Lately, I’ve been getting a lot of questions from customers about AI. Everything from “What’s this LLM thing?” to “I keep hearing about Generative AI, but what does it do?”.
It’s no surprise, as AI capabilities grow, so does the pile of jargon. Here’s a simple explanation of common AI terms, their true significance, and how they’re used in practice.
Artificial Intelligence (AI)
AI is the catch-all that focuses on building machines that can mimic human intelligence, reasoning, and decision-making. Think of it as a toolkit that makes everyday processes smarter, faster, and more intuitive, from more relevant product recommendations to more efficient warehouse operations.
Machine Learning (ML)
A subset of AI where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed. ML powers things like Netflix suggestions, can help detect credit card fraud, and optimises logistics by anticipating supply and demand.
Deep Learning (DL)
A specialised form of ML that uses multi-layered neural networks inspired by how our brains process information. DL enables advanced image recognition, real-time language translation, and predictive maintenance in manufacturing, spotting issues before they cause downtime.
Natural Language Processing (NLP)
The AI discipline that allows machines to understand, interpret, and generate human language. NLP makes voice assistants like Alexa or Siri comprehend what you’re asking, allows search engines to understand your intent, and enables chatbots to handle customer inquiries efficiently.
Large Language Models (LLMs)
Highly advanced NLP models trained on vast amounts of text data. LLMs can draft articles, summarise lengthy reports, and respond to questions with near-human-like fluency. This is what many “generative AI” solutions use to produce content on demand.
Computer Vision (CV)
AI that enables machines to “see” and interpret images and video. CV can identify defects in a product on the assembly line, recognise faces at a security checkpoint, or categorise items in a warehouse to speed up order fulfillment.
Retrieval Augmented Generation (RAG)
A method that pairs an LLM with the ability to “fetch” external data, like searching your company’s internal documentation or the latest product release notes before generating a response. Instead of relying solely on static training data, a RAG-based tool can provide the most up-to-date specs, regulations, or market info, ensuring its answers are always relevant and current.
Practical Use Cases
Content Creation (LLMs):
Marketing teams can use LLMs to draft blog posts, social media updates, and product descriptions in minutes—freeing them up to focus on big-picture strategy.
Customer Support (RAG + NLP):
AI-driven chatbots can tap into your company’s latest documentation and FAQs (RAG) to give customers accurate answers right away, improving both response time and satisfaction.
Quality Control (CV):
Computer Vision tools in manufacturing spot defects on the production line instantly, reducing waste and ensuring product consistency.Personalized Recommendations (ML):
ML models deliver personalized experiences—suggesting the perfect next TV series, recommending products that match your style, or optimizing your supply chain routing based on historical data.
Supply Chain & Logistics (ML + CV):
Predictive ML models forecast inventory needs and shipping demands, while CV can track items through the supply chain to ensure transparency and efficiency. Together, they help reduce costs, speed up deliveries, and improve overall reliability.
Wrapping Up
The genuine power of AI isn’t just in the technology, it’s in how it integrates into our daily workflows. Whether it’s helping a distribution center run more smoothly, enabling customer service agents to get the right info at the right time, or freeing your marketing team from the blank-page struggle, AI is no longer an abstract concept.