Raj Sehgal has 20+ years of enterprise software experience. He has run 4 marathons and is a 3-time Moth StorySLAM Champion. Those communities shaped how he thinks about showing up for people — which led him here, helping local businesses navigate AI in a practical, human way.

Maybe ChatGPT. Maybe something else. You asked it a question. It gave you an answer. It was impressive.

But has anything actually changed in how your business runs?

For most businesses — not yet. That gap between trying AI and actually using it, consistently, inside the business — is exactly what this session is designed to close.

Many business owners have already experimented with AI tools. They have asked questions, generated content, or tested prompts. But for most, AI has not yet become part of how the business actually works. This session starts with that reality — and builds from there.

AI does not think. It does not understand. It finds patterns in large amounts of data and uses those patterns to generate responses. That is the whole thing.

At its core, AI learns patterns from large amounts of data and applies those patterns to new situations. Understanding this removes a lot of confusion and helps set realistic expectations for how AI can help in a business context.

Instead of being programmed with rules for every situation, the system learns from examples. Show it enough examples, and it starts to generalize.

Machine learning is the core technique behind most AI. Rather than writing explicit rules for every case, the system learns from data. The more data it sees, the better it gets at applying what it has learned to new situations it has never encountered before.

It does not know what a mile is. It has not traveled anywhere. It saw the pattern in the training data — and now it can predict any new value you give it.

If a system sees enough examples of miles-to-kilometers conversions, it learns the underlying ratio and can apply it to new values. It does not truly understand miles or distance. It simply learned the pattern. This is what most machine learning does — at scale.

AI is already a part of everyday life. It predicts traffic, filters email, recommends content, and detects unusual activity. You are already using AI regularly — you just may not have thought of it that way. This is not new technology. It is familiar technology you are already trusting.

Earlier AI was mainly used to predict outcomes: recommendations, fraud detection, traffic. Today, AI can generate new content — text, images, plans, responses. This shift makes AI feel interactive and immediately useful in everyday business situations.

Large language models were trained by taking text, hiding the last word, and asking the model to predict it. Every wrong prediction adjusted the model slightly. After billions of iterations across the entire internet, the model developed a deep understanding of how human language works — not just grammar, but context, meaning, tone, and intent. Same principle as the miles example. Just at a scale nobody can visualize.

The practical significance of LLMs for small businesses is not just automation — it is comprehension. These models understand intent, detect tone, adapt to context, and reason through nuance in a way no previous technology could.

No technical knowledge required. No special commands. Just describe what you need, and it responds. This is what opened the door for everyone — including local businesses.

Instead of requiring technical expertise, you can now communicate with AI the same way you would with a capable colleague. This accessibility is what makes AI relevant for small and local businesses in a way it never was before.

Most people use AI in a scattered way. They ask a question, get an answer, and move on. There is no structure or repeatability, which means the results do not add up over time. The tool feels interesting but not essential.

A system is simple. It is a repeatable workflow — the same input, the same process, a consistent and useful output. When AI becomes part of a workflow, it starts creating real value.

One good system is worth a thousand one-off prompts.

Instead of using AI for isolated tasks, the focus should be on simple, repeatable systems. When AI is part of a workflow, it starts to create real and consistent value. The goal is not to do everything at once, but to start with one area where a repeatable system would save time or improve quality.

The AURA Way is a four-step adoption journey designed for any business starting with AI. Activate by connecting to one real data source. Use it every day until it feels natural. Rely on it once you have found the workflows that consistently save time. Automate those workflows so they run without you. Each step builds confidence before the next one begins.

Your files. Your emails. Your documents. Your processes. When AI can see your actual business context — instead of operating in a generic vacuum — it becomes genuinely useful.

You do not need a big system. You need one connection that saves you real time.

Tools like Claude Desktop and Claude Cowork let you do exactly this — today, for free or very low cost.

Tools like Claude Desktop and Claude Cowork allow you to connect AI to your local files and documents. Instead of asking generic questions, you can ask Claude about your actual business — and get answers that are relevant and actionable. This is the foundation of practical AI use for small businesses.

These four workflows are the most common and highest-impact entry points for AI in small businesses. Start with whichever one removes the most friction from your day.

The card scan is instant and visual — something everyone in this room can relate to right now.

If you want help thinking through where AI could fit in your business — simply or seriously — I am happy to continue the conversation.

No pitch. No pressure. Just a practical conversation.

If this session raised questions about your own business and where AI could help, reach out. The best next step is usually a short conversation — not a sales process.