What is artificial intelligence (AI)? In its simplest form, AI is technology that enables computers to perform tasks that typically require human brainpower. Like people, AI can understand language, recognize patterns, and even make decisions.
Formidably, AI tools and AI-powered systems also learn from their experiences and the data they’ve processed. That learning is held in systemic memory and evolves from the lessons, so that the AI tools, and data stored, consistently improve.
The Value of AI To Businesses of All Sizes
In a nutshell, AI can help businesses of any size automate work, gain insights, and solve problems much faster than the average human. Common applications include:
- Data analysis – AI tools spot patterns and trends in datasets that humans would typically miss, or would be extremely tedious to identify.
- Prediction/Forecasting – AI tools can foretell and project sales volumes, customer demand, and/or prospect behavior to inform any business’s strategy and plans.
- Automation – AI tools can handle boring and repetitive tasks like data entry, scheduling, or invoice processing, to free up employees for more complex work.
- Customer service – Virtual AI assistants and Chatbots can handle customer and prospect inquiries 24/7, and they learn how to respond consistently as they take on new inquiries.
- Personalization – AI is adept at tailoring content – product recommendations, marketing approaches, and user experiences – to meet the needs of individual customers and prospects.
The core AI business value proposition is simple. AI helps businesses work smarter by handling routine tasks, uncovering hidden data insights, and making faster, more informed decisions. These benefits translate to reduced costs, increased efficiency, and improved customer experiences.
AI Terminology to Know
| Term | Simple Definition | Example / Analogy |
| Algorithm | A set of rules a computer follows to solve a problem or make a decision. | Like a recipe that tells a computer what steps to take. |
| Machine Learning (ML) | A type of AI where computers learn from experience — meaning from data — instead of being programmed with exact instructions. | Like showing a child many pictures of dogs until they learn to recognize one. |
| Neural Network | A computer system modeled after the human brain, made up of layers of “neurons” that process information. | Think of it as a web of tiny decision points that work together to recognize patterns. |
| Deep Learning | A type of machine learning that uses many layers of neural networks to handle complex tasks like voice recognition or image analysis. | Like a more advanced version of learning — the “deep” part refers to how many layers it uses. |
| Natural Language Processing (NLP) | How computers understand and respond to human language (spoken or written). | Used in chatbots, translation tools, and voice assistants. |
| Generative AI | AI that can create new things — text, images, music, or even video — based on what it has learned. | ChatGPT, DALL·E, and other content-creation tools use this. |
| Data | The information AI learns from — examples, facts, numbers, text, or images. | Like the “experience” a human gains over time. |
| Model | The trained system that uses what it learned from data to make predictions or decisions. | If data is the lesson, the model is what the AI “remembers.” |
LLMs, Generative AI and Agentic AI
Breaking down AI terminology further, there are also Generative AI, Agentic AI and Large Language Models (LLMs).To simplify it, Generative AI creates outputs and Agentic AI takes actions. LLMs help to power both.
Large Language Models (LLMs) are a specific technology. LLM’s are founded on neural networks that are trained on vast amounts of text to understand and generate language. LLM’s are typically the underlying engines that power many AI applications. A great engineering analogy is that an LLM is like an engine, while generative and agentic AI are different vehicles you can build with that engine. LLMs are the foundation that enables generative AI for text. LLMs can be used to build both generative and agentic AI systems. A generative AI system might use an LLM to create content, and an agentic AI system might use an LLM to understand instructions, think and reason about tasks, and communicate to humans asking questions.
Generative AI tools create new content. It produces text, images, music, program code, and more based on patterns learned from training data. It responds to prompts but it doesn’t take independent action. Think of it as a creative tool that waits for your instructions. Its best output typically is produced when a person’s instructions or questions are specific, detailed, and as comprehensive as a human can make them. Some examples might be DALL-E, which creates wonderful images, ChatGPT which generates text in response to prompts, and Claude which can write computer code.
Agentic AI takes autonomous action and achieves goals. Agentic AI can operate as a digital assistant that executes complex workflows. It can plan multiple steps, use tools, make decisions, and adapt its approach based on results. And it does this without constant human guidance. As an example, Agentic AI can schedule effective meetings by checking calendars, availability of participants and emailing everyone invited. Agentic AI can also autonomously debug code and fix issues.
Considerations When Implementing AI
AI is an exciting technology. And just like any emerging tool or trend, the conversation on how to best implement it is still ongoing. For as many wonderful things that AI can do, there are some things that businesses should be wary about using this technology for.
One important consideration is data privacy. Because of how AI is taught, anything fed into it is treated as public info. You should not use AI to process any data that is confidential.
It’s also important to remember that AI is, above all things, a tool. We prefer to extend our team’s capabilities, not replace them. Always remember, human team members should be responsible for the final output of any AI-assisted projects.
AI is a complex topic, and not one that can be covered in just one blog. Keep an eye out next month, where we’ll be expanding on the topic of generative AI and how it can be used in your CRM.
Whether you’re a new client looking for a system that will fit your needs, or an existing client who wants adjustments to the system you have in place, we want to hear from you. Reach out today for a free consultation.