How is AI Different from Traditional Internet Searches?
If it is not already obvious, the output or response received from a generative AI tool differs drastically from what is rapidly becoming its predecessor, Internet search engines. The most significant difference lies in AI’s ability to more broadly understand language and use it to articulate a response.
The World Wide Web and our ability to utilize a search engine (i.e., Edge, Google, Yahoo, etc.) allowed us all to use keywords or terms to search for web pages or web-published documents (PDFs) that provided available information specific to those terms. Of course, we could use quotes and connectors like “and” and “or,” along with other terms, to yield more specific search results, but the search engine simply provided web page results that ranked highly for the specific words and terms we used.
The search terms used and the results they provided were primarily a function of the algorithms or “instructions” the search engine developed. In part, continuous search result outcomes led to different levels of traffic a web page might receive, measuring the time spent, and ultimately leading to a “popularity or usefulness contest.” The more useful a web page is, the higher its ranking for those terms, and that ranking, too, has value for many entities and organizations.
Today, we refer to the measurement of a website’s ranking as search engine optimization (SEO). The better your SEO, the more likely your website is to show at the top of certain keyword searches. I’ll share more about how AI is changing this model and why it’s important for EDO leaders to understand it later in this article.
For now, the critical takeaway is that AI technology, unlike that of search engines, has the ability to understand and respond to queries with human language.
Natural Language Processing: The Key Capability of AI
Unlike traditional keyword web searches, advanced (fast) processors, along with the development of LLMs, have made it possible for computing to accomplish natural language processing (NLP). This technology enables computers to understand and generate human language, unlike traditional search engine technology. This allows for the association of words and, thus, the ability to provide a unique narrative to the user in the form of text (language), audio, images, and even video, instead of just a web link or reference document.
NLP computing, for example, has given LLMs the ability to understand that the word “car” also means “automobile” or “EV.” This is accomplished by creating numerical values for letters and words and creating associations between each—something only storage and fast processors could do. In its simplest form, if the word “car” is associated with the numeric value 12769098 within an LLM, that model also knows that the word is similar or identical to 4876209856 or “automobile” and that both also have an association with 810998564223, or the word “driving.” Note that these numeric values were contrived for explanation purposes.
It is the increased processing speed, made possible with NVIDIA, Huawei, AMD, Amazon, and other high-speed chips, that allows computers to quickly scan and process numeric values housed in the LLM and to return a response in the form of language.
Where traditional searches with engines like Google would only yield keyword results available on web pages, LLMs like that built by OpenAI (provider of ChatGPT) can now yield a unique response in the form of language and images from the documents, reports, data, webpage content, and other information now stored within their LLM. This new form of output eliminates the need for users to review multiple web pages and documents offered in traditional search results to find the content they were looking for.
Now that you understand the hardware and technology that make AI possible, let’s explain the tools that allow us to interact with the system.
The Next Level of AI Capability: Machine Learning to Agentic AI
Most of us have become familiar with AI through our introduction to generative AI tools; however, the real productivity gains will come from the development and use of agentic AI and machine learning. It’s important to understand the differences between them when developing an organizational AI culture and strategy.
Generative AI refers to LLM AI tools, like OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and numerous others. Users can ask these “AI assistants” to provide information, analyze data sets, or generate new content. These tools then reference one or more (if it’s an aggregator) LLM and respond to a user’s prompt or question via text, images, audio/ music, video, code, and even 3D models.
Generative AI accesses an LLM and uses NLP to respond to a user’s prompts. However, it is unable to perform tasks without a human request and guidance.
Machine learning is the broad field of building systems that learn patterns from data to make predictions or decisions without being explicitly programmed. The vast amounts of data stored in LLMs and the processors capable of sifting through all that data to identify events, movement, patterns, or lack thereof, correlations between datasets, and other information, are what allow AI systems to learn.
Generative AI uses machine learning to identify patterns and generate content. Machine learning enables these systems to produce output and communicate with minimal or no human interaction. This is the foundation of what will become a more productive AI tool: agentic AI.
Agentic AI refers to more advanced artificial intelligence systems that can autonomously take actions to achieve goals, rather than simply responding to direct user prompts (e.g., a chatbot) or following pre-set instructions.
The term “agentic” originates from the concept of agency, which refers to the capacity to act independently. So, agentic AI systems are designed and have the ability to plan tasks based on goals, make decisions about what to do next, take action via software tools, APIs, robots, or digital systems, and most importantly, learn from results and adjust their behavior over time.
Unlike generative AI, which relies on user input, agentic AI can operate autonomously to accomplish a task or achieve a defined outcome. For example, it can schedule meetings, draft reports, and write and send emails without human instruction. So, while a normal chatbot waits for you to tell it what to do, an AI agent might take your high-level goal (like “grow our customer base”) and research target markets, draft outreach emails, schedule follow-ups, and track and report on results, all without intervention or oversight.
It is agentic AI, and its ability to increase employee and organizational productivity, that offers the greatest benefit for economic development organizations (EDOs) that can understand and invest in not only building these digital assistants but also the data model necessary to support them. As a result, for EDOs to truly optimize AI for improved performance, it is going to require an investment in training, data/information, software, and AI agent programming.
Examples of How EDOs Might Use Agentic AI
- Gathering and monitoring organizational performance metrics (projects, strategy accomplishments, marketing metrics, financial performance, and new investment announcements
- Handling membership fee invoicing
- Scheduling and booking travel
- Entering, processing, and paying vendor bills
- Evaluating resumes
- Building business attraction prospect lists
- Preparing unique proposals
- Completing digital marketing campaigns
In effect, agentic AI represents a shift from AI as a tool to AI as a collaborator or co-worker. It’s the foundation of the next generation of productivity and automation, and enables AI to act and not just think.
Part 3 in this series of articles will be published during the week of January 19-23, 2026. It will share seven barriers to AI adoption that EDOs will need to overcome.
If you would like a free preliminary AI organizational assessment or want to discuss how your organization might get started, please contact Rob Camoin, CEcD, at rcamoin@camoinassociates.com.
View The AI-Ready EDO series home page
About the Author
Robert Camoin, CEcD, is founder and current President of Camoin Associates, an economic development consulting firm that works with EDOs across the nation, and is currently leading the firm’s effort to build its first AI agent. He is also President and CEO of ProspectEngage®, which provides digital business retention and prospecting platforms supported by a closed-source data platform that now incorporates AI for user insight and efficiency.
