outline the steps for benchmarking the maturity of each of these components and developing a plan for bridging gaps between where you are and where you need to be.
No twenty-first-century executive can succeed without understanding the role of AI in enterprises and how to make this technology work effectively. It’s time to go beyond clouds, big data, and mobile fantasies. It’s time to learn what it takes to power your enterprise with AI, now.
TAKEAWAYS FROM CHAPTER 1
In this chapter, I’ve described how AI will change the priorities for enterprises and how ontology can play a critical role in taking advantage of those opportunities. These are the main points in this chapter:
•AI marks an inflection point in human history.
•AI initiatives fail if the information they depend on is not properly structured.
•The ontology—a central repository of data terms and relationships—is what makes it possible for AI projects to succeed.
•Organizations are like organisms that depend on freely flowing information to survive.
•Agility and adaptability are the qualities that enable organizations to thrive in their broader ecosystem.
•The failure of holistic communication, data incompatibility, and junk data prevent companies from operating effectively.
•Tagging speeds the flow of information.
•Competitive advantage arises from the ways that organizations manage data, technology, and operationalization.
CHAPTER 2
BUILDING THE ONTOLOGY
It costs over a billion dollars to build a “fab”—a semiconductor fabrication plant. At a price like that, it had better be up and running every possible hour of the day and night.
That’s the problem that Applied Materials was struggling to manage. The company bills itself as “the leader in material engineering solutions used to produce virtually every new chip and advanced display in the world.”1 Its field service technicians are tasked with getting fabs running after problems have taken them offline. A down fab can cost its owner millions of dollars per day in lost business. If Applied Materials can’t fix the problem quickly, it can end up on the hook for large penalties for failing to deliver on its service-level agreements—not to mention the damage to its reputation. It’s a tough job, because semiconductor manufacturing processes are mind-bogglingly complex.
The know-how to keep a semiconductor fab running was spread throughout so many systems and processes within Applied Materials that up to 40% of its field service technicians’ time was spent searching for the information they needed. Each plant was unique, so technicians needed to be able to locate the exact configuration of the equipment and procedures for any plant they were working in. The technicians work in dust-free, ultraclean environments and have elaborate and time-consuming processes for getting in and out of the plants. Some fabs even prohibit laptops or tablets, so technicians had to equip themselves with all the information they would need to solve any suspected problem before entering the plant.
Techs in this environment hedged their bets. They stocked their service vehicles with a wide variety of costly components, since not having the right part ready would lead to expensive additional delays. With 3,000 technicians in the field, this practice tied up tens of millions of dollars of inventory. Technicians became frustrated with attempting to locate service information across 14 different systems. Adding complexity, Applied Materials technicians working for one customer might have to maintain trade secrets for that customer, making it impossible for them to share all their information with other technicians.
Because this challenge threatened Applied Materials’ brand and reputation, it’s no surprise that 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
