*What HR leaders and business partners need to know about Al and ML*
By Blaise Radley, EMEA Staff Writer
With AI and ML transforming everything about not only HR, but the very way we do business, it’s vital that you know what people are talking about when they bring up these new technologies.
AI is embedded in everything we do, whether in our personal or working lives. For organisations 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 ten AI terms we believe every HR leader should know.
In our report ‘AI IQ: Insights on artificial intelligence in the enterprise’, 1,000 senior decision makers were surveyed about AI and ML. 81 percent of leaders agree that AI is required to keep their business competitive. Despite that, 74 percent of leaders say their organisation 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 they're well informed about AI and its advantages. Organisations that are slow to adopt AI won't just lose their competitive edge, they'll get left behind entirely. The AI thought leaders of tomorrow will be those that master the basics today.
The essential AI glossary
AI terminology can often be highly technical, making AI expertise incredibly valuable. In the AI glossary below, we've focused on the essential terms.
In addition, we've included an explanation of each term's relevance to a successful HR organisation. 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.
1. Artificial intelligence (AI)
AI is the ability for machines to perform tasks traditionally seen as requiring human intelligence. AI analyses and learns from data, recognises patterns and makes predictions. By performing these tasks at greater speed and scale, AI will enhance intelligent decision-making and human productivity.
Why it matters: This 2022 survey of senior data scientists and technology executives found 92 percent of large companies reported returns on their AI investments. That’s up markedly from 48 percent in 2017 – a sign that the business value AI represents is massively on the rise.
2. Machine learning (ML)
ML is a sub-discipline of AI that, as the name suggests, enables machines to learn through repetition. ML algorithms rely on data and self-modifying methods to identify patterns and make predictions. ML models can then constantly refine themselves to generate stronger pattern recognition and predictive analytics.
Why it matters: ML is essential in analysing skills, understanding their relationship to each other and 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.
92% of data scientists at large companies reported returns on their AI investments in 2022, up from just 48% in 2017.
3. Responsible AI
Responsible AI refers to the idea that AI deployers have a responsibility to ensure AI systems are developed and used ethically. For AI and ML to be responsible, we believe trust must be designed into it – and expected of it. This is why we’re 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.”
Why it matters: Our report, ‘AI IQ: Insights on artificial intelligence in the enterprise’, shows that only 29 percent of senior business leaders are very confident that AI and ML are currently applied ethically. Decision makers must prioritise partnering with companies that are committed to the ethical and responsible use of AI.
4. Deep learning (DL)
DL is a subset of ML that’s 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 analyse large amounts of information. This is particularly useful in enabling computer vision, the process by which machines decode visual imagery.
Why it matters: For HR teams at enterprise companies, the ability to process high amounts of data swiftly is crucial. Whether it’s utilised in creating a comprehensive skills taxonomy, tracking employee data from onboarding to exit, or the categorisation and administration of employee benefits, deep learning will have a major impact on the quality of your employee experience.
5. Natural language processing (NLP)
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 more.
Natural language generation (NLG) focuses on turning structured data into language that appears as if it was created by a human.
Why it matters: 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.
6. Algorithm
An algorithm is a computer programme written to solve a problem or perform a task. Each algorithm contains an automated set of instructions which 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.
Why it matters: 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 utilisation of well-written algorithms across traditional HR functions will be what distinguishes success.
7. Generative AI
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 that is 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.
Why it matters: 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, job descriptions and provide strategic decision support, to name a few examples.
8. Large language model (LLM)
LLMs are the underlying technology behind generative AI. LLMs are trained on large quantities of unlabelled text, typically featuring billions of parameters. These can be designed for a variety of ML tasks, including:
Search: Identifying the intended search versus what the user actually typed.
Topic classification: Performing data analysis to categorise data or content.
Summarisation: Providing a summary of an entire data set or a specific section.
Generative text: Generating semantically similar phrases based on existing data.
Why it matters: With each year that passes, businesses have to process more and more data. Large language models not only enable data to be processed and analysed quickly, they also empower HR professionals to generate valuable insights in real-time.
9. Optical character recognition (OCR)
OCR is a form of image recognition that scans images or documents to interpret text and numerical characters. That process converts the image or document into a machine-readable text format. Most systems that do image recognition leverage deep learning, including Workday.
Why it matters: 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 will be able to focus on the bigger strategic picture rather than mundane processes.
Thanks to AI, the future of work is already upon us. As the world continues to evolve at a remarkable pace, it's critical that businesses make the right decisions now to safeguard themselves against future changes.
10. Neural network
A neural network is a complex computer system modelled on the way neurons connect and interact in the human brain. Also referred to as an artificial neural network, neural networks are a type of ML. By mirroring the data processing style of the human brain, neural networks adapt well to change.
Why it matters: The future of work is adaptive. Neural networks not only surface valuable data insights, they also identify patterns and learn over time. Embedding ML technology that evolves alongside your company will ensure that you remain ahead of the curve.
The future of work with AI
Thanks to AI, the future of work is already upon us. As the world continues to evolve at a remarkable pace, it's critical that businesses make the right decisions now to safeguard themselves against future changes. HR solutions that have 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. In doing so, we've enabled our applications to natively leverage AI and ML as part of the workflow. Cutting-edge organisations 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 realise 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 at workday.co.uk.
