AI Glossary and FAQ

Whether you’re building a strategy, guiding a team, or solving problems, AI is showing up in tools, workflows, and decisions. You don’t need to be an engineer to make sense of it—you just need a shared language and a little context.

This glossary is designed to help you and your team get familiar with some of the most common AI terms, so you can ask better questions, spot opportunities, and bring AI into your work with confidence. Use it as a reference, a conversation starter, or a way to build fluency across your organization. Let’s dig in!

AI Terms You Should Know, A-Z

AI Confidence Index: Based on transparency, performance, and oversight, a measure of how much anyone can trust an AI system.

AI Maturity: How advanced your AI capabilities are.

AI Operating Model: How AI fits into your workflows, decision-making, and governance.

AI Readiness: Your organization’s current ability to adopt AI.

AI Risk Management: Identifying and addressing potential risks in how AI is built, used and maintained.

AI Strategy Alignment: Ensuring AI initiatives support business goals and stakeholder priorities.

AI Transformation: Rolling out AI across your business to improve how work gets done—from operations to customer experience.

AI Value Waves: The stages of impact AI brings to a business, starting with efficiency, then moving into innovation and growth.

Agentic AI: AI designed to act autonomously on a pre-defined goal with minimal human intervention.

Application Programming Interface (API): A set of rules and definitions that allows different software applications to communicate.

Artificial intelligence (AI): The ability of computer systems to perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, or generating content.

Augmentation: The use of AI and other technologies to perform repetitive tasks without human intervention. Automation can be applied in various domains, including manufacturing, customer service, data entry, and more, to streamline operations and improve productivity

Automation: The use of AI and other technologies to perform repetitive tasks without human intervention. This involves creating systems that can execute predefined rules and processes, thereby increasing efficiency, reducing errors, and freeing up human workers to focus on more complex and creative tasks. Automation can be applied in various domains, including manufacturing, customer service, data entry, and more, to streamline operations and improve productivity

Bias: Patterns in AI outputs caused by training data or model design that can lead to uneven or unfair results across different groups.

Chain-of-Thought Prompting: A way to guide AI to reason step-by-step instead of jumping to an answer.

Compute Power: Processing capacity needed for AI.

Context Window: The amount of information an AI model can “see” at one time when making a decision or generating a response.

Data Privacy: Protecting sensitive information.

Deep Learning: An area of machine learning that uses multi-layered neural networks of unstructured data that excels at complex tasks like image and speech.

Digital Twin: A virtual model of a real system.

Embedding: A way to represent data—like words or images—as numerical vectors so AI models can process and compare them.

Explainability: Understanding how AI makes decisions.

Few-Shot Learning: Training an AI model to perform a task using only a small number of examples.

Fine-Tuning: Customizing a pre-trained model to get more refined results.

Generative AI: AI technologies designed to create new content, such as images, text, and code, from given prompts. These systems leverage advanced machine learning models to generate innovative solutions, automate content creation, and enhance technical workflows, making them invaluable for businesses seeking to integrate cutting-edge AI capabilities into their services and products.

Hallucination: When an AI system generates information that is false or unsupported by its training data—often with high confidence.

Human-AI Collaboration: Designing work so people and AI support each other.

Human-in-the-Loop (HITL): Humans guiding AI decisions to improve system efficiency.

Inference: Using a model to make predictions.

Inference Pipeline: The sequence of steps from input to output.

Large Language Model (LLM): AI trained on massive text datasets to understand and generate human-like language.

Latency: Time it takes for AI to respond.

Machine Learning (ML): Algorithms that learn from data to make predictions or decisions without being explicitly programmed for each task.

Model Auditing: Reviewing AI for compliance and fairness.

Model Drift: When model performance declines over time.

Model Weights: Parameters that define a model’s behavior.

Multimodal AI: AI that can understand and generate across different types of input such as text, images, and audio—all at once.

Natural Language Processing (NLP): Enables computer systems to understand and communicate with human language.

Neural Network: A computer model inspired by the structure of the human brain.

Next Best Action (NBA): AI’s suggestion for the most effective next move based on data.

Overfitting: When a model memorizes instead of generalizing.

Prompt Engineering: Crafting inputs to guide AI to better outputs.

Proof of Concept (PoC): A small test of an AI idea, for instance.

Responsible AI: Ethical and safe AI practices.

Retrieval-Augmented Generation (RAG): A technique to enhance LLMs by providing up-to-date, external data.

Supervised Learning: Machine learning approach that trains algorithms using labeled input-output data to make predictions on new, unseen data.

Synthetic Agents: AI-powered digital workers that simulate human roles in virtual environments used for training, testing, or scaling.

Synthetic Data: Artificially generated data that mimics real-world data. It is used to train AI models when real data is scarce, sensitive, or expensive to obtain. Synthetic data helps in improving model performance and ensuring privacy.

Throughput: The volume of tasks or data an AI system can process within a given time frame. It is a measure of the system’s efficiency and capacity to handle workloads.

Token: A unit of text in language models, typically representing words, subwords, or characters. Tokens are the basic building blocks that language models use to understand and generate text.

Training Data: The dataset used to teach an AI model how to perform a specific task. It consists of input-output pairs that the model learns from to make predictions or decisions on new, unseen data.

Transformer: A type of neural network architecture designed to handle sequential data. Transformers are widely used in large language models for tasks such as translation, summarization, and text generation. They excel at capturing long-range dependencies in data.

Transparency: The practice of being open and clear about how AI systems work, including their decision-making processes, data sources, and potential biases. Transparency helps build trust and accountability in AI systems.

Underfitting: A situation where an AI model is too simple to capture the underlying patterns in the data. Underfitting results in poor performance on both the training data and new, unseen data.

Unsupervised Learning: A type of machine learning that uses unlabeled data to find patterns and relationships within the data. Unlike supervised learning, unsupervised learning does not rely on predefined input-output pairs.

Use Case: In the context of AI, a use case refers to a specific problem or scenario where AI can be applied to provide a solution. It defines the practical application of AI to address real-world challenges.

Vector Database: A specialized database designed to store and search embeddings, which are numeric representations of data. Vector databases are used to efficiently handle high-dimensional data and perform similarity searches.

Zero-Shot Learning: The ability of an AI model to perform a task without having seen any examples of it during training. Zero-shot learning relies on the model’s ability to generalize from related tasks and apply its knowledge to new, unseen tasks

5 Questions for Decision-Makers to Ask AI Services Providers

If you’re exploring AI tools for your business, these quick questions can help you evaluate fit and functionality. They’re designed to guide conversations with vendors or internal teams, so you can make confident decisions about what works best for your goals and workflows.

How will this AI solution support our existing workflows—not replace them?
What kind of data do we need to make this work—and is ours ready?
How will we measure success, and what does ROI look like in the first 90 days?
What kind of training or enablement will our team need to use this confidently?
How do you ensure the AI stays accurate and relevant as our business evolves?

AI FAQs

Q: Do I need a technical background to understand or use AI?

Not at all. Many AI tools are designed for non-technical users. What matters most is understanding what AI can (and can’t) do, and how it fits into your goals.

Q: How is AI being used in everyday business?

AI is helping teams automate repetitive tasks, personalize customer experiences, forecast trends, and make data-driven decisions—across industries like healthcare, retail, finance, and staffing.

Q: What’s the role of data in AI?

Data is the fuel for AI. The quality, quantity, and relevance of your data or information directly impact how well AI performs. Data must be relevant and representative to avoid bias and improve performance. Clean, well-organized data makes everything easier—in more than just AI and LLMs.

Q: Can AI make decisions for us?

AI can offer recommendations, but you always want a human in the loop—especially for decisions that affect people, strategy, or compliance. Think of AI as a smart assistant, not a decision-maker.

Q: What’s the difference between automation and AI?

Automation follows predefined rules to complete repeatable tasks—think of it as “if this, then that.” AI, on the other hand, learns from data. It can recognize patterns, adapt to new inputs, and improve over time. While automation is great for consistency, AI adds flexibility and insight—especially when the task involves prediction, personalization, or decision support.

Q: What’s the difference between machine learning and AI?

Machine learning (ML) is a subset of AI. It’s the part that allows systems to learn from data and improve over time without being explicitly programmed for every task. AI is the broader concept—it includes machine learning, but also other approaches like rule-based systems, computer vision, and natural language processing. So, while most modern AI tools use machine learning, not all AI is machine learning.

Q: What are some risks with using AI?

Like any technology, AI comes with considerations. Depending on how it’s designed and used, it may produce biased results, generate inaccurate outputs, or be relied on too heavily without human oversight. That’s why many organizations focus on building responsible practices around transparency, governance, and team involvement when exploring AI.

Q: How do I know if an AI tool is working well?

It depends on what you’re using it for. In general, you might look at how consistent, accurate, and useful the outputs are—and whether your team finds it helpful. If the tool supports better decisions, saves time, or improves workflows, that’s a good sign. But it’s also important to check in regularly, gather feedback, and adjust as needed. AI performance can shift over time, so ongoing evaluation is part of the process.

Q: How do I start introducing AI to my team?

It often helps to start with something small and specific—like a process that’s repetitive, data-heavy, or time-consuming. From there, you can explore how AI might support or streamline that task. Involving your team early, offering training, and being transparent about goals and limitations can help build trust and momentum. Every team is different, so the path to adoption may vary—but curiosity and collaboration go a long way.

Q: How do I know if my organization is ready for AI?

Start with an AI readiness assessment—look at your data quality, team skills, and existing workflows.

Q: What are common AI use cases in business?

AI is being used across industries for tasks like forecasting demand, personalizing customer experiences, automating routine processes, and supporting decision-making. These use cases often span departments—from marketing and operations to HR and finance. To get the most out of AI, it helps to involve cross-functional teams early, align on goals, and create space for feedback and iteration. Collaboration between technical and non-technical roles is key to making AI useful and usable.

Q: What role does change management play in AI adoption?

Change management helps teams understand, trust, and use AI tools effectively. It’s not just about rolling out technology—it’s about preparing people. That includes clear communication, training, and ongoing support. It also means involving stakeholders early, listening to feedback, and adjusting as needed. When done well, change management turns AI from a tool into a team asset.

Final Thoughts

AI is influencing the future, and it’s key to keep up with the rapid advances happening. We hope you now feel you’re better equipped to speak the language as you continue your AI journey. Remember that learning about AI is not about mastering everything at once. Keep asking questions, stay curious, and don’t be afraid to revisit these terms and how they apply to your evolving needs as your understanding deepens.

If you want to find out more, Insight Global and Evergreen, our professional services division, have AI experts ready to help with your organization’s transformation. Let us help you turn AI challenges into transformational solutions.