Understanding AI's evolution helps you recognize why today's tools are fundamentally different from previous attempts. This isn't just another tech trend—it's the culmination of 70 years of research reaching a critical inflection point.
Alan Turing & Early Concepts: The "Turing Test" - can a machine convince a human it's human?
Business Impact: Pure theory, no practical applications
Rule-Based AI: If-then logic systems for specific domains
Business Impact: Limited success in narrow fields like medical diagnosis
Statistical Learning: Algorithms that could learn from data
Business Impact: Early recommendation systems, basic pattern recognition
Infrastructure Revolution: Massive datasets + cloud computing
Business Impact: Google search, early personalization
Neural Networks Renaissance: Image recognition surpasses humans
Business Impact: Smartphone cameras, voice assistants, fraud detection
"Attention Is All You Need": Google's paper that changed everything
Business Impact: Foundation for all modern language AI
What Changed Everything:
Result: AI that can understand, reason, and create like never before
Before Transformers: AI processed text sequentially (word by word)
After Transformers: AI processes entire documents simultaneously, understanding relationships between all words
Sequential processing
Limited memory
Struggled with long text
Parallel processing
Global attention
Handles entire documents
This is why ChatGPT can write coherent emails while older AI could barely complete sentences.
What was the key breakthrough that enabled modern AI capabilities?
LLMs are sophisticated pattern recognition systems that learned to predict what comes next by studying virtually all human text. This simple concept enables remarkably complex capabilities.
Click on each step to understand how LLMs process your requests:
What Happens:
• Text is converted into "tokens" (pieces of words or concepts)
• Example: "CFO report" becomes ["CFO", " report"] tokens
• This allows the AI to work with standardized units of meaning
What Happens:
• Each token gets analyzed in relation to every other token
• The model builds understanding of relationships and meaning
• This is where the "attention" mechanism works its magic
What Happens:
• Compares current context to patterns seen in training data
• Identifies the most likely and appropriate next words
• Uses statistical probability to guide decisions
What Happens:
• Produces text token by token, each informed by all previous context
• Can generate anything from emails to financial analysis
• Maintains coherence throughout the entire response
~1.8 Trillion Parameters
Trained on ~13 trillion tokens
Context: 128k tokens (~100 pages)
~200 Billion Parameters
Optimized for reasoning
Context: 200k tokens (~150 pages)
~137 Billion Parameters
Multimodal capabilities
Context: 1M tokens (~750 pages)
For Context: The human brain has ~86 billion neurons. These models have trillions of connections.
The Challenge: LLMs are trained on general knowledge, but you need AI that knows your specific company data, policies, and context.
Example: "What was our Q3 EBITDA margin?"
RAG makes AI truly useful for internal business applications by grounding it in your actual data.
What makes RAG (Retrieval-Augmented Generation) valuable for business applications?
LLMs can handle any task that involves:
They excel at tasks that require understanding context, following instructions, and generating human-like output.
LLMs don't just follow rules—they understand intent. When you ask for a "financial summary for the board," the model understands:
And with RAG technology, it can access your actual financial data to provide real, actionable insights.
Now that you understand how LLMs and RAG work, let's explore the AI applications available to you. This isn't theoretical—these are the tools you can use today for immediate productivity gains.
Your organization uses Cortex as the secure AI foundation that powers user-friendly applications designed specifically for business users like you.
• Easy-to-Use Applications: No technical knowledge required
• Automatic Optimization: Apps choose the best AI model behind the scenes
• Enterprise Security: Cortex ensures your data never leaves the organization
• Compliance Ready: Built-in audit trails and governance
Your specialized AI application for Finance operations
Click for finance-specific features
Create custom RAG applications for your team
Click for capabilities
Integrated across Office suite
Click for capabilities
The secure AI foundation (API only)
Click for technical details
You need to analyze budget variances and identify cost optimization opportunities. Which tool should you use first?
With your specific application ecosystem, here's the time you can reclaim:
Total: 65 minutes of high-value time returned to strategic leadership and decision-making.
Progressive adoption using your actual applications:
By month's end, you'll have experienced your complete AI application ecosystem and identified the highest-impact use cases for your role.
Every AI term explained in plain business language. Click on any term to see the definition, why it matters to you as a CFO, and real-world examples.
Simple Definition: Computer systems that can perform tasks that typically require human intelligence.
Why CFOs Care: AI can automate financial processes, provide insights from data, and support decision-making.
Example: Software that can read invoices, categorize expenses, and flag unusual transactions.
Simple Definition: A type of AI that improves its performance by learning from examples in data.
Why CFOs Care: ML systems get better over time, making more accurate predictions and classifications.
Example: Expense categorization that becomes more accurate as it processes more receipts.
Simple Definition: AI systems trained on massive amounts of text that can understand and generate human-like language.
Why CFOs Care: LLMs can write reports, answer questions, analyze documents, and communicate in natural language.
Example: ChatGPT, Claude, or GPT-4 that can help draft board presentations or analyze financial documents.
Simple Definition: AI that can create new content like text, images, or code based on prompts.
Why CFOs Care: Can generate financial reports, create presentations, and produce analysis documents.
Example: AI that writes a complete variance analysis report from your financial data.
Simple Definition: A method for AI to process all words in a document simultaneously, understanding relationships between them.
Why CFOs Care: This breakthrough enabled AI to understand context and generate coherent, relevant responses.
Example: Why ChatGPT can write a coherent email instead of just random words.
Simple Definition: A method that allows AI to search through your company's documents and data before generating a response.
Why CFOs Care: Makes AI responses specific to your company's actual data, policies, and context.
Example: AI that can answer "What was our Q3 EBITDA?" by looking at your actual financial reports.
Simple Definition: The process of breaking text into smaller pieces (tokens) that AI can understand and process.
Why CFOs Care: Understanding tokens helps you know AI limitations and costs (often charged per token).
Example: "CFO report" becomes separate tokens ["CFO", " report"] for the AI to process.
Simple Definition: The internal connections in an AI model that store learned information (like synapses in a brain).
Why CFOs Care: More parameters generally mean more capable AI, but also higher costs and complexity.
Example: GPT-4 has ~1.8 trillion parameters, making it very capable but expensive to run.
Simple Definition: The maximum amount of text an AI model can consider at one time when generating responses.
Why CFOs Care: Determines whether AI can analyze your entire financial report or just small sections.
Example: Claude can handle ~150 pages at once, while GPT-4 handles ~100 pages.
Simple Definition: The instructions, questions, or requests you give to an AI system.
Why CFOs Care: Better prompts lead to better results. Learning to prompt effectively is a key skill.
Example: "Analyze our Q3 variance report and highlight the top 3 concerns" is a clear, effective prompt.
Simple Definition: AI systems loosely inspired by how the human brain processes information through connected nodes.
Why CFOs Care: The foundation technology behind most modern AI capabilities.
Example: The underlying technology that enables AI to recognize patterns in your financial data.
Simple Definition: The massive amounts of text, images, or other data used to teach AI systems.
Why CFOs Care: Quality of training data affects AI performance and potential biases.
Example: LLMs trained on books, websites, and documents to learn language and knowledge.
Simple Definition: A set of rules or instructions that tells a computer how to solve a problem or complete a task.
Why CFOs Care: Different algorithms are suited for different business problems.
Example: An algorithm that determines which invoices need manual review based on risk factors.
Simple Definition: AI that can understand and work with multiple types of input like text, images, audio, and video.
Why CFOs Care: Can process financial documents with charts, graphs, and mixed content types.
Example: AI that can analyze both the text and charts in your quarterly earnings presentation.
Simple Definition: A technique that allows AI to focus on the most relevant parts of input when generating responses.
Why CFOs Care: This is what makes modern AI so much better at understanding context and generating relevant answers.
Example: When analyzing a financial report, AI can focus on the variance section when answering questions about performance gaps.
Simple Definition: Your company's centralized platform that provides secure access to multiple AI models and tools.
Why CFOs Care: Ensures data security, compliance, and governance while giving access to best-in-class AI capabilities.
Example: One secure platform where you can access GPT-4, Claude, and other AI models without data leaving your organization.
Simple Definition: Massive networks of specialized computers (GPUs) accessed over the internet that provide the computing power needed for AI.
Why CFOs Care: Understanding the infrastructure helps with cost planning and vendor negotiations for AI services.
Example: The cloud infrastructure that allows you to run complex AI analysis without buying expensive hardware.
Simple Definition: A test proposed by Alan Turing where a machine is considered intelligent if a human can't tell they're talking to a machine.
Why CFOs Care: Helps understand how we measure AI capability and why modern AI feels so human-like.
Example: Modern AI like ChatGPT can often pass informal Turing tests in business conversations.
Simple Definition: An older type of AI that processed text sequentially (one word at a time) and had trouble remembering information from earlier in long documents.
Why CFOs Care: Understanding RNN limitations helps you appreciate why modern Transformer-based AI (like ChatGPT) is so much better at understanding context.
Example: RNNs might forget the beginning of a financial report by the time they reach the end, while Transformers can consider the entire document simultaneously.