inBusinessGreaterPheonix printed an excerpt from Seth Earley's book, "The AI-Powered Enterprise" as an Articles titled Harness the Power of AI on June 1, 2020.
Any big company is likely to have an abundance of technology. It has systems for customers, inventory, and products, along with websites and mobile apps. These systems are spitting out data all day long. Within that data is exactly the information needed to make a business more responsive. The problem is, the data is often not used as it could (and should) be.
Even after multiple generations of investments and billions of dollars of digital transformations, organizations are still struggling with information overload, with providing excellent customer service, with reducing costs and improving efficiencies, with speeding the core processes that provide a competitive advantage. Why is this happening? Because key foundational principles are ignored, given short shrift, deprived of resources, or considered an afterthought. The elements that are required to make the shiny new technologies live up to their promise require hard work that is not sexy and shiny. There are new tools and approaches that make these efforts more efficient, and ways to embed new approaches to dealing with information and data, but they still require discipline, focus, attention, and resources.
Perhaps your organization has experimented with AI. An executive at a major life insurance company recently told me, “Every one of our competitors and most of the organizations of our size in other industries have spent at least a few million dollars on failed AI initiatives.” In some cases, technology vendors have sold “aspirational capabilities” — functionality that was not yet in the current software. But in most cases, the cause of the failure was overestimation of what was truly “out-of-the-box” functionality, overly ambitious “moonshot” programs that were central to major digital transformation efforts but unattainable in practice, or existing organizational processes incompatible with new AI approaches. Leadership may have bought into the promise of AI without adequate support from the front lines of the business. Technology organizations may not have been adequately prepared to take on new tools and significant process changes. In many cases, the technology may have been potentially capable of functionality, but the data, locked in siloed systems, was inaccessible, poorly structured, or improperly curated.
Many AI programs attempt to deal with unstructured information and replicate how humans perform certain tasks, such as answering support questions or personalizing a customer experience. That may require pulling information from multiple systems and weaving together multiple processes, including some that have historically been done manually. To deliver on its promise, AI needs the correct “training data,” including content, metadata (descriptions of data), and operational knowledge. If that data and corresponding outcomes are not available in a way that the system can process, then the AI will fail.
How do you make those data and outcomes accessible to power the AI? That’s where the ontology comes in.
The Central Role of the Ontology
AI cannot start with a blank page. It leverages information structures and architecture. Artificial intelligence begins with information architecture. In other words, there is no AI without IA.
AI works only when it understands the soul of your business. It needs the key that unlocks that understanding. That’s the science behind the magic of AI. The key that unlocks that understanding is an ontology: a representation of what matters within the company and makes it unique, including products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content and data of all types. It’s a concept that, correctly built, managed, and applied, makes the difference between the promise of AI and delivering sustainably on that promise.
Simply put, an ontology reveals what is going on inside your business — it’s the DNA of the enterprise. Ontology is also referred to as a “knowledge graph” and technology organizations are realizing that so-called “graph databases” offer tremendous advantages over traditional database structures.
An ontology is a consistent representation of data and data relationships that can inform and power AI technologies. In different contexts, it can include or become expressed as any of the following: a data model, a content model, an information model, a data/content/information architecture, master data, or metadata. But an ontology is more than each of these things in themselves. However you describe it, the ontology is essential to and at the heart of AI-driven technologies. To be clear, an ontology is not a single, static thing; it is never complete, and it changes as the organization changes and as it is applied throughout the enterprise.
The ontology is the master knowledge scaffolding of the organization. Multiple data and architectural components are created from that scaffolding, so without a thoughtful and consistent approach to developing, applying, and evolving the ontology, progress in moving toward AI-driven transformation will be slow, costly, and less effective. The components of the ontology are the ones we have mentioned: metadata structures, reference data, taxonomies, controlled vocabularies, thesaurus structures, lexicons, dictionaries, and master data correctly designed into the information technology ecosystem. The ontology is at the heart of the information design of the AI-powered enterprise and it becomes an asset of ever-increasing value.
While it is true that some algorithms can operate on data without an external structure, they still operate based on the features programmed into the underlying system. Even if there is no structure to the raw data, the algorithm will perform better if more of that structure is provided as an input — as an element of the ontology.
Ontologies are a complex topic. For now, just know that the ontology is what makes the difference in whether AI drives your enterprise forward or just adds to the incompatible welter of technology that is slowing you down.