The AI-Powered Enterprise. Seth Earley
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the company tried to solve the problem on three separate occasions over a five-year period. All three attempts failed.
When Applied Materials brought my company in to help them, it became clear that the missing piece of the puzzle was how to organize diverse sources of information and unify the field technician experience. The key was finding ways to remove friction from the process of accessing exactly the information that was needed to solve the problem at hand. The magnitude of the problem seemed overwhelming because the company had so much information in so many places
We discussed the problem with one of the company’s senior finance executives and outlined a solution, including an ontology and its subsidiary classification systems, known as taxonomies. The ontology would enable an AI-powered semantic search approach to organize the information for retrieval in the context that a technician would need when servicing a fabrication plant. The finance executive was dubious. He asked, “Why do we need taxonomies? Why don’t we just get Google?”
I responded to his question with one of my own: “Do you have a chart of accounts for your finance organization?” Naturally, he did. So I asked, “Why don’t you get rid of your chart of accounts and just get Google?” He laughed at such a preposterous idea. “Well,” I responded, “what we want to build is like a chart of accounts for field service knowledge. It organizes information far better than a random search ever could. It will help the service people locate exactly what they need when they need it.”
Consider the parallels for a moment. A chart of accounts organizes financial information for the accounting department and helps financial managers identify patterns in the numbers, make predictions about the future, and decide how to allocate resources. It provides the organizing principles to eliminate noise (such as irrelevant trends, anomalies, onetime events, or variations that are immaterial) and identify the important signals (trends in leading indicators like leads, success of promotions, effectiveness of advertising, and the impact of pricing changes). By providing a structured way to interpret corporate data, an ontology does for knowledge what the chart of accounts does for financial information.
We helped Applied Materials create the required ontology. Once the company had identified the multiple vocabularies, hierarchies, and relationships that comprised the ontology, the next step was to integrate it with the organization’s technologies and apply it to the data. Various elements of the techs’ experience needed to be designed to reflect the organizing principles of the ontology. Information about parts had to be tagged consistently: a part could not be called by a short name in one system and a stock number in another. Similarly, documents for troubleshooting had to be reclassified using a form of AI called “text analytics.” Text analytics starts with example documents that have been tagged with information from the ontology, and then automatically applies the same tags to documents containing similar text.
It took a lot of work to implement the ontology in multiple systems and processes; incorporate it into workflows for content publication and approval; connect it to enterprise resource planning and digital asset management systems for visual part identification; and incorporate dozens of content libraries by ingesting them, automatically classifying content, and manually mapping relationships.
But once this work was complete, the architecture of the Applied Materials solution allowed field technicians to get the information they needed when they needed it. It helped them locate exactly what would be most useful by building logical groupings of content—surfacing the troubleshooting guides according to the process the tech would be troubleshooting, for example. The ontology enabled this by creating hierarchies—lists of concepts that are grouped logically—so that technicians could scan groupings and infer what they needed just by scanning the list. These hierarchies also allowed ambiguous queries to be clarified by asking questions about the parts and their applications. When they searched, the search results were grouped in a way that made sense to the technician—that fit their “mental model” of the problem and solution. These changes enabled the computer to give the technicians what they were seeking using the terms they were likely to be thinking about, rather than forcing them to root through masses of information with terms they might not recognize, organized in incompatible ways more accessible to computers than to humans. The result was a system that reduced the time that field technicians spent searching for information by half. The company estimated their savings at tens of millions of dollars per year. We enabled technicians to reduce their component and replacement part inventories and speed turnaround time for down fabrication plants.
And Applied Materials was able to live up to its reputation as a global leader in chip manufacturing, an essential element for its future business success.
WHY ONTOLOGIES MATTER
With the speed and effectiveness of the company at stake, building an ontology is not just an IT project. It is one that should matter to every CEO, CMO, and senior manager inside your company. Once you create the framework for the ontology, you can get more from your current investments in technology and apply emerging artificial intelligence techniques to drive your business. The ontology is the tool that teaches intelligent machines how your business runs. Without it, neither your systems nor your employees can truly understand how to access and organize the lifeblood of the business—the knowledge and information that provides value for your customers and the marketplace.
Ontologies are the secret weapon that will bring you victory in the battle for customers by transforming your company into an AI-powered enterprise. AI capabilities, supported by an ontology, will allow employees and the systems they use to function faster than ever before. It becomes a supercharger for your business. It helps you get products and services to market faster, serve customers more efficiently, and take advantage of quickly emerging opportunities in the marketplace. With the streamlined information flows that the ontology supports, both automated systems and humans can make better decisions.
Your ontology has the potential to accelerate your enterprise whether you work in retail, finance, health care, government, or any other sector. Once ontology-powered AI is in place, you can create:
•apps that suggest exactly the right product for each customer, based on history, context, inventory, and even the weather;
•tools that facilitate team meetings by suggesting the best solutions to your toughest problems;
•audience insights based not on superficial attributes like age but on people’s secret motivations—along with product improvements that match those motivations;
•sales improvements that prioritize the leads that are most likely to generate more profit and make this determination more quickly than any other method now available;
•systems that predict when equipment will fail and proactively order maintenance before disaster strikes;
•virtual assistants that provide new capabilities and levels of service at a lower cost than previously possible; and
•bots that will help engineers solve challenging design and manufacturing problems by embodying the knowledge and expertise currently in the heads of your own experts—much as Applied Materials did.
Ontologies speed the information metabolism of the enterprise, forming the foundation for improved search, information management, and digital asset retrieval. They support mechanisms that improve communication among systems by acting as the Rosetta Stone of system integration.
Think of an ontology as a “knowledge chart of accounts” for marketing, engineering, finance, human resources, operations, and sales. Ontologies form the source of features for predictive analytics programs to improve their performance. They are the source of labels for navigational elements on an ecommerce website. They are the foundation for improved customer service since they contain knowledge of customers, problems, processes, and solutions. They provide improved business insights and intelligence by allowing for consistent analysis of products, revenue, costs, efficiencies, and metrics across the enterprise. They are the source of rights-management attributes and can help manage risks, exposures, and compliance.
Since the virtual world is entirely composed of data, victory will go to the organizations that can best control, manipulate, and exploit that data