The AI Onion Model

AI (artificial intelligence) is like an onion. 🧅

Not because it makes you cry (though, if you're trying to learn all of this on your own, that might feel true to) -but because it has layers.

And those inner layers, need the outer ones to work.

If you pick the wrong layer for your problem, you risk wasting time, money, and resources with no payoff.

Here’s how it breaks down:

Machine Learning (ML)

This is your outer layer.

It’s simply a machine trained to learn: input goes in, output comes out, and over time the system gets better at predicting the right answer.

Machine Learning Tools

So, how do we actually use machine learning tools as business owners?

These tools are best for structured tasks with clear inputs/outputs (like churn prediction, price optimization, segmentation).

MonkeyLearn
→ Use: Text classification and sentiment analysis
→ Layer: Machine Learning
→ Why it works: You train the model with your own labeled data, and it improves with feedback. Great for tagging support tickets, product reviews, or survey responses.
BigML
→ Use: Predictive modeling for forecasting, sales, or inventory
→ Layer: Machine Learning
→ Why it works: Offers easy-to-use supervised/unsupervised ML pipelines for non-technical users. Think: Excel, but smarter.

Deep Learning (DL)

This sits inside ML.

DL uses artificial neural networks - layers of computations that process data in human-brain-like ways.

These systems can be opaque.

You often don't know how the model reached a decision.

But they’re powerful.

They take huge volumes of data, smash them into thousands or millions of parameters, and extract patterns in ways we might not fully understand.

Deep Learning Tools

Deep learning tools are perfect for research.

They're also great for large data environments, image/video recognition, and personalization at scale.

Some examples include:

Clarifai
→ Use: Visual recognition (identify objects, faces, logos, etc.)
→ Layer: Deep Learning
→ Why it works: Trained on millions of images, this platform uses convolutional neural networks to deliver high-accuracy detection and tagging.
Amazon Personalize
→ Use: Real-time recommendation systems (for e-commerce, media, etc.)
→ Layer: Deep Learning
→ Why it works: Powers Amazon’s own recommendation engine—uses DL to personalize content based on real-time user behavior and context.

Large Language Models (LLMs)

This is where the world goes “oh, that’s what AI is.”

ChatGPT, Gemini, Claude... they’re LLMs powered by deep learning, trained on massive datasets.

When you ask a question, it predicts the most probable response based on all its learned patterns.

Not because it "knows," but because it’s optimized to answer.

Sometimes it nails it, sometimes it doesn’t, but those outputs are getting better all the time.

Large Language Model (LLM) Tools

LLMs (large language model) tools are probably what you think of when it comes to AI tools.

These are the tools that many of us learn first to help with marketing content generation, internal knowledge access, and this includes conversational AI.

You might already be using these tools.

And if not?

You really should be.

Because these AI tools aren't the future; they're necessary right now.

ChatGPT (OpenAI)
→ Use: Writing, brainstorming, customer service prompts, automation workflows
→ Layer: LLM (built on deep learning)
→ Why it works: Trained on vast language datasets; predicts likely word sequences to generate coherent, human-like answers.
Claude (Anthropic)
→ Use: Safer, more “steerable” chatbot interactions for sensitive data
→ Layer: LLM
→ Why it works: Tuned with a focus on alignment and ethical constraints; helpful for healthcare, legal, or HR-sensitive use cases.
Writer
→ Use: Enterprise-grade content generation that stays on-brand
→ Layer: LLM with fine-tuned parameters
→ Why it works: You can feed it your own style guides, tone of voice, and data—it responds accordingly. Great for marketing and comms teams.

But before you go down the rabbit hole of exploring a million different AI tools, pause.

Ask yourself - what does your business really need right now?

And based on your business needs, which AI models best meet those needs?

Once you know what the models do, and which model each tool is using, then you can pick the tool that matches what you need.

In this video, you’ll learn:
• Why understanding AI’s “layers” matters more than hype
• How machine learning, deep learning, and LLMs actually work together
• What most businesses get wrong when they say, “We’re using AI”
• How to avoid wasting money on the wrong kind of model
• Why choosing the right AI can be a game changer

We're breaking down terms - moving beyond the buzzwords to help business owners get the results that they need.

We’re translating complex AI theory into decisions that actually move your business forward.

Real leverage doesn’t start with software.

It starts with clarity.

Need help implementing AI strategically in your business?

Book a consult with me by clicking here.

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Keep going.

It’s worth it.

– James & The Disruption Continuum Team