Artificial intelligence is already changing the global corporate landscape. To make sure your business is at the forefront of that change, learn 10 key AI terms and why they matter in our AI glossary.
Blaise Radley
Editorial Strategist
Workday
Artificial intelligence (AI) is embedded in everything we do, whether in our personal or working lives. For organizations to remain competitive in this new world of work, it’s critical that business leaders understand AI and its value. That’s why we’ve compiled the most important AI terms every business leader should know.
In our report “AI IQ: Insights on Artificial Intelligence in the Enterprise,” 1,000 senior decision-makers were surveyed about artificial intelligence and machine learning (ML). Eighty-one percent of leaders agree 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.
To enable a successful and responsible companywide deployment, business leaders at all levels, from the CEO to team managers, must ensure that they’re well informed about AI. Organizations that are slow to adopt AI won’t only lose their competitive edge, they’ll get left behind entirely. The AI thought leaders of tomorrow will be those that master the basics today.
81% of leaders agree that AI is required to keep their business competitive.
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AI terminology can often be incredibly technical, covering everything from decision trees to reinforcement learning. In the AI glossary below, we’ve focused on the essential terms, including each term’s relevance at an organizational level.
Here are 10 AI terms you have to know as a business leader:
Artificial intelligence is the ability for machines to perform specific 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 models enhance intelligent decision-making and human productivity.
Why it matters: This 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% five years prior—a sign that the business value AI represents is massively on the rise.
Machine learning is a subset of AI that, as the name suggests, focuses on creating models that can imitate human methods of learning. Rather than following explicit instructions, machine learning algorithms use training data and examples to identify patterns in large datasets. By doing so, machine learning models learn to develop stronger pattern recognition and predictive analytics.
There are multiple types of ML, including supervised machine learning, unsupervised learning, and reinforcement learning. What type of ML model you need depends entirely on the use case, as explained in our guide to ML.
Why it matters: The automated predictions generated by ML empower business leaders to focus on strategic decision-making, with the option to keep a human in the loop at decisive moments. Companies that continue to rely on manual processes risk wasting employee time that could be best spent on other projects.
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, trust must be designed into it—and expected of it. This is why Workday is committed to the ethical, transparent, and accountable use of AI. 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% 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.
Artificial intelligence is the ability for machines to perform tasks traditionally seen as requiring human intelligence.
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 (see No. 10: Neural Networks), 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.
Why it matters: For enterprise companies, the ability to swiftly process high amounts of data is crucial. At Workday, we use deep learning across a variety of functions. For example, in finance deep learning is used to identify data points in expense reports and invoices before mapping them to fields within our database, drastically improving efficiency.
Natural language processing 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:
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 people leaders at every level of the business 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 the backbone of the vast majority of computer science fields, as well as AI and ML models.
Why it matters: AI or not, algorithms are behind nearly every major technological advancement of the 21st century. As the world of work continues to become more and more data-driven, well-written algorithms will be what distinguishes success.
Generative AI is a type of AI system that creates 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 include:
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 can create offer letters, job descriptions, and provide budget decision support. Understanding the art of prompt engineering (i.e. writing effective prompts for GenAI) will be a key skill in the coming years.
Large language models 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:
Why it matters: With each year that passes, businesses have to process more and more data. LLMs not only enable data to be processed and analyzed quickly, they also empower users to generate useful insights in real time.
Optical character recognition 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’s system.
Why it matters: The potential business applications for OCR are massive, reducing unnecessary manual workload across multiple functions. Since every invoice, expense report, and document can be scanned and processed in real time, employees have more time to focus on the bigger picture.
A neural network is a complex computer system modeled 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 machine learning. 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 AI technology that evolves alongside your company will reap major benefits further down the line.
Thanks to AI, the future of work is already upon us. As the global workplace continues to evolve at a blistering pace, it’s critical that businesses make the right decisions now to safeguard themselves against future changes. 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 organizations are already using Workday technology to help:
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.
A remarkable 82% of organizations are already using AI agents. But is your team ready? Read our latest report to learn how businesses are maximizing human potential with AI, featuring insights from nearly 3,000 global leaders.
This article has been updated since it was first published in July 2023.
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