meaningful only to individuals (“Joe—Important docs”), along with excessive translation and manual manipulation of data because systems do not use the same terminology or data standards.
Constraints can be liberating. The enterprise adapts and thrives when simple tagging rules that speed information flows create value through emergent behaviors within the system. Artificial intelligence is a mechanism that, properly designed and applied, speeds information flow and enables those emergent efficiencies, including tagging. It can therefore help every part of your organization do its job more quickly, efficiently, and consistently. To succeed over the long term, you must allocate resources in a sustainable way to build AI-ready assets of increasing value—including the ontologies and data structures that will serve all aspects of the business. The business needs to use continuous feedback to develop these structures, apply them to the production of value, fine-tune systems and processes, and adjust and make course corrections.
What does such a solution look like? There are three areas in which your company needs to rethink its resources, focus, and attention so it can exploit the power of artificial intelligence. My systematic plan that you can follow to prepare for an AI-powered enterprise includes these elements:
1.Data. This includes how data is architected, managed, curated, and applied. You must wrangle your messy and inconsistent data into a refined asset for high-precision, high-leverage activities. The organizations with the most agile architecture, highest-quality data, and best algorithms for applying that data to address customer and employee needs will win.
2.Technology. To deliver personalized experiences to customers (whether internal or external), appropriate technologies must scale processes by relying on detailed enterprise knowledge. They must also remain adaptable as tools, approaches, and information sources evolve and change.
3.Operationalization. Just as with the reengineering transformation of the ’80s, these improvements require a commitment to new forms of organizational discipline, with new accountabilities and metrics. This includes rethinking how the organization delivers value through end-to-end digital processes.
Let’s examine each of these three requirements in detail.
Data: The DNA of the Organization
Most executives reflexively nod their heads when discussing the value of data. “Data is extremely valuable to our enterprise,” they say. “We are undergoing a data-driven digital transformation.” According to this thinking, good data is good, and bad data is not good. But when it comes to creating ontologies—which means fixing the foundational data issues in a sustainable way—and they see the price that the enterprise needs to pay, these same executives deprioritize projects like fixing the data-quality issues. “It’s not that valuable” is the message that is communicated to the organization. Because data issues are not addressed properly at a fundamental level, tens or hundreds of millions of dollars will be spent on digital transformations that will fail in the long run.
High quality, findable, and usable data is an essential part of the AI-powered enterprise. Machine learning can find patterns in unstructured data, but it cannot make sense of information that is of poor quality or missing. Data needs to be contextualized; you cannot simply “point the AI at all of your data” as some in the industry have claimed. Depending on the application it can also require curation and structuring for ingestion into many classes of AI tools, including cognitive systems. More structure will allow the algorithm to function more precisely.
For cognitive applications such as chatbots, the required training data is the same information that humans need but with a different structure and format. Predictive modeling AI needs training data to build recognition patterns and examples to learn from. Data is more important than the algorithms, and bad data will provide bad results.
Perhaps technology vendors have assured you that their AI will fix your data. That’s optimistic.5 In fact, many of the AI technologies on the market are actually Band-Aid solutions that try to make up for our sins in data and content curation. Because of a lack of resourcing and poor data hygiene, organizations are paying the price, and that price includes trying to fix data with AI, even though it’s the organization’s own data processes and governance that are at fault. Yes, AI can help, but there’s more to it than what the large systems integrators are telling you.
The way to fix this problem is to harmonize your data with consistent data structures and models, creating a Rosetta Stone that helps your systems communicate and provides a waypoint so your AI can navigate your messy, fast moving, diverse, unstructured and structured data universe. That Rosetta Stone is the ontology.
While many master data types are challenging and prone to failure, the approaches we will discuss will increase your chances of success and will move the needle on multiple projects. Rather than striving for a “single source of truth,” the goal is to increase the consistency and quality of information so that there is less friction throughout the organization. There will be differences in organizing principles and structures, but rather than being accidental, they will be intentional. The ontology becomes a reference point to inform where information structures need to be harmonized and where there can be (intentional) differences.
Technology: Having the Right Tools to Serve Customers (Internal and External)
Choosing the right technologies and integrating them successfully is a critical part of building out your AI program. It is not simply about adopting machine learning tools and technologies that are labeled as “AI,” because, these days, every technology vendor says what it does is “AI.”
Forget AI for a moment. Recognize that knowledge workers of all types, from engineers to marketers to designers, interact with technologies to accomplish their day-to-day tasks that directly or indirectly support the ways that customers interact. These workers work with systems on their smartphones, laptops, tablets, and intelligent connected appliances and devices. Having the correct technologies integrated in an adaptable, flexible way allows for processes and functionality to evolve in parallel with customer needs and the competitive landscape. Having the right tools enabled and enhanced by AI to serve each stage of the customer journey makes the business run faster, better, and in a way more aligned with customer needs.
The real challenge is that, if you’re like most organizations, you probably have a patchwork of systems, technologies, and processes that have evolved organically over time. These have inevitably led to messy and complex integrations, manual processes, and workarounds that make things more difficult in the long run. Some people refer to this as technical debt—the shortcuts taken in the hope of deploying technology more quickly. But just like debt in the real world, this approach is costly, and you have to pay for the shortcuts, sometimes at interest rates that would be classified as usury if they were on a consumer’s credit card. What appears to save time and money in the short run, when accumulated over numerous projects and multiple years, hamstrings the business and prevents adaptation as each leader kicks the can down the road for the next person to deal with. And now that person is you.
Adapting legacy systems built on a patchwork of different platforms and technologies to changing conditions is a slow and laborious process. But it’s essential to making the tools and technology effective and the data actionable.
Operationalization: Leading with Vision and Managing Change
Every project, whether a departmental reorganization or a ten-year growth plan, begins with vision and strategy. What is different about an AI strategy is that the possibilities are entirely new, which means you will have to look at the business and customer relationships in entirely new ways. Whenever there are fundamental shifts in technologies, what becomes possible is not just an extension of where we are
