understand, interpret, and generate human language. It’s mostly applied for speech recognition, machine translation, sentiment analysis, and responding to questions. NLP also includes two further subfields:
Natural language understanding (NLU) focuses on understanding human language and its intended meaning, factoring in grammatical errors, and so on
Natural language generation (NLG) focuses on turning structured data into language that appears as if it were created by a human
As the pace of work continues to accelerate, it’s essential that businesses are able to measure employee sentiment accurately. NLP enables HR leaders to efficiently sort through vast amounts of language data and surface relevant employee feedback to inform key priorities.
An algorithm is a computer program written to solve a problem or perform a task. Each algorithm contains an automated set of instructions that are triggered when certain parameters are met. Algorithms are at the backbone of the vast majority of computer science fields, as well as AI and ML models.
AI or not, algorithms are behind nearly every major technological advancement of the twenty-first century. As the world of work continues to become more and more data-driven, the utilization of well-written algorithms across traditional HR functions will be what distinguishes success.
Generative AI is a type of AI system that generates new content such as data, images, music, or text. This content is often generated in response to simple user prompts, which has seen generative AI become incredibly popular. Common examples of generative AI include:
ChatGPT: a language processing chatbot capable of generating coherent and realistic human-like language
Stable diffusion: a text-to-image tool that generates detailed images based on text descriptions
Amper music: an AI music platform that generates audio based on the user’s selection of genre and mood
While the most visible examples of generative AI have been consumer-facing, the potential business applications are huge. Working alongside human input, generative AI could create offer letters and job descriptions, and provide strategic decision support, to name a few examples.
Large language models (LLMs) are the underlying technology behind generative AI. LLMs are trained on large quantities of unlabeled text, typically featuring billions of parameters. These can be designed for a variety of machine learning tasks, including:
Search: identifying the intended search versus what the user actually typed
Topic classification: performing data analysis to categorize data or content
Summarization: providing a summary of an entire data set or a specific section
Generative text: generating semantically similar phrases based on existing data
With each year that passes, businesses have to process more and more data. Large language models not only enable data to be processed and analyzed quickly, but they also empower HR professionals to generate valuable insights in real time.
Optical character recognition (OCR) is a form of image recognition that scans images or documents to interpret text and numerical characters, and then converts the image or document into a machine‑readable text format. Most systems that do image recognition leverage deep learning, including Workday.
The potential applications for OCR are massive, especially in terms of reducing unnecessary manual workload. Since OCR enables documents to be scanned and processed in real time, HR teams can focus on the bigger strategic picture rather than on mundane processes.
A neural network is a complex computer system modeled on the way neurons connect and interact within the human brain. Also referred to as an artificial neural network, neural networks are a type of machine learning. By mirroring the data processing style of the human brain, neural networks adapt well to change.
The future of work is adaptive. Neural networks not only surface valuable data insights, but they also identify patterns and learn over time. Embedding ML technology that evolves alongside your company helps to ensure that you remain ahead of the curve.
Thanks to AI, the future of work is already upon us. As the world continues to evolve at a blistering pace, it’s critical that businesses make the right decisions now to safeguard against future changes. HR solutions with AI and ML embedded at the core will be the difference between success and failure.
At Workday, we’ve embedded AI and ML into the very foundation of our platform, enabling our applications to natively leverage AI and ML as part of the workflow. Cutting-edge organizations are already using Workday technology to help:
Deliver better employee experiences
Improve operational efficiencies
Provide insights for faster, data-driven decision‑making
With more than 60 million users on the same version of Workday, only our customers have the trusted finance and people data necessary to realize the business potential of AI. For more information on how Workday can support your HR team in the new world of work, read about our innovations with AI.
