I talk to a lot of business owners who know they ought to do something with AI but have no idea where to start.

The AI landscape feels like chaos to most business leaders - a bewildering array of technologies, terminologies, and techniques that seems to change weekly.

How on earth are they meant to make any lasting AI business decisions when it is all shifting so quickly?

Over the past four days, we've built a solid foundation of knowledge about how AI works. You now understand the evolution of these systems, how they function as prediction engines, the distinction between training and inference, and the critical importance of data.

But the question is still: "OK, but what do I actually DO with all this?"

I'm going to answer that question by giving you a practical implementation framework - a step-by-step roadmap you can use to deploy AI in your own business or help clients as a consultant.

No more paralysis by analysis. No more overwhelm. Just clear, actionable steps to move forward.

Let's get started:

The AI Implementation Maturity Model

Before we dive into the details, let's look at the big picture. Here’s an outline of a maturity model providing a lear progression from simple beginnings to more advanced implementations:

We covered RAG and fine tuning in the last Part - now we’re talking about how we actually step up to deploy these.

I know you’ll want to jump to the cool stuff in Level 3 but your company needs to grow at the same time as you are implementing these new tools. Step by step is best!

Level 1: Getting Started

Level 1 is all about quick wins and low-hanging fruit. You're using existing AI tools and services without any custom development or complex integration. Think of it as "off-the-shelf AI."

This level involves:

• Using public AI models through their interfaces (ChatGPT, Claude, Midjourney)

• Simple API integrations with existing tools

• Learning effective prompting techniques

• Establishing basic AI workflows

Tools and Technologies

At this level, you're primarily using tools like :

• General AI assistants: ChatGPT Plus, Claude, Bard

• Image generation: Midjourney, DALL-E, Stable Diffusion

• Simple automation tools: Zapier with OpenAI integration, Make.com

• Basic no-code tools: Bubble with AI plugins, Webflow with AI features

• AI-enhanced office tools: Microsoft Copilot, Google Workspace AI features

This is non-exhaustive obviously. There are MANY (too many!) tools at this level.

Real-World Examples

A marketing agency might use ChatGPT to draft initial content outlines, Midjourney to generate concept images, and Zapier to automate posting to social media platforms.

A law firm might use Claude to summarise legal documents, extract key clauses, and generate first drafts of standard correspondence.

A solo entrepreneur might use AI assistants for research, content creation, and email management without any custom development.

This is the bread and butter stuff we cover extensively in Prompt Entrepreneur Playbooks. And it is where all businesses and entrepreneurs need to start to find their footing.

When to Level Up

You should consider moving to Level 2 when:

• You find yourself constantly feeding the same context into AI tools

• Your prompts have become complex multi-page documents

• You have valuable proprietary data that could enhance AI outputs

• You're spending significant time on repetitive AI interactions

• You have a team of people who don’t necessarily have your level of AI sophistication

Common Pitfalls at Level 1

Watch out for shiny object syndrome (trying every new AI tool rather than mastering a few), unrealistic expectations (expecting perfect outputs without proper prompting), security blindspots (feeding sensitive information into public AI systems), and forgetting human oversight (deploying AI-generated content without proper review).

Level 2: Building Custom Solutions

Once the above seems old hat it’s time to move up a level. Specifically we’re going to put together a RAG system using our company’s “knowledge”.

What It Looks Like

Level 2 is where you start creating more customised AI solutions that leverage your data. You're no longer just using public tools; you're building simple but tailored applications off the back of internal knowledge.

This level involves:

• Implementing Retrieval-Augmented Generation (RAG) systems

• Creating specialised AI workflows for specific use cases

• Integrating AI capabilities into existing products or services

• Some custom development, often using frameworks and libraries

Tools and Technologies

At this level, you're primarily using:

• Vector databases: Pinecone, MongoDB, Weaviate, Chroma

• RAG frameworks: LangChain, LlamaIndex

• Embedding APIs: OpenAI embeddings, Cohere embeddings

• No/low-code AI platforms: Retool, Bubble, FlutterFlow with AI components

• Document processing: PyPDF, LangChain document loaders

Real-World Examples

A real estate company might build a RAG system with their property listings, market analyses, and historical transaction data, allowing agents to query this knowledge base for specific client needs.

A software company might create an internal AI assistant that has access to their codebase, documentation, and support tickets to help developers solve problems faster.

An e-commerce business might implement a product recommendation system that combines general AI capabilities with their specific product catalog and customer purchase history.

When to Level Up

You should consider moving to Level 3 when:

• Your RAG system struggles with complex reasoning about your domain

• You need more consistent adherence to specific formats or terminology

• You have enough high-quality training data to support fine-tuning

• The business value justifies deeper investment in customisation

Common Pitfalls at Level 2

Be careful of poor data quality (GIGO - Garbage In, Garbage Out), chunking issues (improper document chunking leading to lost context), over-reliance on RAG (sometimes fine-tuning is necessary, see next step), and neglecting the user experience (building technically sound systems that are difficult for end-users).

Level 3: Advanced Implementation

Let’s now layer in fine-tuning on top of the previous work to really refine our systems.

What It Looks Like

Level 3 represents a significant step up in sophistication. You're now building deeply customised AI capabilities that are specifically tailored to your domain or use case.

This level involves:

• Fine-tuning models for specific use cases

• Building sophisticated applications with multiple AI components

• More extensive custom development

Tools and Technologies

At this level, you're primarily using:

• Fine-tuning services: OpenAI fine-tuning API, Anthropic fine-tuning, Hugging Face

• Model deployment platforms: Replicate, Modal

• Specialised AI services: OpenAI Assistants API, Azure AI services

• Advanced orchestration: Langfuse, LiteLLM

• Monitoring tools: Weights & Biases, Helicone

• Development frameworks: LangChain (advanced features), DSPy

Real-World Examples

A healthcare company might fine-tune models on medical documentation to create an AI system that can extract patient information, suggest diagnosis codes, and draft preliminary reports for physician review.

A financial services firm might build a comprehensive system that combines market data analysis, regulatory compliance checking and personalised client recommendations

A manufacturing company might implement an AI quality control system that analyses images and sensor data to detect defects, predict maintenance needs, and optimise production processes.

When to Level Up Further

As your AI journey progresses beyond Level 3, you might eventually consider more advanced implementations when AI has proven so valuable that it needs to be embedded throughout the organisation, or your business strategy relies on AI as a core competitive advantage. But for most businesses, focusing on mastering Levels 1-3 will get them a long way ahead of their competitors.

Common Pitfalls at Level 3

Watch for over investing in fine-tuning (when RAG might work just as well), data leakage (not properly separating training/test data), fragmented implementation (building isolated systems), scaling too fast (implementing across too many areas simultaneously), and neglecting governance (ie. ignoring privacy and ethical considerations).