What is AI (Without the Jargon)

The Simple Version

Artificial intelligence is software that can handle tasks that normally require human judgment. Not because it "thinks" the way people do, but because it has learned from enormous amounts of data to recognize patterns and make decisions.

That's it. Strip away the hype, and AI is pattern recognition at scale.

What Most People Get Wrong

The biggest misconception in business today is conflating different levels of AI. Here's the practical breakdown:

Traditional Software

Does exactly what you program it to do. If-then rules. Spreadsheet formulas. Your accounting software. Predictable, reliable, but rigid — it can't handle anything it wasn't explicitly designed for.

Machine Learning

Software that improves with data. Instead of programming every rule, you feed it examples and it figures out the patterns. Your spam filter learns what spam looks like. Your recommendation engine learns what you're likely to buy next.

Large Language Models (LLMs)

The breakthrough that changed everything. Models like GPT-4, Claude, and Gemini were trained on vast portions of the internet and can understand and generate human language. They can write, analyze, summarize, reason, and converse — capabilities that were science fiction five years ago.

What AI Can Actually Do Today

Forget the marketing slides. Here's what works reliably right now:

• Analyze and summarize large volumes of text — contracts, reports, customer feedback, research papers
• Generate content — drafts, emails, documentation, code, marketing copy
• Answer questions about your data, documents, and processes
• Classify and route — sort incoming requests, categorize data, triage support tickets
• Extract structured information from unstructured sources — invoices, forms, emails
• Translate between languages with near-human quality
• Write and debug code — accelerating development by 30-50%

What AI Cannot Do (Yet)

Being honest about limitations is how you avoid expensive mistakes:

• Replace human judgment on high-stakes decisions — it can inform decisions, not make them
• Guarantee accuracy — LLMs can produce confident-sounding wrong answers (called "hallucinations")
• Understand your business without context — AI needs to be configured with your specific data and processes
• Work unsupervised on critical tasks — it needs guardrails, validation, and human oversight

The Real Opportunity: AI Agents

Here's where it gets interesting for business. A basic AI interaction is a question and an answer — you ask ChatGPT something, it responds. Useful, but limited.

An AI agent goes further. It can:

1. Break complex tasks into steps
2. Use tools (databases, APIs, calculators)
3. Make decisions about what to do next
4. Execute multi-step workflows autonomously
5. Learn from feedback and adjust

The difference between a chatbot and an agent is the difference between a search engine and an employee. One answers questions. The other gets work done.

How to Think About AI Investment

If you're evaluating AI for your company, here's the framework:

Start With the Problem, Not the Technology

The worst AI projects begin with "we need to use AI" and go looking for problems. The best begin with a specific, expensive, repetitive problem and ask whether AI can solve it better than current approaches.

Look for the 80/20

AI doesn't need to be perfect. It needs to be better than the alternative. If your team spends 40 hours a week on research that AI could do in 4 hours at 85% quality — and a human spends 30 minutes reviewing and fixing the remaining 15% — you've just saved 35+ hours per week.

Build, Don't Buy (Off-the-Shelf)

Generic AI tools (ChatGPT, Copilot) are useful for individuals. But to transform a business process, you need AI that understands your specific data, workflows, and rules. That means custom-built solutions — which is exactly what we do.