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Further, generative AI is shown to be more useful as a co-pilot for senior employees that can sift through AI “hallucinations” — inaccurate information presented as fact — and take the output as an aid. Inexperienced employees, however, may not be discerning enough and need a path to develop this knowledge. The sentiment was echoed by Ted English, former CEO of TJX Companies and current executive chairman of Bob’s Discount Furniture, who told us leadership requires “a lot of instinct, experience, and knowledge. Some of it you can’t get from a machine. Technology reinforces and allows you to make a more confident decision.”

The first layer of the framework is intentionality. In this context, we mean that a company’s business model should be purposefully designed around AI capability, rather just applying AI to existing processes. Spencer Fung, president and CEO of Li & Fung, a global supply-chain and logistics company, gave us an analogy: “Companies acquiring AI without a new business model is like a company digitizing a horse and carriage — while the competition has created a digital automobile.”

Next comes integration across all functions of the enterprise, with horizontal communication and AI as the enabling layer — in other words, getting rid of silos.

The real challenge, however, is implementation. Garry Kasparov, former world chess champion, has written that winning performance does not come from combining the best technology with the best people — but from the best process of combining. To achieve this, talent must be familiar with AI capabilities and know how best to utilize them.

However, AI is an evolving technology, and that necessitates a business add slack to the system to allow opportunity for learning.

Competitive advantage cannot be achieved without humans in the loop. Rushing to replace talent with AI is a huge mistake. Why? First, AI is copyable. What is not copyable is a unique business model, processes, and thoughtful integration of humans.

Second, AI is based on historical data that may not hold true in a volatile global business environment.

Third, AI is subject to hallucination and “drift,” where output is either fabricated by the AI or simply inaccurate.

By reading this, you’ll learn:  What an entity is and why it’s important. The history of semantic search. How to identify and use entities in the SERP. How to use entities to rank web content.

Examples of entities

Perhaps the best example of entities in the SERP is intent clusters. The more a topic is understood, the more these search features emerge

What is an entity? An entity is a uniquely identifiable object or thing characterized by its name(s), type(s), attributes, and relationships to other entities. An entity is only considered to exist when it exists in an entity catalog.  Entity catalogs assign a unique ID to each entity. My agency has programmatic solutions that use the unique ID associated with each entity (services, products, and brands are all included). If a word or phrase is not inside an existing catalog, it does not mean that the word or phrase is not an entity, but you can typically know whether something is an entity by its existence in the catalog.

concepts and ideas are entities

More could be said about schema, but suffice it to say schema is an incredible tool for SEOs looking to make page content clear to search engines.

That brings us to the current search system. Google went from 570 million entities and 18 billion facts to 800 billion facts and 8 billion entities in less than 10 years. As this number grows, entity search improves.

How to optimize for entities What follows are key considerations when optimizing entities for search: The inclusion of semantically related words on a page. Word and phrase frequency on a page. The organization of concepts on a page. Including unstructured data, semi-structured data, and structured data on a page. Subject-Predicate-Object Pairs (SPO). Web documents on a site that function as pages of a book. Organization of web documents on a website. Include concepts on a web document that are known features of entities.

We know this, so how can we optimize for it?  Your documents should contain as many search intent variations as possible. Your website should contain every search intent variation for your cluster. Clustering relies on three types of similarity:  Lexical similarity.  Semantic similarity. Click similarity.

Schema is one of my favorite ways of disambiguating content. You are linking entities in your blog to knowledge repositories. Balog says:  “[L]inking entities in unstructured text to a structured knowledge repository can greatly empower users in their information consumption activities.” 

(Remember, Google wants to understand the hierarchy of the content, which is why H1–H6 is important.)

Balog writes:  “We wish to help editors stay on top of changes by automatically identifying content (news articles, blog posts, etc.) that may imply modifications to the KB entries of a certain set of entities of interest (i.e., entities that a given editor is responsible for).” Anyone that improves knowledge bases, entity recognition, and crawlability of information will get Google’s love.  Changes made in the knowledge repository can be traced back to the document as the original source.  If you provide content that covers the topic and you add a level of depth that is rare or new, Google can identify if your document added that unique information. Eventually, this new information sustained over a period of time could lead to your website becoming an authority. This isn’t an authoritativeness based on domain rating but topical coverage, which I believe is far more valuable. With the entity approach to SEO, you aren’t limited to targeting keywords with search volume. All you need to do is to validate the head term (“fly fishing rods,” for example), and then you can focus on targeting search intent variations based on good ole fashion human thinking.

We begin with Wikipedia. For the example of fly fishing, we can see that, at a minimum, the following concepts should be covered on a fishing website: Fish species, history, origins, development, technological improvements, expansion, methods of fly fishing, casting, spey casting, fly fishing for trout, techniques for fly fishing, fishing in cold water, dry fly trout fishing, nymphing for trout, still water trout fishing, playing trout, releasing trout, saltwater fly fishing, tackle, artificial flies, and knots. The topics above came from the fly fishing Wikipedia page. While this page provides a great overview of topics, I like to add additional topic ideas that come from semantically related topics.  For the topic “fish,” we can add several additional topics, including etymology, evolution, anatomy and physiology, fish communication, fish diseases, conservation, and importance to humans.  Has anyone linked the anatomy of trout to the effectiveness of certain fishing techniques? Has a single fishing website covered all fish varieties while linking the types of fishing techniques, rods, and bait to each fish?  By now, you should be able to see how the topic expansion can grow. Keep this in mind when planning a content campaign. Don’t just rehash. Add value. Be unique. Use the algorithms mentioned in this article as your guide. Conclusion This article is part of a series of articles focused on entities. In the next article, I’ll dive deeper into the optimization efforts around entities and some entity-focused tools on the market.

89% said less time will be spent on reporting.