Artificial intelligence, whilst a phrase used in most of our daily lives, can feel huge, strange, unknown, scary, exciting and sometimes even intimidating. In this post I decided I would strip back the noise and waffle and share nine crisp, usable concepts. I’ve aimed to provide clarity over jargon and give some practical examples over theory.
Before I start, many and to put into familiar brands, here are a few AI tools and brands you will of already know or at least of have heard of:
1. Common AI Tools to know about
- ChatGPT – What really started the world of “publicly accessible” Generative AI Chat Bots. ChatGPT (version 5 is the current) is a conversational AI that generates text, pictures, and even video. It can answer questions and help with creative writing. It’s a clear example of generative AI in action, showing how large language models can produce human‑like responses. Free and Paid versions.
- Copilot (Microsoft) – leverages many different AI models including ChatGPT, Microsoft’s own and others, can do very what ChatGPT can do, but is also integrated across line of business apps and data like Word, Excel, PowerPoint, and Windows. Copilot acts as an AI agent that helps you create, draft, analyse, and even automate tasks. It’s a practical demonstration of how AI agents and retrieval techniques can boost productivity. Free tier (ChatGPT Pro equivalent) and Premium for Consumer/Family. Microsoft 365 Copilot for Business use.
- Google Gemini – Google’s AI assistant that blends search with generative capabilities, pulling in live information to give context‑aware answers. Free and Paid tiers.
- GitHub Copilot – A developer‑focused AI that suggests code snippets and functions in real time. It shows how reasoning models and pattern recognition can accelerate software development.
- MidJourney / DALL·E – Image generation tools that turn text prompts into visuals. These highlight the creative side of AI, where models learn patterns from vast datasets and apply them to new artistic outputs.
- Perplexity – Great for research including financial data and educational content. Has free and paid versions.
- Siri / Alexa – typically home style voice assistants that act as simpler AI agents, interpreting commands and connecting to external systems like calendars, music apps, or smart home devices. Great for simple tasks like “what is weather like today” and for linking to smart home devices – “Alexa, turn on the porch light“.
If you are just starting (or are a beginner), the easiest way to decide which AI tool to use is to match the tool to the problem you’re trying to solve. If you need help writing or brainstorming, generative text tools like ChatGPT or Copilot in Word are ideal. If you’re working with numbers or data, Copilot in Excel can analyse and visualise patterns for you. For deeply creative projects, image generators like MidJourney or DALL·E turn ideas into visuals, while GitHub Copilot accelerates coding tasks. The key is not to chase every shiny new AI release, but to ask: what am I trying to achieve, and which tool is designed for that job? If you are starting out, start small, experiment with one or two tools in their daily workflow, and build confidence before expanding into more advanced applications.
Which AI in 5: Pick the AI tool that fits your task- writing, data, images, or code—and grow from there.
2. What is Artificial Intelligence (AI)
Artificial Intelligence (AI) is not really a product though word bingo might have people say ChatGPT or Copilot (at work), but it is far more than that! AI is a broad field of computer science focused on creating systems that can perform tasks which normally require human intelligence. These tasks include many things such as recognising speech, interpreting and understanding images and videos, making decisions, and even generating creative content such as music, videos and images. As of 2025, AI is already embedded in many aspects of our everyday lives – in work and in personal life – from recommendation engines on Netflix to fraud detection in banking, to summarising meetings at work.
At its core, AI combines data, algorithms, and computing power to simulate aspects of human cognition, but it does so at a scale and speed that humans could never achieve.
AI in 5: AI is machines learning, reasoning, and acting like humans.
3. AI Agents
Right, so an AI Agent is a system designed to act autonomously in pursuit of a goal. Unlike traditional software that follows rigid instructions, agents can perceive their environment, make decisions, and take actions with or without constant human input.
For example, a customer service chatbot is an agent that listens to queries, interprets intent, and responds appropriately. More advanced agents can coordinate multiple tasks, such as scheduling meetings, analysing reports, or even controlling robots in manufacturing.
The key is autonomy: agents don’t just follow orders—they adapt to changing conditions.
AI Agents in 5: AI agents are digital helpers that think and act for you.
4. Retrieval-Augmented Generation (RAG)
RAG is a technique that makes AI more reliable by combining generative models (or sub models) with external knowledge sources such as the Web or date from corporate SharePoint sites, email etc.
Instead of relying solely on what the AI model was trained on (which may be outdated or incomplete), RAG can retrieves relevant documents or data in (near) real time and integrates them into its response.
This is especially powerful in business contexts, where accuracy and timeliness are critical – for example, pulling the latest compliance rules or product specifications from an application or data repository, before answering a query. RAG bridges the gap between static training data and dynamic, real-world knowledge.
RAG in 5: RAG = AI that looks things up from multiple sources before answering.
5. Explainable AI (XAI)
One of the biggest challenges with AI is the “black box” problem. What I mean by that is that often do not know how AI arrived at its decisions or answer when instructed.
Explainable AI addresses this by making the reasoning process transparent and understandable to humans. For instance, if an AI is being used by a bank to determine if a customer should/can get a loan or not and that AI model rejects the loan application, XAI will highlight / explain the factors such as credit history or income that influenced the decision.
In essence this is about seeing it’s workings out. If you have used Microsofts Researcher or Analyst agent at work, you will see some of this as it does its work.
This transparency is vital in ensuring we can trust AI and is required in regulated industries like healthcare, finance, and law, where accountability and fairness are non-negotiable.
By opening this black box, XAI builds trust and ensures AI is used responsibly.
XAI in 5: XAI shows you why the AI answers the way it did, what information it used and how it made its choice.
6. Artificial Super Intelligence (ASI)
While today’s AI is powerful, it is still considered “narrow AI” – specialised in specific tasks despite the advances we see every week.
Artificial Superintelligence (ASI) is a (some say) theoretical future state where machines surpass human intelligence across every domain, from scientific discovery to emotional understanding.
Many might be thinking “The Terminator” here but in reality it is more than conceivable given the current pace of evolution that ASI could in design new technologies, solve global challenges, or even “create” beyond human imagination.
This naturally raises profound ethical and safety concerns: how do we ensure such intelligence aligns with human values and what happens if ASI becomes smarter than the humans that created it?
ASI remains speculative and there are many opinions and research on the matter, but today it is a concept that drives much of the debate around the long-term future of AI.
ASI in 5: ASI is the idea of AI being smarter than all humans in every way.
7. Reasoning Models
Traditional AI models excel at recognising patterns, but they often struggle with multi-step logic.
Reasoning models are designed to overcome this by simulating structured, logical thought processes. They can break down complex problems into smaller steps, evaluate different pathways, and arrive at conclusions in a way that mirrors human reasoning.
This makes them especially useful in domains like legal analysis, financial analysis, scientific research, or strategic planning, where answers are notjust about recognising patterns and finding information but about weighing evidence and making defensible decisions in a way similar to how we as humans might undertake such work.
Reasoning Models in 5: Reasoning models let AI think step by step like us.
8. Vector Databases
AI systems need efficient ways to store and retrieve information, and that’s where vector databases come in.
Unlike traditional databases that store data in rows and columns, vector databases store information as mathematical vectors – dense numerical representations that capture meaning and relationships.
This allows AI to perform semantic searches, finding results based on similarity of meaning rather than exact keywords. For example, if you search for “holiday by the sea,” a vector database could also return results for “beach vacation” because it understands the conceptual link.
Vector Databases in 5: Vector databases help AI find meaning, not just words.
9. Model Context Protocol (MCP)
Finally, MCP is a framework that helps AI agents connect seamlessly with external systems, APIs, and data sources. Instead of being limited to their own training data, agents using MCP can pull in live information, interact with business tools, and execute workflows across platforms. For example, an MCP-enabled agent could retrieve customer records from a CRM, analyse them, and then trigger a follow-up email campaign—all without human intervention.
MCP makes AI more versatile and practical in enterprise environments.
MCP in 5 : MCP is the bridge that connects AI to other tools.
What next and Getting Started
AI is not a single technology but a constellation of concepts – agents, RAG, XAI, ASI, reasoning models, vector databases, and MCP – that together define its capabilities and potential. Understanding these terms helps demystify AI and highlights both its current applications and future possibilities.
As AI continues to evolve, these building blocks will shape how businesses, governments, and individuals harness its power responsibly.
AI is a toolkit of ideas working together to change the world. When we look at what tool to use when, in reality there is not one better than the other it’s more about context of use, the platform you use it on, what your work provides, what you get included in your other software (for example Copilot in Windows, Office apps etc) and what task you are performing. Some AI’s are better at images, some at research and some at writing and analysis.
