What do people mean by AI today?
When most people say AI today, they usually mean large language models like ChatGPT, Claude, or Gemini. AI as a field has existed for decades, but it has only become useful enough for real business work in the last few years.
Is AI a fad?
Probably not. Cell phones and the internet also looked optional before they became infrastructure. AI is likely following the same pattern: slow adoption at first, then obvious once it is inside every system and job.
What is an LLM?
A large language model is software trained on huge amounts of text so it can understand and generate language, code, and reasoning steps. ChatGPT made LLMs accessible to regular business users.
Why did we go from LLMs to Generative AI to Agents so quickly?
LLMs were the core technology, generative AI became the consumer-friendly name, and agents are the next step: AI that can use tools and take action. The field moved quickly because the major labs raised large amounts of capital, built data centers, and trained much stronger models.
AI vs. machine learning
Machine learning has been used for years in fraud detection, credit scoring, forecasting, and computer vision. Modern LLMs are different because they can work with messy language, long documents, code, and reasoning-heavy tasks.
Is RPA AI?
No. RPA follows brittle rules and clicks buttons. AI can understand language, documents, context, and exceptions.
Is OCR AI?
Not by itself. OCR reads text from images or PDFs. Modern AI can understand what the document means, compare it to business rules, and decide what should happen next.
What are LLMs good at?
LLMs are strong at reading, writing, summarizing, extracting data, classifying information, generating code, and helping humans move faster when the work involves text, knowledge, documents, judgment, or repetitive decisions.
What are LLMs bad at?
LLMs can be wrong, overconfident, and weak on facts unless they are connected to the right data. They do not magically understand your company unless you give them systems, rules, workflows, and context.
AI vs. agent
An AI model answers or predicts. An agent combines AI with tools so it can take steps: read an invoice, check an ERP, draft a response, route an exception, or ask a human for approval.
Not everything is an agent
A chatbot, workflow, script, dashboard, OCR tool, or RPA bot is not automatically an agent just because someone put AI on the slide.
What is an ontology?
An ontology is a map of your business. It defines important objects like customers, orders, invoices, suppliers, products, claims, accounts, and payments, then shows how they connect.
Ontology matters because AI needs to understand your business objects, not just your words. Without it, AI guesses against messy systems instead of working from a real map.
What is a VLM?
A vision-language model can understand images, screenshots, PDFs, forms, charts, and text together. That matters because real business work often lives in messy documents, not clean databases.
What is Voice AI?
Voice AI combines speech-to-text, an AI model, and text-to-speech. It can listen to a customer, understand the issue, look up information, and respond in a natural voice.
What is Computer Use AI?
Computer Use AI can look at a screen and click around like a person. It is useful when old software has no clean API, but it is usually slower and more fragile than direct system integration.
What are Cursor and Claude Code?
Cursor and Claude Code are AI coding tools that help engineers write, edit, test, and understand code faster. Code generation is one of the strongest AI use cases today.
Why do token costs matter?
AI is not free every time it thinks. Models are usually priced by tokens, which are chunks of text the model reads and writes. Costs rise when an agent reads long documents, runs all day, makes many tool calls, or serves thousands of customers.
How do I minimize LLM token costs?
A live AI agent is flexible, but it can be expensive and less predictable. Often the smartest use of AI is to build or improve deterministic software that then runs cheaply and reliably without an LLM call for every step.
Why do LLMs usually run in the cloud?
The best LLMs need massive GPU infrastructure, constant updates, and serious engineering to run well. Small models can run on-prem, but most companies should use secure cloud deployments with the right controls.
Why cannot we just run our own LLM on-prem?
You can, but it usually makes little sense. You would need GPUs, servers, model software, security, upgrades, monitoring, and people who know how to keep the system alive.
What does NVIDIA have to do with AI?
NVIDIA makes the GPUs, networking, and software stack that train and run many leading AI models. It is not the AI app, but it is one of the most important infrastructure companies underneath the boom.
Should I be afraid of Chinese models?
Not automatically. The bigger question is who hosts the model, where your data goes, and what controls exist. A trusted provider with the right security terms is usually the real requirement.
Which model is best?
This is usually the wrong question because model quality changes every few months. The right answer is to test models on your actual work, with your data, systems, latency needs, and risk constraints.
Should we build our own LLM?
Almost certainly not. Most companies that say they built an LLM actually built an app, wrapper, fine-tune, or workflow on top of someone else's model. That can be valuable, but it is not the same as training a frontier model.
What are the security risks?
The main risks are data leakage, bad permissions, wrong answers, prompt injection, and employees pasting sensitive data into random tools. These risks are manageable with enterprise contracts, access controls, logging, private deployments, and good system design.
Will I get hacked if I use AI?
Not if you use it correctly. AI is like cloud software or email: risky when unmanaged, safe enough when deployed with the right controls.
Do LLMs replace people?
They can replace full processes, not just assist workers. The clearest opportunities are manual back-office processes built around documents, data entry, approvals, and exceptions.
Are we doing AI if we use Microsoft Copilot?
Copilot is a productivity tool. It is not by itself an AI strategy for transforming business processes, systems, data, and cost structure.
What is a forward deployed engineer?
A forward deployed engineer works close to the business process, learns how the work really happens, fixes messy data problems, connects systems, and builds working software in the field.
My data is messy. Can I still use AI?
Yes, but messy data is usually the reason you need expert help. Bad data is not a reason to avoid AI. It is the reason to connect AI to real operations carefully.
Why do I need to solve my data problems?
AI is the brain, but data is the body. A smart brain is useless if it cannot see, move, or touch the systems where the work actually happens.
Will my ERP, CRM, or EMR vendor give me AI?
Maybe they will add AI buttons, OCR, copilots, or dashboards. That does not mean they will transform your business. Be skeptical when legacy vendors repackage old workflows as AI transformation.
What are the best AI use cases?
The best use cases are usually practical, painful, and measurable.
- Generate code to replace internal SaaS you pay for every year.
- Automate business processes involving documents, data entry, approvals, and exceptions.
- Use Voice AI for customer service, collections, scheduling, support, and call center work.
- Use Computer Use AI when old software forces people to click through screens manually.
- Use AI agents when the job requires reading, deciding, taking action, and escalating exceptions.