AI concepts, without the jargon

Think of Artificial Intelligence as the goal: make computers do things that normally require human thinking — understanding language, recognizing images, making decisions.

Machine Learning is one way to get there. Instead of writing rules for every possible situation (which is impossible for most real-world problems), you show the computer thousands of examples and let it figure out the patterns on its own.

Here's an analogy: imagine you're training a new employee to sort your incoming mail. You could write a 200-page manual with rules for every scenario. That's traditional programming. Or you could sit with them for a week, show them how you sort it, and let them learn from watching you. That's machine learning — the computer learns from examples rather than instructions.

When someone says "we use AI," they're often using machine learning under the hood. When someone says "we use machine learning," they're being more specific about how. Both are correct — one is just more precise.

How the major AI concepts relate to each other

If machine learning is teaching a computer by showing it examples, deep learning is giving the computer a brain-like structure to process those examples in layers — each layer picking up on something more complex than the last.

Think of it like how you recognize a friend's face. Your eyes first pick up edges and shapes. Then your brain assembles those into features — a nose, eyes, a jawline. Then it combines those features into a face you recognize. Deep learning works the same way: the first layer finds simple patterns, the next layer combines them into more meaningful ones, and so on — layer after layer — until the system can understand something remarkably complex.

This is what makes things like voice assistants, self-driving cars, and AI-generated images possible. Regular machine learning might struggle with these tasks because the patterns are too complex. Deep learning can handle them because its layered approach lets it learn at a much deeper level — hence the name.

The trade-off is that deep learning needs a lot more data and computing power to train. For simpler problems — like predicting next month's sales — traditional machine learning often works just as well and costs much less. Deep learning shines when the problem is genuinely complex: understanding language, recognizing images, or generating new content.

Most AI you've heard about before 2023 was analytical — it looked at data and told you something about it. "This email is spam." "This customer is likely to churn." "This product photo contains a defect." It classified, predicted, and sorted. Useful, but it didn't create anything new.

Generative AI flipped that. Instead of just analyzing, it creates. It writes emails, drafts reports, generates images, writes code, composes music. ChatGPT, DALL-E, Midjourney, Claude — these are all generative AI. You give them a prompt, and they produce something that didn't exist before.

Think of the difference between a food critic and a chef. The food critic (analytical AI) evaluates what's already been made. The chef (generative AI) creates something new. Both are valuable, but they do fundamentally different things.

The business impact has been enormous because generative AI can handle tasks that previously required a human's creative judgment — drafting first versions of documents, summarizing long reports, creating personalized marketing content, writing software. It's not perfect, and it needs human oversight, but it's fast. What used to take a person hours can often get to 80% done in minutes.

A Large Language Model is exactly what the name says: a very large AI model that's been trained on an enormous amount of text — books, websites, articles, code, conversations — to understand and generate human language.

Think of it like someone who's read every book in the world's biggest library. They haven't memorized everything word for word, but they've developed an incredibly deep intuition for how language works, what topics relate to each other, and how to communicate clearly about almost anything.

ChatGPT, Claude, Gemini, and Llama are all LLMs. When you have a conversation with one of these, it's not searching a database for your answer — it's generating a response word by word based on the patterns it learned from all that training text. That's why it can be so flexible: you can ask it to explain quantum physics, write a birthday card, summarize a legal document, and debug code — all in the same conversation.

The "large" part matters. These models have billions of parameters (think of parameters as knobs that got tuned during training). That scale is what gives them their remarkable versatility. But it also means they're expensive to build — which is why only a handful of companies train them from scratch, while everyone else builds applications on top of them.

An algorithm is just a set of instructions — like a recipe. "Take the eggs, crack them into a bowl, whisk for 30 seconds, pour into the pan." Follow the steps in order, and you get the result every time. A computer algorithm works the same way, except instead of making breakfast, it might be sorting your customer list, calculating shipping costs, or deciding which emails are spam.

The reason this word comes up in AI conversations is that machine learning uses algorithms to learn from data. The algorithm is the method — the specific approach the computer follows to find patterns. Some algorithms are better at recognizing images, others are better at predicting numbers, and others are better at understanding text.

Think of it like cooking techniques. Grilling, braising, and sautéing are all ways to cook food, but each works better for different ingredients. AI algorithms are the same — different techniques for different types of problems. The skill is knowing which one to use and when.

You don't need to know the names of specific algorithms to make good AI decisions. What matters is that whoever builds your AI system knows which approach fits your problem — and can explain why they chose it in terms that make sense to you.

An AI model is what you get after the learning is done. You fed the computer millions of examples, it found the patterns — and now it has a "brain" that can apply what it learned to new situations it's never seen before.

Think of it like a chef who's cooked 10,000 meals. The chef is the model — all that experience is baked in, and now they can walk into any kitchen and make something good, even with ingredients they've never worked with.

ChatGPT, Claude, and Gemini are all AI models — specifically large language models trained on enormous amounts of text. But not all models are that big. Some are small, specialized models trained only on your data to do one job really well — like reading invoices or predicting which customers might leave.

The key thing to know: there's no single "best" model. The right model depends on your problem, your data, and your budget. A small custom model that's 98% accurate on your specific task often beats a giant general-purpose model that's 80% accurate.

If fine-tuning is onboarding a smart generalist, training a custom model is raising a specialist from the ground up. You're not starting with someone else's AI — you're building one specifically designed for your task, trained on your data, optimized for your goals.

Think of a dog trained from birth to be a guide dog versus teaching an adult pet dog to guide. Both can work, but the purpose-built one is more reliable for that specific job.

Custom models are common for things like demand forecasting ("how many units of this product will we sell next month?"), fraud detection ("does this transaction look suspicious?"), or quality control ("does this part have a defect?"). These are narrow, well-defined problems where you have lots of historical data and need very high accuracy.

Training custom models requires good data and some patience — but the result is something that's truly yours, runs on your infrastructure, and doesn't depend on any third-party AI provider. For the right use cases, they outperform general-purpose AI significantly.

Imagine you hired a brilliant new employee who graduated top of their class. They're smart, well-read, and capable — but they've never worked in your industry. They don't know your terminology, your processes, or what "good" looks like at your company.

Fine-tuning is the onboarding process. You take an already-capable AI model and train it further on your specific data — your documents, your examples of correct outputs, your domain language. The result is a model that still has all its general intelligence but now speaks your language and understands your standards.

For example, a general AI model might read a mortgage document and get 70% of the fields right. A fine-tuned version — one that's been shown thousands of your actual mortgage documents with correct extractions — might hit 96%. Same underlying brain, but now it knows what a "DTI ratio" is and where to find it on page 3.

Fine-tuning makes sense when you need consistently high accuracy on a specific task and general prompting isn't cutting it. It takes some upfront effort but pays off when you're running thousands of similar tasks.

Most AI you've used is reactive — you ask it something, it responds, and then it sits there waiting for your next question. An AI agent is different. It can plan, take actions, use tools, and follow through on multi-step tasks without you holding its hand at every step.

Imagine the difference between asking someone for directions versus hiring a driver. The directions person gives you information — you still have to do the driving. The driver takes you there. AI agents are the driver.

A practical example: you ask an AI agent "Why are sales down in the northeast this quarter?" Instead of giving you a generic answer, it goes and pulls your sales data, compares it to last year, checks if there were any shipping delays, looks at competitor pricing changes, and comes back with an actual analysis — citing specific numbers from your systems.

The important nuance: good AI agents aren't fully autonomous robots. The best ones are designed to do the heavy lifting but check in with humans when they hit something unexpected or need a judgment call. Think of them as very capable assistants, not replacements.

RAG is like a search engine — but instead of matching keywords, it actually understands your question. You can ask it in natural language, with typos, grammar mistakes, vague phrasing — and it still finds the right information from your documents, databases, or knowledge base.

Here's the problem RAG solves: AI models like ChatGPT are trained on public internet data. They know a lot about the world, but they know nothing about your company's internal policies, your product specs, your HR handbook, or your client contracts. If you ask them a question about your business, they'll either say "I don't know" or — worse — confidently make something up.

RAG fixes this by giving the AI access to your information. When you ask a question, the system first searches your documents to find the relevant pieces, then hands those pieces to the AI along with your question. The AI writes its answer based on your actual data — not its imagination.

Think of it like the difference between asking someone a question from memory versus asking them to look it up in your filing cabinet first. The answer is grounded in your real information, and it can tell you exactly which document it found it in.

Every business has someone whose job involves opening PDFs, scans, or forms — finding specific information, typing it into another system, and moving on to the next one. Document AI does that reading and data entry automatically.

It's like hiring a very fast, very patient reader who never gets tired, never skips a field, and can process a 200-page document in seconds instead of 40 minutes. They can handle messy handwriting, crooked scans, and documents that look different every time — and still pull out the right data.

What makes modern Document AI different from old-school OCR (the technology that's been around for decades) is that it actually understands what it's reading. Old OCR just converts images to text — it has no idea what a "borrower name" or "invoice total" means. Document AI knows what it's looking for, where to find it, and how to validate that what it found makes sense.

This is one of the most immediately impactful AI applications for most businesses, because the ROI is obvious: work that took a person 40 minutes now takes 38 seconds, with fewer errors.

Think about the tasks your team does over and over: pulling data from one system and entering it into another, sending the same follow-up emails after every meeting, generating the same weekly report by copying numbers from five different dashboards. Workflow automation connects those steps together so they happen automatically.

Imagine a row of dominoes. Right now, a person has to knock each one over individually — they copy a file here, send a notification there, update a spreadsheet somewhere else. Workflow automation lines those dominoes up so that when the first one falls, the rest follow on their own.

What makes AI-powered workflow automation different from old-school automation (like simple email rules or scheduled scripts) is that it can handle messy, real-world situations. Traditional automation breaks when something unexpected happens — a field is missing, a document has a different format, a customer writes their request in a weird way. AI-powered workflows can understand context, adapt to variations, and make reasonable decisions without crashing.

The best part: you don't have to automate everything at once. Start with one painful, repetitive process — the one your team complains about most — automate that, and build from there.

Here's a truth that most AI vendors won't tell you: the fanciest AI in the world is useless if it's working with bad data. Garbage in, garbage out — no algorithm can fix that.

Data foundations is the work of getting your house in order before (or while) you build AI. It means connecting your scattered data sources, cleaning up inconsistencies, establishing reliable pipelines that keep everything in sync, and building a structure that lets you actually ask questions of your data.

Think of it like building a house. You can pick the most beautiful countertops and light fixtures, but if the foundation is cracked and the plumbing doesn't connect, nothing works. Data foundations is the plumbing, the electrical, and the foundation — not glamorous, but everything depends on it.

Most businesses that try AI and fail don't fail because the AI was wrong. They fail because their data was scattered across 12 systems, formatted differently in each one, with duplicates and gaps everywhere. Fixing that first — or as part of the AI project — is what separates a prototype that demos well from a system that actually runs your business.

Most businesses don't have a data problem — they have a data fragmentation problem. Your sales numbers live in Salesforce, your marketing data is in Google Analytics and your ad platforms, your inventory is in a different system, your customer support tickets are somewhere else, and your accounting lives in QuickBooks or Xero. Each system has a piece of the puzzle, but nobody can see the whole picture.

Data unification brings all of those pieces together into a single, connected view. It's like going from having 10 different photo albums scattered around your house to having one organized library where you can find anything in seconds.

This isn't just about convenience — it's about the questions you can suddenly answer. "Which marketing channel brings in customers who actually stick around and spend the most?" requires combining marketing data, sales data, and customer retention data. When those live in separate systems, that question is nearly impossible to answer. When they're unified, it's straightforward.

Data unification is often the first step in any serious AI initiative, because AI needs connected, comprehensive data to produce reliable insights. It's also where businesses often see their first "aha" moment — just being able to see everything in one place reveals patterns and problems that were invisible before.

Remember when every phone had a different charger? Then USB came along and one cable worked for everything. MCP is the USB standard for AI.

Here's the problem it solves: your business probably uses a dozen different tools — your CRM, your accounting software, your email, your project management system, your database. If you want AI to actually do useful work, it needs to connect to these tools. Without a standard, every single connection has to be custom-built from scratch. That's expensive and fragile.

MCP creates a common language so that any AI model can connect to any tool that supports the protocol. Build the connection once, and it works across different AI systems. If you switch from one AI provider to another, your connections still work.

For business leaders, the practical impact is: MCP makes AI integrations faster to build, cheaper to maintain, and less likely to break when you change tools. It's infrastructure — you won't see it directly, but it's the reason your AI assistant can pull data from Salesforce, check your calendar, and update your spreadsheet in one go.

Vibe coding is a new trend where instead of writing software the traditional way, you describe what you want in plain English — "build me a dashboard that shows sales by region" — and an AI tool writes the code for you. You review it, tweak your description, and iterate until it looks right.

It's like telling a contractor "I want a modern kitchen with an island" instead of drawing the blueprints yourself. You describe the vibe, and they build it.

For simple prototypes and internal tools, this can be incredibly fast. A weekend project that would have taken weeks of development time can sometimes be done in hours. That's genuinely powerful.

The risk — and this is important — is that speed and correctness are not the same thing. Vibe-coded software often looks great on the surface but has problems underneath: security holes, data handling issues, edge cases that crash the system, code that nobody can maintain or debug because no human actually understands how it works. For a personal side project, that's fine. For software that handles your customers' data or runs your business operations, it's a serious liability.

The best approach is somewhere in the middle: use AI coding tools to accelerate development, but have experienced engineers review, test, and own the code that goes to production.