The Secret to Getting Recommended by AI

According to a McKinsey’s 2025 state of AI survey, businesses are regularly using generative artificial intelligence in their marketing and sales efforts more than in any other business function. It’s not surprising, given the benefits, that AI is empowering marketers to do more, and do it faster than ever before. However, a lack of resources and knowledge of how AI works is preventing marketers from fully leveraging AI to their most competitive advantage. In order to master AI, you must first master data science.

How AI Works

AI is a form of machine learning, where computers or programs or software look for patterns in data using liner algebra, a mathematical language matrix. Those patterns are then added to a model to make sense of them as predictable patterns, which undergo a cross validation process that divides the data and tests the model by slicing the data in different ways. 

For example, if there are 100 points of data, machine learning will take 80% of the data and use it to find patterns and build a model of what’s going on in the data. Since the machine already knows the answers, it uses the remaining 20% for testing to see how good the model actually is. This cross validation process is done multiple times so that, regardless how the data is sliced, the model converges to a version that best generalizes on the overall data. 

Applying this approach to language requires exponentially more data points and energy. Traditionally, language learning machines (LLMs) require tens of thousands of data points, general AI needs hundreds of thousands, and generative AI models like ChatGPT are usingis using millions or billions points of data to build its model. LLMs break down words into letters and letters into numbers to see how those letters relate to each other and form a word, then how words form sentences, how sentences form paragraphs and how paragraphs form essays or another collection of thoughts. By converting language into numbers to understand what the language means, LLMs then interrogate the model to respond to the query, and convert the numbers back to language to generate a response for humans to read. 

Master Data Science, Master AI

The generative AI outputs are only as good as the quality of original data they are modeled on, whether it’s text or images. Same as with language, AI will convert the image into numbers and identify patterns to codify it. In effect, when a marketer uses gen AI to create content for their website, the content is merely a summary of existing content from the data that the AI model used. And where is the competitive advantage in that? 

Increasingly more marketers and SMBs are realizing that posting this copy of a copy onto a company’s website will not only not yield the search results they once saw with traditional SEO but there’s also no gain from using gen AI content. That’s because gen AI is built with markers as a survival mechanism in order to remain useful and improve; AI tools can detect AI-generated outputs in order to avoid polluting its own model. As a result, gen AI will not recommend a diluted output. 

Because LLMs consistently yearn to learn and improve their model through authentic, authoritative content, every marketer has the opportunity to influence AI recommendations with reinforcement learning. Reinforcement learning uses data science in the form of linear algebra to make decisions about the data, essentially creating neural networks mimicking the trial-and-error process humans go through. And, every marketer should be looking at how to use reinforcement learning to get their brand recommended on AI, especially considering it took less than two years for ChatGPT to achieve more than one billion daily search queries—a feat that took Google more than a decade. 

Don’t Snooze, or You Will Lose Out

Reinforcement learning is not only the highest iteration of machine learning currently, it’s the answer marketers are looking for in order to fully leverage AI to benefit their brand. Artios custom built its proprietary data science-driven AI modeling technology and uses reinforcement learning to interrogate its model and help brands create new, authentic content that LLMs not only crave, but also selectively recommend in its AI search results.

It’s free to use generative AI tools like ChatGPT and Perplexity, but while big brands stick to their antiquated best practices and other marketers wait to see if their diluted content gets recommended, early adopters of reinforcement learning are getting results and will continue to be recommended as models seek out their quality data to maintain or improve outputs. 

Today, many companies believe getting recommended by AI is just an extension of SEO and will eventually happen for them, but it’s not and they won’t. Twenty-five years ago, when many brands were slow to implement SEO in part because they either didn’t know what SEO was or who on their team should do it, ambitious disruptors such as SMBs and startups acted quickly and multiplied their business growth while their competitors slept through the opportunity. 

Since AI is far more intelligent than traditional search engines and a much faster learner, now is the best time to invest in reinforcement learning while it is considerably affordable. Of course, marketers and SMBs can wait and see how AI evolves and watch competitors seize valuable search traffic, but in the future, it will definitely cost more to get recommended than it does right now. And, good news, what works in AI works really well in SEO. 

So, what are you waiting for?

If you’re reading this, you probably realize you brought a bicycle to a motor race. Don’t worry, Artios can help your brand get recommended by AI a lot sooner—guaranteed. Don’t delay, contact Artios today.