Co-written with Josh Bernoff, the HBR article answers an important question—Why do most of the AI initiatives of organizations fail?
Seth Earley, from his 20-years experience opined that it is because promises of AI vendors don’t pay off unless a company’s data systems are properly prepared for AI. Data is locked in silos, inaccessible, poorly structured, and most importantly, not organized in such a way as to be used as the fuel that makes AI work. Instead, to reap the benefits of AI, companies need to create something called an ontology, a comprehensive characterization of the architecture of all of its data.
Here are snippets from the are article:
You may have read advice that you should start small with AI initiatives. (I suggested that myself a few years back.) And it’s true — the “AI Lite” approach can generate some quick wins. But as AI initiatives inevitably multiply throughout the organization, the limits of scattered experiments become more glaring. When you feed such AI programs with different types of (sometimes incompatible) data sources, the result will entangle you in complexity. Soon you’ll have a slew of one-off AI pilots connected to your existing data systems in a way that fails to deliver broader, more strategic benefits for your business.
AI is now far enough along that a more cohesive approach is needed — a key that brings together all of your company’s data. That’s where the ontology comes in.
An ontology is a consistent representation of data and data relationships within your business, a model of all the elements that go into and connect your various information systems: the products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content, and data of all types. It is the master knowledge scaffolding of the organization. Without a consistent, thoughtful approach to developing, applying, and evolving an ontology, AI systems can only develop in a piecemeal, fragmented way — they will lack the underpinning that would allow them to be smart enough to make an impact. The ontology is at the heart of the information design of the AI-powered enterprise, an investment that will continue to pay off as AI becomes more pervasive.
Once you’ve created an ontology this way (and a system to maintain it), the benefits multiply. It becomes an essential part of not only the solution to today’s information problem, but to the next problem that arises, and the next. Keep in mind that the ontology is a continuously evolving entity — as products, services, markets, competitors and customer needs change, an intentional process for changing the ontology will keep it fresh and relevant.
It’s increasingly clear that AI is going to be solving many of those problems in the future, whether that’s by means of a customer service chatbot, a system that surfaces signals about business efficiencies and breakdowns, or any of a thousand other applications. The ontology, as a representation of what matters within the company and makes it unique, is what unifies those solutions and makes them more than just a quick fix that will just as quickly become obsolete. If you suspect that AI is the future of business — a conclusion I’m certain of — then creating an ontology is an essential investment to prepare your enterprise to realize the benefits of that future. It’s a concept that, managed and applied appropriately, makes the difference between the promise of AI and delivering sustainably on that promise.
Read the rest here.
Seth Earley is the founder and CEO of Earley Information Science. He has developed advanced information management strategies for a wide variety of organizations including Fidelity Investments, Progress Software, the Internal Revenue Service, Progress Software, Abbott Laboratories, Millennium Pharmaceuticals, Plymouth Rock Insurance, Gartner Group and others. His work has included projects for the IBM Office of the CIO to develop new application architectures and refine system performance for a worldwide deployment, and for GE to assist business unit leaders in architecting the GE Capital Virtual Boardroom which spanned 30 plus business units. He has also developed process analysis and solution architecture courses and workshops that he has taught worldwide to a large variety of industries and developed the enterprise information architecture and application of metadata standards for a large US government agency. He is co-author of “Practical Knowledge Management” which focuses on taxonomy and information architecture as the foundation for knowledge processes. He has a unique combination of business savvy, technical capabilities and the ability to bring people together to see a common vision during his working sessions.
Seth’s additional credentials include:
- Founder of the Boston Knowledge Management Forum, former adjunct professor at Northeastern University, where he taught graduate courses in Knowledge Management Infrastructure and E-Business Strategy
- Former Co-Chair of the Metadata Committee, National Digital Information Infrastructure and Preservation Program (NDIIPP) Education and Outreach for the Academy of Motion Picture Arts and Sciences (AMPAS)
- Leader of workshops for senior executives about aligning information management strategy with measurable business outcomes
- Developer of information strategy programs for clients in health care, technology, manufacturing, insurance, retail, pharmaceutical, and financial services industries