Christine Darby // Published: April 2023 // Updated: June 2025

Feeling overwhelmed by the increasing buzz around AI and GPT in the business world? Our AI primer for small businesses breaks down key terminology to provide you with an overview understanding of complex concepts. We provide clear definitions to help you become fluent in generative AI conversations. 

This AI guide offers a step-by-step accessible introduction to essential topics in artificial intelligence, ranging from the broad concept of AI, to the basics of machine learning (ML), down to popular generative AI tools like ChatGPT and AI search engines. Gain insights into AI fundamentals and how generative AI will transform and impact your business.

What is AI?

Artificial Intelligence (AI) involves developing algorithms and systems to perform tasks that would normally require human intelligence, such as learning, planning, reasoning, perception, and decision-making. It is a broad field that draws from the fields of computer science, philosophy, psychology, linguistics, mathematics, statistics, neuroscience, and even art and design.

AI encompasses areas such as computer vision, robotics, game playing, and natural language processing, or the ability to understand and generate human language.

What is machine learning (ML)?

Machine learning (ML) is a subset of AI that gives computers the ability to learn from data without being explicitly programmed. Think of it as teaching a computer to recognize patterns, similar to how we might learn to ride a bicycle: once we understand the pattern of pedaling and balancing, we can do it again and again.

Machine learning algorithms and models can be used to build systems that can make predictions, detect patterns, and classify data. ML has numerous applications in various industries, including healthcare, finance, retail, and more. For example, machine learning can be used to predict patient outcomes, detect fraud, recommend products to customers, and optimize supply chains.

There are many different techniques in machine learning, but two major categories include:

  • Traditional Machine Learning: These techniques have been around for a while. Some examples are linear regression (used to predict a continuous outcome, like house prices based on various features of the house) or decision trees (used to classify data, like determining whether an email is spam or not). These methods often need important pieces of data to be identified in advance.

  • Deep Learning: This is a newer subset of machine learning that’s all about neural networks with several to many layers, hence the term “deep.” These neural networks are modeled after the neural networks found in the human brain. These tools are designed to analyze large amounts of data by recognizing complex patterns.

    Deep learning powers many AI tools, enabling automated systems to handle both analytical and hands-on tasks without needing humans. It can be used for a variety of tasks, including natural language processing, speech recognition, and image recognition.

The broad field of machine learning encompasses Natural Language Processing and Language Modeling as subfields.

What is NLP?

Natural language processing (NLP) is a subfield of AI that specifically focuses on developing algorithms and models that can understand, analyze, and generate human language. This is a field that blends computer science and linguistics.

Examples of NLP tasks powered by AI algorithms and systems include:

  • Text Classification: Categorizing text into predefined categories, such as sorting emails into spam and non-spam folders.

  • Sentiment Analysis: Determining the emotional tone of a piece of text, such as whether a customer review is positive or negative.

  • Named Entity Recognition: Identifying and extracting important named entities from a piece of text, such as Google’s Knowledge Graph that extracts people, places, or things from text to provide a summary.

  • Machine Translation: Translating text from one language to another, such as Google Translate which can help small businesses communicate with international customers.

  • Question Answering: Automatically generating answers to questions based on a given context, such as chatbots, or Apple’s Siri and Amazon’s Alexa.

Other examples of cloud-based NLP services include Amazon Comprehend and IBM Watson—these platforms provide sentiment analysis, entity recognition, content classification and topic modeling.

NLP focuses on the understanding and analysis of human language, and language modeling is specifically concerned with generating human-like text or predicting the next word in a sentence. Language models are one of many tools used in AI-based natural language processing.

What is a language model (LM)?

A language model (LM) is a type of computer program that has been trained on large amounts of text data to analyze, understand, and generate human language or perform other NLP tasks.

Language models can be used to perform tasks like writing, identifying grammatical errors in text, translating, or summarizing text. LMs can also be used for tasks such as generating new text based on prompts or predicting the next word in a sequence.

It’s important to note that while language models can improve in ability over time, this is done through training on large amounts of data, not through individual experiences or interactions. The tools don’t have the capability to learn or remember information from one interaction to the next.

Examples of large language models (LLM) you might be familiar with include GPT, BERT, RoBERTa, XLNet, ELECTRA, and T5.

The BERT language model was added to the Google search system in 2019, making Google better at understanding natural language and the intent behind search queries. This had an SEO impact on websites with low-quality content. If your business was already producing high-quality content for SEO relevant to your target audience, then the impact of BERT on your optimization efforts was likely minimal.

Thus far the primary focus has been on creating large language models, but now there’s growing interest in smaller models. These compact models can be deployed locally for greater control, faster response times, and privacy or cost advantages.

Some language models, known as generative AI, are specifically designed to generate new text based on training data. These models can be used for a variety of natural language processing tasks.

What is Generative AI?

Generative AI is a specific subset of artificial intelligence focused on creating new, original content. These systems can produce a wide variety of outputs, including text, images, code, music, and even complex structures like 3D models or videos. Generative AI models are trained on massive datasets and are designed to recognize and reproduce patterns from that data.

A well-known example is the GPT (Generative Pre-trained Transformer) series of language models developed by OpenAI. These large language models generate highly coherent, human-like text based on user prompts. They combine language, vision, and audio capabilities in a single model, allowing them to process text, images, and even voice inputs and outputs.

Businesses use generative AI for tasks like creating content, automating customer support, drafting emails, and generating code. These models are available through platforms like ChatGPT or can be integrated into internal business tools via an API (Application Programming Interface).

APIs, such as the OpenAI API, let businesses leverage generative AI capabilities without developing their own language models. Some companies are now exploring the use of smaller local LMs to avoid using third-party APIs—and to gain more control over performance, cost, and data privacy—but most SMBs still rely on APIs for ease of use.

Generative AI can produce remarkably realistic output, but it’s important to remember: these tools generate responses based on learned patterns—not true comprehension or intent. They are powerful assistants, not subject matter experts.

What is ChatGPT?

ChatGPT is an AI-powered assistant built on OpenAI’s GPT language models. It generates human-like text responses in a conversational format, responding to prompts with relevant, fluent answers. While often called a “chatbot,” ChatGPT doesn’t actually “understand” conversation the way people do—it produces output based on patterns and probabilities learned from training data.

Artificial intelligence has been integrated into widely-used everyday applications for years—like Siri, Alexa, Netflix, Spotify, or business programs such as Salesforce, QuickBooks, HubSpot, and Tableau. But ChatGPT brought advanced AI into the mainstream by offering a free, easy-to-use interface with powerful language generation capabilities. Small businesses now rely on it to draft content (like blog posts, product descriptions, or emails), brainstorm ideas, summarize documents, or generate code.

OpenAI continues to release new GPT models. The free version of ChatGPT allows limited access to the most advanced model, while paid plans offer access to more functionality.

Chatbots versus AI Search

After OpenAI released ChatGPT to the public in late 2022, Google raced to release their own generative AI chatbot, Gemini (originally called Bard). Around the same time, Microsoft and Google added AI-powered results to their search engines, leading to Microsoft’s Copilot (formerly Bing Chat) and Google’s AI Overviews (formerly Search Generative Experience or SGE).

There is crossover in the function and use of these tools, but in general, chatbots are better for creative tasks, exploration, and conversation-style interaction, while AI-powered search engines are more focused on surfacing current and factual web-based information. That said, these distinctions continue to blur with every passing day.

Chatbots

Chatbots can be used to generate content such as stories, marketing campaigns, and even code. But the “creativity” displayed is based on patterns recognized from existing data, not genuine creativity as exhibited by humans. The tools can provide summaries or outlines of factual topics and help with more complex tasks that are difficult to explain in words, such as math problems.

The chatbot experience is designed to mimic a conversation with a human who understands questions and provides thoughtful answers. Each model has a knowledge cutoff date, which might result in outdated facts, but most can now search the internet and read webpages when generating responses.

Based on capability and adoption, the top 3 general-purpose AI chatbots are:

  • ChatGPT: OpenAI’s tool is highly customizable via “Custom GPTs” and integrates with OpenAI API for business tools.

  • Gemini: Google’s answer to ChatGPT is tightly integrated with Google Workspace.

  • Claude: Anthropic’s tool is known for safe responses and handling long documents.

AI Search

AI-powered search is built into search engines to help users find and understand information faster. These search tools summarize content from around the web and aim to answer complex queries more directly than traditional search results. Examples include:

  • Google AI Overviews (AIO): Powered by Gemini. These AI-generated summaries appear at the top of many search results, offering a summary based on multiple sources. If you prefer the classic search experience, Google has added a “Web” filter under the “More” tab.

  • Google AI Mode: Google’s newest search mode expands on AI Overviews for more advanced reasoning, thinking and multimodal capabilities.

  • Microsoft Copilot Search: Powered by GPT-4. This tool is available both inside the Bing search engine and in Microsoft tools like Edge and Office.

Chatbots and AI-based search are very powerful tools, but have limitations. The tools cannot discern what is true or false in the information they process. And the tools can produce biased or inaccurate responses based on training data, so it is important to carefully evaluate AI output. That said, AI model developers are actively working on reducing biases to make AI outputs more reliable and fair.

Overall, the output quality of any generative AI tool is highly dependent on the quality of your prompting instructions. Clear, specific prompts will almost always lead to better output.

Note: AI-generated search summaries (like Google’s AIOs) is reducing the visibility of traditional website links. For businesses that rely on organic search traffic, this shift can impact SEO performance and how often users click through to your site. Keep an eye on how your content appears in search results and consider strategies to improve inclusion in AI summaries.

AI and Your Small Business

Using AI tools—especially those involving NLP and language models—raises important ethical and legal considerations. Topics like privacy, transparency, bias, and accountability are central to the growing field of AI ethics. Governments and industry leaders are introducing formal regulations and frameworks to guide the responsible use of AI.

Even very small businesses will want to develop internal AI guidelines. For now, treat generative AI output as something created by an assistant rather than an expert. For example, if you’re using AI to draft marketing copy, blog posts, or product descriptions, ensure a human reviews and edits the content before it goes public.

There are also legal issues surrounding AI-generated content. Lawsuits are ongoing against AI companies over the use of intellectual property and copyrighted material in training data. And some creatives and publishers are choosing to block AI bots from scraping their content.

Because AI tools are evolving quickly, it’s important for small businesses to stay informed. Use reputable sources to track developments, and be cautious about over-relying on AI for tasks that impact brand voice, legal risk, or customer trust. Consider the long-term implications of how AI is integrated into your workflows, and revisit your practices regularly to align with emerging best practices and compliance requirements.

Additional Reading

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