Raj Sehgal has 20+ years of enterprise software experience, most recently leading product development at Flexera with teams across four countries. He has run 4 marathons and 15 half-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?

Many business owners have already experimented with AI tools. They have asked questions, created 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.

That gap between trying AI and actually using it — consistently, usefully, inside the business — is exactly what this session is designed to bridge.

There is a real gap between experimenting with AI and integrating it into how a business operates. This is normal. The goal today is to close that gap by starting with clarity, not tools.

Before choosing a tool. Before setting up a system. Before doing anything.

You need a clear picture of what AI actually is and how it works. Everything else gets easier from there.

Jumping straight into tools or use cases often creates more confusion than value. Without a clear understanding of what AI is at a basic level, it is difficult to use it effectively. This session starts with clarity so everything else becomes easier.

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.

It has been developing quietly in the background for decades. What feels new today is not a sudden invention — it is a more visible, more accessible version of technology that has been building for years.

What changed recently is not what AI can do. It is how easy it became to use it.

AI is not something that suddenly appeared. It has been evolving for many years. The recent wave of tools represents a more accessible form of technology that was already in development for a long time in research labs, universities, and large companies.

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 pattern and can apply it to new values. It does not truly understand miles or distance. It simply learned the ratio and applies it reliably. This is what most machine learning does.

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 transformer architecture introduced in the 2017 Google paper "Attention Is All You Need" solved a fundamental limitation of earlier models. Instead of processing words one at a time, transformers process entire sentences simultaneously and learn which words are most relevant to each other. This is what enabled modern AI to understand context, resolve ambiguity, and reason about meaning — not just match surface patterns.

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. A small accounting or legal firm can now have a system that reads like a capable colleague, writes like a professional, and responds like someone who understands what is actually being asked.

AI is already a part of everyday life. It helps predict traffic, filter emails, recommend content, and detect 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.

The same pattern recognition that powers language models applies to every other data format. Images are grids of numbers. Audio is a sequence of numbers over time. Video combines both. Models trained on these formats learned to understand them the same way language models learned to understand text — through billions of examples and corrections. For small businesses, this means AI can now handle the full range of how clients actually communicate with you.

Earlier forms of AI were mainly used to predict outcomes — recommendations, fraud, traffic. Today, AI can generate new content such as text, images, and responses. This shift makes AI feel much more interactive and immediately useful in everyday business situations.

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.

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

Most people use AI the same way they search Google. Ask a question, get an answer, move on. That works for information. It does not work for running a business.

Many business owners assume the challenge is choosing the right tool. In reality, the bigger issue is how AI is being used. Without a clear approach, even the best tools will not create meaningful results in a business.

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.

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.

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 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.

The AURA Way is a four-step adoption journey designed for local business owners starting with AI for the first time. 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.

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. Incoming email and repeated questions work well as internal tools before you add any customer-facing layer. Missed call follow-up and voice AI are customer-facing from day one and deliver visible results quickly.

Inboxero reads your email, surfaces what actually needs a reply, and drafts responses in your voice — so you spend minutes, not hours, on email every day.

Inboxero is designed for business owners and professionals who spend too much time in their inbox. It does not replace your judgment — it handles the volume so you can focus on the replies that actually matter.

BizMind starts as a structured knowledge base your whole team can query. As you get comfortable, it grows into something more — automating tasks and running workflows through AI agents, so the business keeps moving even when you are not watching.

BizMind is designed for businesses ready to move beyond individual AI use and into a shared, structured system. It starts as a knowledge base — your team asks questions, gets consistent answers. Over time, as confidence grows, it can run as an agent: triggering workflows, automating follow-ups, and completing tasks without manual intervention. It handles multi-user access, privacy compliance, and optional private deployment.

This progression is designed to meet you where you are. You do not need to implement everything at once. Starting small builds confidence, real experience, and the clarity to know what to do next.

You do not need a big investment. You do not need a technical team. You need one workflow that actually saves you time — and the willingness to start there.

Where in your business do you feel the most friction?
That is where you start.

AI does not need to be complex to be useful. Small, simple systems can create meaningful improvements in how a business operates. The goal is not to automate everything — it is to find one place where a consistent, repeatable system creates real value and build from there.

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.