Can companies direct AI responses?
gettyMany companies are hesitant to embrace AI because they fear that AI engines will expose their proprietary data to other companies, including competitors. At the same time, some companies want to deliberately feed their data into artificial intelligence engines as part of branding. Is this a billion dollar opportunity or (another) fatal flaw in the evolution of artificial intelligence?
This opposing idea came up during a discussion hosted by Next Accessa consulting firm that advises clients on how best to use artificial intelligence to improve their product-to-market and monetization strategies.
Let me start from the beginning. Simply put, an AI engine has two components. The first is an extensive database of content, called a large language model (LLM), that contains all the information the AI company can find. This includes all of Wikipedia, the New York Times, and other publicly available content. (There is serious and growing controversy over copyright infringement, but that’s a topic for another time.)
The second component of an AI engine is an algorithm that uses the LLM data to synthesize answers to queries. If I ask an AI engine to complete the sentence, “The dog ran up to…”, the algorithm checks the LLM to see how often that part already exists and which words usually complete the sentence. It then gives the user the statistically most likely next word. In this case, “hill” is a standard answer, while “casserole” is not.
A company trying to harness artificial intelligence can start by asking questions. For example, a clothing company might ask, “What’s the latest trend in men’s footwear?” However, just by asking this question, the AI engine knows that the clothing company is considering a new product in the category, which is information that the company would like to hide from its competitors.
A much more effective use of AI would have the company upload some of its data – customer reactions or sales history – and then ask the AI engine to find patterns and compare it to any other information in its LLM. However, many AI engines add the uploaded company data to their LLMs, so that a person from another company with exactly the right question can create an answer that reveals that data. Although most AI companies have policies and other protections in place to protect against this data leakage, in several recent studies, 60-75% of companies have banned the use of AI because they are concerned that these protections are insufficient. (There are many other reasons why companies hesitate, but data privacy consistently ranks at the top.)
Despite these corporate bans, I suspect that every company in the world has at least one employee who has used an AI engine – perhaps on a non-corporate PC – to solve a business problem.
In the Next Access panel discussion, one participant runs a consulting firm. In direct contrast to most other companies, she actively yearns to get her company’s data into LLM, especially if it can be somehow associated with her company’s brand. If someone asks an AI engine a question where her company’s data would improve the response, she wants the questioner to see her company as the source of wisdom, hoping it can lead to new customer engagements.
Putting a company’s wisdom and brand in front of information seekers is not a new concept. Search Engine Optimization (SEO) is the practice of making a company’s website more available to search engines like Google so that the company’s web link appears in more Google queries. This practice has spawned an entire industry of consulting and technology companies that can help brands design their websites for maximum visibility in Google’s crawlers. Companies can even pay Google to have their web link appear at the top of the page for relevant queries. It is important that these “sponsored” results are clearly marked so that the web traveler knows which Google answers are based on organic content and which are based on corporate payments.
Google has trained us all to know that the results from its search engine do not necessarily provide the correct—or even the best—answer to the question. Clicking multiple links to scan source websites has become a common, expected routine for web searchers.
AI engine users currently have different expectations. They assume that the AI engine provides the best possible answer. Even well-known AI flaws like bias and hallucinations become less common in new, more powerful AI engines. User confidence in AI accuracy is growing.
Attracting additional revenue will convince AI companies to divulge some of their algorithmic secrets to create an AI Engine Optimization (AEO) industry so that companies can reorganize their data in a way that is especially easy for companies artificial intelligence LLMs and increase the likelihood of reference to the data and company brand in AI responses to user queries? Will AI engines offer paid placement (ideally with a sponsored content note) to brands seeking to appear in AI responses?
And how will AI users react? Will they appreciate more relevant, specific answers? Or will they question the objectivity and neutrality of the AI company? These open questions demonstrate that AI is both unlike previous technological tools and, therefore, has yet to be settled on the path it will take. Stay tuned.
