Artificial intelligence is suddenly everywhere. Companies are promoting “AI-powered” software, people are using tools like ChatGPT and Claude at work, and we hear about AI assistants, AI agents and AI automation almost daily.
But what is AI actually doing?
That question matters because AI can produce answers that are fast, polished and surprisingly convincing. It can summarize information, explain complex topics, compare alternatives and even help guide multi-step research.
But a good-sounding answer is not necessarily the same thing as a good analysis.
Understanding that distinction starts with understanding a few basic pieces of AI.
Start With the AI Model
At the center of most of today’s AI applications is a large language model, or LLM.
Think of the model as the engine. It has been trained on enormous amounts of information and has learned patterns in language that allow it to answer questions, summarize material, explain concepts and generate new content.
That is why you can ask an AI model a question in plain English and often receive a remarkably useful response.
For example, you could give it a demographic report and ask:
“What are the three most important things I should know about this market?”
The AI can review the information you provided, identify what appears important and turn it into an easy-to-understand explanation.
But now consider a different question:
“Where should this restaurant open its next location?”
That is a much different problem.
A reliable answer might require demographics, competitors, consumer spending, existing store locations, traffic patterns, trade areas, population growth, historical store performance and other information.
So where does the AI get all of that?
Depending on the system, its answer might draw from information learned during training, information you provide directly, information it retrieves from the internet or data and tools it has been specifically connected to.
Those are very different sources of information, and they are not necessarily equal.
Where Did the Answer Come From?
This is one of the most important questions to ask when using AI.
If an AI system tells you that a market is growing quickly, where did that information come from?
Was it based on a current demographic dataset? A government source? A recent article? An old web page? Something included in its training data? Or was it simply inferring an answer from the information available to it?
In many cases, the user may not know.
That does not make the AI useless. Far from it. But it does mean that the quality of the answer depends heavily on the quality of the information behind it.
The same principle has always applied to research and analytics: poor inputs tend to produce poor outputs.
AI does not make that problem disappear. In some ways, it can make the problem harder to notice because the final answer may still sound highly confident and professional.
AI Can Interpret Information. That Is Not the Same as Creating the Analysis.
This distinction is especially important in location analytics.
Imagine that you want to evaluate a potential retail location using a 12-minute drive-time trade area or draw your own trade area based on your boots-on-the-ground knowledge of the area.
A specialized location analytics platform can create that trade area using a road network, calculate the population within it, measure population growth, identify nearby competitors and retrieve other market characteristics.
Once that information exists, AI can be extremely useful.
It can help explain what the numbers mean. It can compare one site with another. It can summarize strengths and weaknesses. It can highlight unusual patterns and help a user ask better follow-up questions.
The important point is not that AI can never perform geographic or analytical tasks. Increasingly, AI systems can use specialized tools to do exactly that.
The question is what is actually behind the answer.
What data was used? What tool performed the calculation? What methodology was applied? And can the result be traced back to something reliable?
What Is an AI Application?
An AI application combines an AI model with software, information and other tools.
ChatGPT and Claude are familiar examples. You interact with the application by asking questions or giving instructions, while an AI model operates behind the scenes.
Depending on how the application is configured, it may also be able to search the web, read uploaded files, access databases or use specialized software.
That is what makes modern AI applications much more capable than a language model operating by itself.
For example, imagine reviewing a SiteSeer demographic report and asking:
“Explain this trade area to me like I’m presenting it to a client.”
SiteSeer provides the underlying market information. AI can help organize it, interpret it and communicate it more clearly.
That can make sophisticated research much easier to use.
So What Is an AI Agent?
An AI agent goes a step further.
Instead of simply answering one question, an agent can be given a goal and access to tools that allow it to complete a series of tasks.
For example, you might ask an agent:
“Compare these five potential restaurant locations and tell me which ones deserve a closer look.”
If the agent had access to the right tools and data, it could potentially retrieve demographics, evaluate competition, review location scores and compare the five sites.
It could then summarize the results in plain language.
The important part is what happened underneath.
The AI did not simply “know” the answer. It used information and tools to build one.
That is an important way to think about AI agents. Their value often comes not from replacing specialized systems, but from making it easier to interact with them.
AI Is Very Good at Sounding Right
One of the biggest strengths of modern AI is also one of its biggest risks.
It is extremely good at producing clear, polished and confident answers.
Suppose you ask:
“Why are my best-performing franchise locations doing better than the rest?”
AI might tell you that those locations have stronger demographics, better visibility, less competition, higher traffic or stronger customer demand.
Those are all reasonable possibilities.
But unless the AI has access to your actual store performance, market data, competitors, trade areas and other relevant information, it does not know which explanation is correct.
It may be generating a plausible explanation rather than a demonstrated one.
That difference matters.
The more important the decision, the more important it becomes to understand whether the answer is based on real analysis or simply sounds like one.
Consistency Matters Too
There is another important difference between AI and a traditional analytical model: consistency.
Ask a forecasting model to evaluate the same site twice using the same data and methodology, and you should expect the same result.
Ask a general-purpose AI system the same question multiple times, and you may get somewhat different answers. The emphasis may change. The reasoning may change. In some cases, even the recommendation may change.
That is because large language models are designed to generate likely responses, not to apply a fixed analytical formula unless they are specifically instructed and constrained to do so.
For brainstorming or summarizing information, that flexibility can be useful. For repeatable business analysis, however, consistency of data, methodology and approach becomes much more important.
AI can certainly be incorporated into a structured process, but the structure still has to come from somewhere.
The Better Question to Ask
As AI becomes part of more business software and more everyday decision-making, asking whether a product “has AI” is becoming less meaningful.
Almost everything will have AI.
A better question is:
What is the AI actually doing, and what data, tools and analysis are behind its answer?
If the AI is working with reliable data, proven analytical tools and clearly defined methodologies, it can make research faster, easier to understand and much more accessible.
If the underlying information is incomplete, outdated or inappropriate for the question, AI can just as easily help produce a very polished version of the wrong answer.
That is why AI should not be thought of as a magic source of knowledge.
It is better understood as a powerful new way to work with information, software and analysis.
And, as with any analytical process, the quality of what comes out still depends heavily on what goes in.
Coming Next: There Is No Magic AI Button
In Part 2, we will look more closely at what happens when someone asks AI to perform location analysis such as White Space modeling, Void Analysis, Site Scorecards and other site selection tasks.
We will explore what those analyses actually require, where the data comes from and why AI can make the process easier without eliminating the need for quality data, analytical tools and sound methodology.
