AI Terms, Made Simple for Business.AI and automation glossary for local business owners

AI designed to act, not just answer. Agentic systems break goals into steps, use tools, and adjust based on results.

In context: An Elkhart distributor tests agentic AI that checks inventory, notices low stock, and prepares a reorder for a manager to approve.

See also: AI Agent, Agentic Workflow

A process where one or more AI agents plan and carry out several steps, using tools and checking their own work along the way.

In context: A Mishawaka clinic’s agentic workflow gathers new patient forms, flags missing insurance details, and sends a friendly reminder text.

See also: AI Agent, Multi-Agent System, Human-in-the-Loop Automation

Making sure a real person or team is responsible for what an AI system does and for fixing problems.

In context: A Goshen agency assigns one staff member to own its AI chatbot, review its conversations weekly, and update its answers.

See also: AI Transparency, Human-in-the-Loop (HITL), AI Governance

An AI system that can plan and take actions toward a goal, such as looking up information, filling in forms, or updating a CRM, with limited step-by-step direction.

In context: A Goshen property manager’s AI agent reads a maintenance request, checks the vendor list, and drafts a work order for approval.

See also: Agentic AI, Autonomous Agent, Human-in-the-Loop (HITL)

Making sure an AI system’s goals and behavior match human intentions and values.

In context: When a Goshen shop asks its AI assistant to “boost sales,” a well-aligned system suggests honest promotions instead of misleading claims.

See also: AI Safety, Artificial General Intelligence (AGI)

An AI tool that helps a person complete tasks, such as writing, researching, scheduling, or summarizing, often across many apps.

In context: A Goshen insurance agent uses an AI assistant to summarize long policy documents before client meetings.

See also: AI Chatbot, AI Agent

Unfair or skewed results from an AI system, often caused by unbalanced training data or design choices. Reviewing outputs and testing with varied examples reduces it.

In context: A Goshen employer reviews AI-screened applications by hand to make sure qualified candidates from every background get a fair look.

See also: AI Training Data, AI Transparency, AI Governance

A program that holds a conversation in text or voice, using AI to understand questions and respond.

In context: A Middlebury RV rental company’s website chatbot answers questions about pickup times and pet policies at any hour.

See also: AI Assistant, Large Language Model (LLM), AI Knowledge Base

The policies, roles, and processes an organization uses to guide safe, fair, and effective AI use.

In context: An Elkhart manufacturer writes a one-page AI policy listing approved tools, what data stays out of them, and who approves new uses.

See also: AI Risk Management, AI Accountability, Data Privacy

When an AI tool states something false or made up with confidence, such as a fake statistic or a wrong business detail. Checking facts against trusted sources keeps content accurate.

In context: An AI draft for a Goshen bakery lists Sunday hours the bakery does not keep. The owner catches it during review and corrects it before posting.

See also: Grounding, Human-in-the-Loop (HITL), Retrieval-Augmented Generation (RAG)

A collection of your own documents, such as FAQs, policies, and product details, that an AI tool searches to give accurate, business-specific answers.

In context: An Elkhart HVAC company loads its warranty terms and service area into a knowledge base, so its chatbot answers with the company’s real policies.

See also: Retrieval-Augmented Generation (RAG), AI Chatbot, Grounding

Coordinating AI models, tools, data, and steps so they work together reliably as one system.

In context: A South Bend ecommerce shop orchestrates order lookups, an AI reply writer, and its shipping system so customer questions get accurate answers.

See also: Multi-Agent System, API Integration, Workflow Automation

Identifying, measuring, and reducing the risks of using AI, such as errors, privacy issues, and bias. The NIST AI Risk Management Framework is a widely used guide.

In context: A Warsaw credit union reviews each new AI tool for data security and accuracy before staff can use it.

See also: AI Governance, AI Safety, Data Privacy

Research and practices that help AI systems behave reliably and avoid causing harm.

In context: A Mishawaka software startup tests its AI support agent with tricky questions before launch to make sure it stays accurate and polite.

See also: AI Alignment, AI Risk Management

The text, images, and other examples used to teach an AI model. The quality and variety of training data shape what the model knows and how it behaves.

In context: A Goshen HVAC company trains its booking assistant on its own service list and FAQs, so answers match how the company actually works.

See also: AI Bias, Fine-Tuning, Data Privacy

Being open about when and how AI is used, and about how an AI system reaches its outputs.

In context: A South Bend insurance agency notes on its website chat that customers are talking with an AI assistant and can ask for a person anytime.

See also: AI Accountability, AI Governance

A series of steps where AI handles one or more tasks, such as sorting, drafting, or summarizing, inside a larger process.

In context: A Mishawaka law firm’s workflow transcribes intake calls, summarizes them with AI, and sends the summary to the right attorney.

See also: Workflow Automation, Agentic Workflow, Human-in-the-Loop (HITL)

A set of rules that lets two software programs talk to each other and share data.

In context: A Goshen boutique’s online store uses an API to send each new order to its shipping software automatically.

See also: API Integration, Workflow Automation

Connecting two or more apps through their APIs so information flows between them without manual copying.

In context: An Elkhart HVAC company integrates its website forms with HubSpot, so every request appears in the CRM instantly.

See also: API (Application Programming Interface), AI Orchestration, CRM (Customer Relationship Management)

A theoretical future AI that could learn and perform any intellectual task a person can, across many fields. AGI does not exist today, and experts debate when or whether it will.

In context: A Mishawaka business owner reading AI headlines can tell the difference: today’s tools handle specific tasks well, while AGI remains a research goal.

See also: Artificial Narrow Intelligence (ANI), AI Alignment

Computer systems that perform tasks that usually take human thinking, such as understanding language, recognizing images, spotting patterns, and making predictions.

In context: A Goshen dental office uses AI to transcribe voicemails into text, so the front desk can read and return calls faster.

See also: Machine Learning (ML), Generative AI, Artificial Narrow Intelligence (ANI)

AI built for a specific task, such as recommending products, filtering spam, or answering questions. Every AI tool in use today, including ChatGPT, is narrow AI.

In context: An Elkhart retailer’s email platform uses narrow AI for one job: predicting the best time to send each subscriber a message.

See also: Artificial Intelligence (AI), Artificial General Intelligence (AGI)

An AI agent that can work toward a goal for an extended time with little human input. Most businesses keep a person in the loop for important decisions.

In context: An autonomous research agent monitors competitor pricing for a Warsaw marine dealer each week and sends a summary report.

See also: AI Agent, Human-in-the-Loop (HITL), AI Governance

A workflow that runs from start to finish without a person stepping in, usually for low-risk, repeatable tasks.

In context: An Elkhart print shop’s autonomous workflow sends proof-ready emails and payment reminders, while custom quotes still go to a person.

See also: Human-in-the-Loop Automation, Workflow Automation

Having an AI model work through a problem step by step before giving its final answer. It often improves accuracy on math and multi-step questions.

In context: Asked to compare two lawn care plans, an AI walks through the cost per visit, number of visits, and extras before recommending one.

See also: Prompt Engineering, Prompt Chaining

The amount of text a language model can consider at one time, including your prompt, any documents, and its own reply.

In context: A Mishawaka accountant pastes a long tax guide into an AI tool. A large context window lets the model read the whole guide before answering.

See also: Tokens, Prompt

The rules about who owns AI-generated work. The U.S. Copyright Office has said protection requires meaningful human authorship, so purely AI-generated material is not protected. Talk with an attorney for specific situations.

In context: A Mishawaka brewery has an artist refine an AI-assisted label concept into a final design, so the finished artwork reflects real human creative work.

See also: Image Generation, Generative AI

Protecting personal information and using it only in ways people expect and allow. With AI tools, check each tool’s data terms before entering customer details. Indiana’s Consumer Data Protection Act took effect on January 1, 2026.

In context: A Warsaw clinic uses an AI tool with a business agreement that keeps patient data private, and staff never paste identifying details into public chatbots.

See also: AI Governance, Small Language Model (SLM), AI Risk Management

Using rules or AI to make routine decisions automatically, such as approving a discount, routing a lead, or prioritizing a ticket.

In context: A Goshen equipment rental company automatically approves returning customers with good payment history, while new accounts get a quick manual review.

See also: Workflow Automation, Human-in-the-Loop (HITL)

A branch of machine learning that uses neural networks with many layers. Deep learning powers speech recognition, image recognition, and language models.

In context: A South Bend real estate team’s photo app uses deep learning to brighten and straighten listing photos automatically.

See also: Neural Network, Machine Learning (ML), Large Language Model (LLM)

Automation that reacts instantly to events in your systems, such as a new order, a missed call, or a status change.

In context: When a Mishawaka plumber misses a call, an event-driven automation texts the caller within a minute with a booking link.

See also: Trigger-Based Workflow, Workflow Automation

Additional training that adapts a foundation model to a specific task, style, or field using a smaller, focused set of examples.

In context: A Warsaw medical device company fine-tunes a model on its own product manuals so it answers technical questions in the company’s terms.

See also: Foundation Model, AI Training Data

A large AI model trained on broad data that can be adapted for many tasks. GPT, Claude, Gemini, and Llama model families are examples.

In context: A Goshen software company builds its customer support assistant on top of a foundation model instead of training one from scratch.

See also: Large Language Model (LLM), Fine-Tuning, Open-Source AI Models

AI that creates new content, such as text, images, audio, video, or code, based on patterns it learned from training data.

In context: A Goshen bakery uses generative AI to draft three versions of a holiday post, then the owner edits the one that sounds most like her.

See also: Large Language Model (LLM), Image Generation, AI Hallucinations

Automation that pauses for a person’s approval at important steps, then continues on its own.

In context: A South Bend law firm’s intake automation drafts a welcome email for each new client, then waits for a paralegal to approve it before sending.

See also: Human-in-the-Loop (HITL), Autonomous Workflow

A setup where a person reviews, approves, or corrects AI output at key points. It keeps quality high and decisions accountable.

In context: Every AI-drafted review reply for a Warsaw dental practice waits for the office manager’s approval before posting.

See also: Human-in-the-Loop Automation, AI Accountability, AI Hallucinations

Using AI to create images from a text description. Review generated images carefully and keep real photos for real products, staff, and work.

In context: A Mishawaka cafe generates a seasonal background graphic for a menu board, while using real photos of its actual drinks.

See also: Generative AI, Copyright and AI-Generated Content, Multimodal AI

The moment a trained AI model is used to produce an answer. Training teaches the model, and inference puts it to work.

In context: Each time a Granger clinic’s chatbot answers a patient question, the model is running inference.

See also: AI Training Data, Large Language Model (LLM)

A foundation model trained on large amounts of text to understand and generate language. LLMs power tools such as ChatGPT, Claude, and Gemini.

In context: When a Warsaw homeowner asks an AI assistant for “questions to ask a roofer,” an LLM writes the list in plain English.

See also: Small Language Model (SLM), Tokens, Context Window

A type of AI that learns patterns from data instead of following hand-written rules. The more quality examples it sees, the better its predictions become.

In context: A Warsaw lawn care company’s booking software learns from past seasons to predict which weeks will be busiest.

See also: Deep Learning, AI Training Data, Neural Network

Several AI agents with different roles working together, such as one that researches, one that writes, and one that reviews.

In context: A Goshen agency tests a system where one agent drafts a blog outline, another writes it, and a third checks facts before a person edits.

See also: Agentic Workflow, AI Orchestration

AI that works with more than one type of input or output, such as text, images, audio, and video, in the same conversation.

In context: A Granger homeowner snaps a photo of a water stain and asks an AI assistant what might cause it. The AI reads the image and the question together.

See also: Generative AI, Image Generation

The field of AI that helps computers understand, interpret, and write human language.

In context: An Elkhart law firm’s intake form uses NLP to read each message and route it to the right attorney.

See also: Large Language Model (LLM), AI Chatbot

A computing system loosely inspired by the brain, made of connected layers that pass signals and adjust as they learn from examples.

In context: The voice assistant that understands “call the pizza place on Main Street” uses a neural network to turn speech into text.

See also: Deep Learning, Machine Learning (ML)

AI models whose weights are publicly released so others can download, run, and adapt them. Licenses vary, so check what each one allows.

In context: A South Bend tech firm runs an open model on its own servers to keep client data in house.

See also: Small Language Model (SLM), Foundation Model

The instructions or question you give an AI tool. Clear prompts with context, examples, and a defined format get better results.

In context: A Warsaw florist writes: “Write a 40-word Facebook post about Mother’s Day pre-orders. Friendly tone. Mention pickup on Saturday.”

See also: User Prompt, System Prompt, Prompt Engineering

Breaking a big task into a sequence of prompts, where each step’s output feeds the next.

In context: A Warsaw contractor chains three prompts: summarize a job’s notes, turn the summary into a customer update, then shorten it into a text message.

See also: Prompt Engineering, Agentic Workflow

The skill of writing and refining prompts so AI tools produce accurate, useful, on-brand results.

In context: An Elkhart marketing coordinator adds the company’s voice guidelines and two sample posts to her prompt, and the AI drafts sound much more like the brand.

See also: Prompt, Prompt Template, Chain-of-Thought

A shared collection of tested prompts and templates a team can reuse.

In context: A Goshen agency keeps a prompt library with approved prompts for review replies, blog outlines, and email subject lines.

See also: Prompt Template, Prompt Engineering

A reusable prompt with blanks to fill in, so a team gets consistent results every time.

In context: A Mishawaka real estate team uses one template for listing descriptions: “Write a 120-word description for a [beds]-bed home in [neighborhood] with [features].”

See also: Prompt Library, Prompt Engineering

Software bots that repeat rule-based computer tasks the way a person would, such as copying data between systems or filling in forms.

In context: A Mishawaka medical billing office uses RPA to copy claim numbers from one portal into its billing system each morning.

See also: Workflow Automation, API Integration

A language model with fewer parameters than an LLM. SLMs run faster and cheaper, and some run directly on phones or laptops.

In context: An Elkhart manufacturer runs a small language model on its own computers to summarize maintenance notes without sending data outside the building.

See also: Large Language Model (LLM), Open-Source AI Models, Data Privacy

Behind-the-scenes instructions that set an AI tool’s role, tone, and rules before any user message arrives.

In context: A South Bend dental office’s chatbot has a system prompt telling it to be warm, answer only scheduling and insurance questions, and offer the phone number for anything else.

See also: Prompt, User Prompt, Prompt Template

The small chunks of text, often parts of words, that language models read and write. Usage limits and pricing are often counted in tokens.

In context: A short Google review of about 75 words is roughly 100 tokens for many language models.

See also: Context Window, Large Language Model (LLM)

An automated workflow that starts when a specific trigger happens, such as a form submission, a purchase, or a date.

In context: A Middlebury boutique’s birthday email workflow triggers on each customer’s birthday with a personal coupon.

See also: Workflow Automation, Event-Driven Automation, Automation Trigger

The message a person types or speaks to an AI tool during a conversation.

In context: A Goshen shopper types “Do you have size 9 hiking boots?” into a store’s chatbot. That message is the user prompt.

See also: Prompt, System Prompt

Software that moves work from step to step automatically based on rules, such as routing a form, creating a task, or sending a follow-up.

In context: When a Granger roofer receives a quote request, automation creates a CRM record, assigns it to an estimator, and texts the homeowner a confirmation.

See also: Trigger-Based Workflow, Event-Driven Automation, Marketing Automation