AI Behind the Scenes: How AI Reads Data and Answers Questions

AI is involved in nearly every part of running a business today, from drafting emails and summarizing reports to crunching data and brainstorming ideas. According to Microsoft’s 2025 “AI Diffusion Report,” one in six people worldwide use generative AI tools. These statistics reflect a wide adoption spectrum, which probably means your employees, vendors and customers are using it too.

At the same time, cybercriminals are exploiting these tools. According to the cybersecurity company CrowdStrike’s “2026 Global Threat Report,” AI‑enabled cyberattacks surged 89% in 2025. AI is giving attackers faster ways to launch phishing campaigns and break into networks.

McKinsey surveyed 1,993 participants across 105 nations representing a wide range of industries and company sizes for its “The state of AI in 2025: Agents, innovation, and transformation” report. The report showed:

• Nearly all respondents are using AI.

• 64% have begun experimenting with AI agents (computer programs that can learn, make autonomous decisions and execute tasks on their own).

• Most are still in the early stages of learning how to scale AI across their companies.

• 32% anticipated AI adoption to decrease employment, 13% expected it to increase employment and 43% expected no change.

AI’s capabilities are advancing rapidly, and so are the businesses that infuse AI-powered tools into their services, products and day-to-day operations. So, understanding what happens when you type a question into an AI agent isn’t just a tech curiosity. It’s a business risk conversation.

How AI versus humans process information

When you ask AI a question, it breaks your words into chunks and generates a request based on patterns and information blocks.

For example, let’s say you give an AI tool this prompt: “Review this vendor contract and tell me if there are any red flags.”

What a human would do with the request

A human would read the sentence as a whole, while also considering the relationship you have with the vendor. A person’s experience with that vendor would influence how they read the contract and may lead to greater or less tolerance for its text, depending on the experience. A human would also infer many things, like red flags specific to their industry, overall risks, interoffice dynamics and whether the vendor would be a good fit. They would do this without being asked.

What an AI tool would do with the request

Unlike a human, AI doesn’t read the sentence or make multiple inferences. Instead, it breaks the sentence into tiny pieces called “tokens” so it can analyze patterns. (Tokens are chunks of words, punctuation and structure.) It doesn’t understand your business, goals or risk appetite unless you spell those out in your prompt. That’s why vague prompts often lead to vague answers. The quality of your question shapes the quality of the response.

AI first looks for patterns, not facts or comprehension

Large language models (LLMs) like ChatGPT are trained on huge volumes of text. Big datasets are what drive LLMs, making them more reliable since there are fewer chances for data anomalies. Over time, they learn patterns about how language is typically used. When you ask a question, the model predicts the most likely useful answer based on those patterns. On the other hand, smaller datasets are less useful to AI because there isn’t enough data to detect anomalies and patterns.

That means AI is:

• Guessing in a very sophisticated way

• Building its answer one word at a time, choosing each new word based on what usually comes next in similar situations

• Rapidly processing information and results, which could give the appearance of extensive knowledge

A document generation example

Let’s say you’re a contractor using AI to draft a safety policy. The AI might generate something that sounds good, including the importance of wearing personal protective equipment, avoiding chemical hazards, reading safety data sheets and submitting incident reports. 

However, it won’t automatically know the Occupational Safety and Health Administration (OSHA) rules your business is required to follow, or when you must notify OSHA about an incident. Nor will it know about your specific risk exposures or your insurer’s loss‑control recommendations.

To get closer to a polished draft, you’d need to provide the AI with background information about your business and other training materials. This would enable it to learn and generate more useful outputs.

AI has a short attention span

AI tools have a limit to how much they can hold in their working memory. This is known as a “context window.” Think of it as the size of a desk: If you put a few pages on the desk, you can see everything clearly. But if you pile on dozens of reports, documents start to fade into the background, and others slide off the table.

In the same way, if you paste several long contracts or policy documents into an AI chat and ask multiple questions, the tool may begin to forget what you shared earlier. This can lead to answers that contradict previous information or skip important details. Don’t assume AI has considered everything you gave it from beginning to end. You have to review its outputs, challenge it and remind it.

Ways AI can help your business

AI can save time in some areas, such as:

• Creating first drafts

• Summarizing long documents

• Brainstorming ideas

• Creating checklists and outlines

• Analyzing large volumes of data

In these cases, you still review, edit and approve the final version, but AI does the heavy lifting.

If you’re using an AI agent:

• Verify the data it’s pulling from and maintain the files.

• State that it should never plagiarize or steal from sources.

• Ask it to provide the sources it references.

You still need a human expert

AI shouldn’t be the final decision-maker in any process. Always verify AI outputs with a subject-matter expert. AI can be a valuable assistant with the right training data and prompts, but it shouldn’t be more than that.

Let the specialists, like your attorney, accountant, insurance agent and employees, provide oversight, judgment and context. This creates accountability and minimizes your liability exposure.

Reduce your liability exposure with robust AI planning and oversight

Create an AI use and ethics policy before you launch it across your business. Train your employees on AI and how to use it properly. Otherwise, uninformed employees may enter confidential client data into a public AI tool. Or they may trust AI‑generated answers about compliance, which could create significant liability for your business. AI-related liability includes:

• Cyber liability and data breach exposures: You could unintentionally violate intellectual property or data privacy laws if sensitive information is mishandled.

• Errors and omissions risks: You could be sued for giving bad advice if you rely on AI‑generated recommendations and pass them to clients.

• Unsecured AI tools: Hackers can breach your public-facing chatbots and force them to purge sensitive information they’ve collected while chatting with clients.

The bottom line on AI

AI can be a powerful assistant for your business, but it doesn’t think like a human or replace professional advice. Update your insurance policies, including professional liability and cyber liability, to reflect this new risk exposure.

Use AI to speed up routine tasks. Use your business expertise and judgment for the decisions that matter.

This content is for informational purposes only and not for the purpose of providing professional, financial, medical or legal advice. You should contact your licensed professional to obtain advice with respect to any particular issue or problem.

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