AI 101: What is AI? A Practical Guide for Founders and Business Leaders
Artificial intelligence.
Machine learning.
Large language models.
Generative AI.
Agentic AI.
If you have spent any time reading about technology in the last two years, chances are you have encountered these terms.
Often in the same sentence, interchangeably, and in ways that often create more confusion than clarity.
Most of the confusion is unnecessary. The main idea behind the terminology is simpler than the vocabulary suggests, and understanding it will change how you evaluate AI tools, think about AI risk, and decide when and where to use it in your business.
Let’s begin.
What exactly is this AI thing?
Artificial Intelligence, also known as AI, is any technology that performs tasks that would typically require human intelligence to complete. Some examples include:
Reading a document and summarizing it.
Recognizing a face in a photo.
Translating text from one language to another. Predicting which customers are most likely to churn.
Answering a question in plain language.
Writing a first draft of an email.
Identifying fraudulent transactions in a stream of financial data.
These are all tasks that, until recently, required human decision-making.
AI can now perform these tasks at a dramatically faster rate and greater scale than humanly possible.
The important word in that definition is tasks.
AI is not sentient, meaning that it doesn’t have a mind of its own. It doesn’t understand, reason, or have goals the way humans do. It simply performs specific tasks based on patterns it has learned from data it was trained on.
Understanding this is important, and the key reason why human oversight is important. You should not blindly trust output from AI systems nor dismiss it as well.
How does AI even work?
Traditional software follows rules.
A programmer writes: if this condition is true, do this; if that condition is true, do that.
The software executes those rules predictably, every time. Hence, if you want it to do something new, you simply write new rules.
AI works differently. Instead of following strict rules written by a programmer, AI learns patterns from examples.
If you show an AI model thousands of emails labelled “spam” and “not spam”, it will learn what distinguishes them, without anyone writing explicit rules.
If you feed an AI model with millions of sentences in English and French, and provide some basic rules of translation between both languages, it will learn the patterns of how to translate future sentences.
If you feed an AI model with several years of sales data, with basic rules on how to identify what constitutes success, it will learn which patterns predict deals closing.
What makes AI powerful is precisely this:
AI handles complexity and nuance that would be impossible to capture in explicit rules, improves as it sees more examples, and applies patterns learned in one context to new situations that weren’t in the training data.
It’s very important to note that AI’s capability is directly proportional to the quality and quantity of the data it was trained on.
Clean, relevant data produces capable, reliable AI.
Messy or insufficient data produces AI that fails quietly and misleads confidently.
The three types of AI you’ll encounter
The taxonomy of AI is genuinely confusing because the terminology overlaps and changes quickly. Here’s a simplified version focused on what matters for a growing company.
1. Predictive AI: learning from historical data to forecast future outcomes.
Examples: lead scoring in your CRM system, revenue forecasting tools, customer churn prediction models.
What it does: it identifies patterns in past data and uses them to predict what is likely to happen next.
Why it matters: predictive AI provides quick wins for growing businesses, as it has the most direct connection to business decisions.
2. Generative AI: learning from vast amounts of existing content to produce new content.
Examples: ChatGPT, Claude, Copilot, AI image generators, code assistants, etc.
What it does: it generates new outputs based on patterns learned from training data, instead of retrieving existing information.
Key things to watch out for: useful for content, communication, and summarization, but requires human oversight and review, as it can produce plausible-sounding content that is factually wrong.
3. Analytical AI: processing of large volumes of data to quickly surface insights, anomalies, and patterns that would be impractical to find manually.
Examples: anomaly detection in financial data, pattern recognition in customer behaviour, automated insight generation in BI reporting.
Where you will find it: increasingly embedded in tools your business already uses.
Most AI tools that a growing company generating $1M to $10M in annual revenue will encounter fall into one of these categories.
Knowing which category a tool belongs to informs you what it can and can’t do, and the data it requires.
What AI is not
The hype around AI has created unrealistic expectations, and founders who hold on to these expectations are prone to make expensive mistakes.
AI is not infallible.
AI tools make mistakes. Sometimes subtle ones that are hard to detect, and other times, confident-sounding ones that are factually wrong.
A language model can generate a plausible-sounding statistic that is completely fabricated. This is known as hallucination.
A predictive model can make systematically wrong forecasts because the training data had underlying bias that nobody noticed.
Every AI output that informs a business decision needs a human who understands the context to review it before acting on it.
AI is not a replacement for human judgment.
AI can surface patterns, generate options, and accelerate analysis.
However, it does not automatically understand your business context, your values, your customer relationships, or other nuanced human factors that come into play in complex decisions. It simply augments the ground rules provided.
The companies that get the most value from AI are the ones that deploy it to handle volume and speed while maintaining human oversight by keeping humans responsible for consequential decisions.
AI is not a substitute for clean data.
AI tools are not data cleaning tools. They simply process whatever data they are given and generate outputs based on it.
If the data is inaccurate, the outputs will be inaccurate. This is where the phrase ‘garbage in, garbage out’ stems from.
If the data has systematic biases, the AI will learn and amplify those biases.
As a result, consequences in an AI context are larger and harder to detect than in traditional software.
AI is not a strategy.
“We need to be using AI” is an aspiration, not a strategy.
The strategy is the specific use case, the data foundation, the implementation plan, the success metrics, and the governance approach that makes the deployment responsible and auditable.
Without those components, buying an AI subscription and hoping it helps is about as strategic as it sounds.
Why AI needs your data to work
Every AI tool that will be relevant to your business runs on data. Specifically, data that is configured for the right business context.
The AI that analyzes your sales pipeline should use your sales data.
The AI that personalizes your marketing should use your customer data.
The AI that forecasts your revenue should use your historical financial data.
The AI that detects operational anomalies should use your operational data.
The quality, completeness, maintenance, and accessibility of your data directly determine whether AI that uses the data produces reliable outputs or confident-sounding disappointments.
This is why the first 6 articles of the Data & AI 101 series focused on data foundations and data strategy, and not AI strategy.
AI is the destination, but data strategy is what makes it reachable.
Companies that intentionally build that strategy, before the AI investment pressure arrives, are the ones that get compounding returns on both.
Founder Tip: Before evaluating any AI tool, ask the vendor the following question: “How does your tool perform when the input data is incomplete or inconsistent?”
The answer will reveal more than any fancy demo will. Choose wisely.
Your next step
Download the Data Strategy Checklist, a one-page tool that assesses your data foundation across the five dimensions AI requires to work reliably. Takes 20 minutes and shows you what needs to be in place before AI investment makes sense.
Continue reading:
[What is data? →] | [What is data quality? →] | [Everyone Wants AI: Here’s What to Build First →]
Series:
Data & AI 101 | Previous: F6: What is data analytics? | Next: F8: What is machine learning?
