Given the nature of my job, I spend a lot of time building AI agents – everything from virtual loan officers for banks to AI coaches that teach doctors better bedside manner. But when I mention this to friends and family, their eyes glaze over and they’ll say something like, ‘Oh yeah, I use ChatGPT instead of Google now… it’s just easier.’

In the past, I’d tell them they were missing the point, and launch into an explanation of all the ‘cooler’ things AI can do. But lately, I’ve realized their observation – that AI is ‘easier than Google’ – actually touches on some profound points about how AI works and how people perceive it. 

Specifically:

1. Despite what people think, AI models don’t actually “search the internet” – under the hood they use Google and Bing, the same way humans do.
2. What makes asking AI “easier” than searching Google is that – where humans would have to open 10 browser tabs and skim each one – the AI model actually reads all the sites for you, in milliseconds, and provides an answer your question. 

In other words, the AI model isn’t a search engine – it’s a reading machine.

What AI Adds to Search

By now, we’re all familiar with simple chatbots on customer service websites. Traditionally, these chatbots acted like mini Googles, searching a knowledge base for key words from your question. However, those chatbots couldn’t understand what they were searching, hence if you asked “Where is the HDMI port located on this new television set?” it might give you an answer about the company’s store locations.

By contrast, AI models go far beyond key words searches. When you upload a document into an AI agent, the AI model will scan the text, identify patterns in the words and punctuation, then compare those patterns to all the text in its “training data” (i.e. the data the model was fed at the time of its development, usually trillions of words.). Based on these patterns it will make an educated guess as to how it should answer your question.

When Google or ChatGPT offer an AI summary of search engine results, the AI model is basically applying this process to the top web pages from the search results to produce a summary of their contents. .

The above AI agent is drawing on various books on business strategy and marketing used in MBA programs.
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Reading Like an Expert

Reciting key facts from a document or web search is only the beginning of what AI agents can do.  If given the proper instructions and references, an AI agent can not only summarize what it reads, but also help users apply that knowledge to real-world situations.

Think about how doctors learn to read X-rays: they’re not just looking at images – they’re interpreting them through years of medical training, noting subtle variations in bone density, identifying patterns of tissue inflammation, and correlating these observations with patient symptoms and medical histories. A radiologist doesn’t just see a shadow on a lung – they understand what that shadow might mean in the context of the patient’s age, lifestyle, and other health factors.”

We can apply that same principle to building customized AI agents.  Instead of simply dumping a legal contract into ChatGPT – we can develop a “lawyer” AI agent grounded in contract law and instructed to follow a structured discovery process to assess the user’s situation and determine whether the terms of the contract align with their interests.  The AI agent could then draw on its vast pool of other legal contracts to suggest modifications to the agreement that better fit the user’s needs, while explaining its reasoning in terms a layperson could understand.

The above AI agent is a demo that is pulling from various resources posted by the
Mayo Clinic and other institutions.
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Where “AI Search” Falls Short

None of this is to say AI models are better than search engines: rather, the two technologies perform different, complementary functions.  

If you ask an AI model ‘What are the International Maritime Organization’s current requirements for ship emissions in protected waters?’ – unless it’s hooked up to a search engine or has been trained specifically on the IMO database – it won’t proactively go out and acquire the full text of IMO regulations. Instead, the AI will generate a response synthesized from patterns of words in its training data where ‘International Maritime Organization’, ’emissions’ and ‘protected waters’ occur in certain arrangements.

While this process might generate a functionally accurate answer 95% of the time, it won’t necessarily reflect the exact wording of the official documents or, worse, it might generate a plausible-sounding but inaccurate approximation of what the official sources say (what we call an ‘AI hallucination’.) This can be a problem in fields where following regulatory requirements verbatim is critical for compliance and safety.

AI technology is also less efficient than search from a resource perspective. If you ask an AI “large language” model (like ChatGPT) to tell you what type of shock absorbers to install on a 2014 Dodge Ram pickup truck, the LLM will waste a significant amount of processing power analyzing every word in that statement just to determine that you aren’t referring to electrical shocks or a sheep with horns – a trick that traditional searches engines accomplish through simpler forms of calculation.

Building a Well Read AI Agent

Fortunately AI versus search is not an either/or proposition.  With the right tools and expertise it’s possible to create digital agents that use a combination of search and AI language models  to accurately and efficiently answer questions on a given topic with a comparable success rate to a human expert.

This includes:

1. Selecting the right AI model: In addition to the AI models most people are familiar with (ChatGPT, Claude, Deepseek), there are other models trained specifically on subjects like medicine, finance, or law.  It’s also possible (but expensive) to train a “small language model” on narrowly defined tasks like reviewing insurance claims.
2. Capturing expert thought processes: Working together, subject matter experts and AI “prompt engineers” can capture a human expert’s thought process and analytical frameworks in a form that AI agents can apply during conversations.  For instance, when an agronomist evaluates a region’s food security outlook, they’re not just checking current crop yields – they’re drawing on years of experience to consider factors like historical weather patterns, changing demographics, infrastructure development, and emerging farming technologies. An AI agent can be taught to apply similar holistic analytical frameworks.
3. Providing reference materials: If your work deals with concepts that aren’t common knowledge (e.g., how specific national food safety regulations applied toothpaste manufacturing), you will probably need to provide the AI agent with access to specific documents (via a “Retrieval Augmented Generation” service)  or a connection to a database, rather than relying on its training data plus whatever it finds via internet search.
4. Adding search capabilities: There are a number of ways to give an AI agent access to search, whether that’s using an AI model with built-in search capability like Perplexity or connecting to Google or Bing via their back-end APIs. 
5. Training users: AI agents can be powerful tools that let people conduct research and solve real-world problems faster than ever before.  However, users need to be trained on the capabilities and limitations of AI models, so they can put the AI’s answers in context and know when they might require verification (versus blindly trusting / deferring to the AI agent – a tendency known as “algorithmic paternalism”)

While the above takes planning and effort, the end result is an AI agent that can act as a reasonable stand in for a human expert for 80% of the questions your audience might have.

The above AI agent is grounded in a set of documents from a
leading agricultural research institution.
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Conclusion

People tend to use new technology the same way they used older technologies, until they learn to appreciate the new tech’s unique capabilities. This was the case in the early days of email and social media, and now people are using AI the same way they’ve used Google for the past two decades.

But the real power of AI isn’t in replacing search engines – it’s in creating intelligent partners that can help organizations make better use of their collective knowledge. When properly designed and implemented, AI agents can do more than just find information – they can help people understand it, apply it, and transform it into better decisions and outcomes.