This content is part of the Essential Guide: Digital transformation strategy guide: From e-fax to AI

How WCM and AI promote smart digital user experiences

Digital user experiences and other business processes are poised to become much richer with artificial intelligence at the helm.

Artificial intelligence is the concept that machines can do what humans can do.

It's one of those big ideas that date back to well before the invention of the first digital computer and that continues to intrigue us. But our notion about what constitutes AI is continually changing. Today, we don't think twice about using our smartphones to discover nearby restaurants, to snap photos of toys in a store to see whether Amazon sells them at better prices or to get recommendations about films we should watch.

Artificial intelligence (AI) is poised to augment several business processes, and digital user experience is just one of them. How companies present information in digital environments will be greatly buttressed by machine learning. A real estate application could bring in third-party information on properties and nearby attractions, and a mobile GPS app could provide recommendations, while also guiding us to our destination. AI enhances these kinds of digital user experiences.

In the age of smart experiences

We are delighted when sophisticated algorithms are combined with oodles of data to produce smart experiences. What was once considered the realm of AI and science fiction has become just another aspect of our digital lives. And, as users, we are blissfully unaware of the effort that goes into creating, organizing and managing the content required to produce these experiences.

Yet business and technical leaders intent on digitizing key activities to connect with customers, compete effectively and win in their respective marketplaces face a different challenge: how to leverage the perceived intelligence of smart machines for a competitive advantage. Many firms are already investing heavily in web content management (WCM) platforms, tools and technologies to create, store, organize, produce and distribute information that appears on one or more websites, in addition to creating multichannel experiences on mobile and tethered devices alike.

How and when should AI in the enterprise make a difference for WCM-powered experiences?

From my perspective, AI today represents a set of next-generation enabling technologies for WCM that, when effectively deployed within enterprise environments, will provide new opportunities for contextual content delivery. Leaders should pay attention to large-scale machine learning, natural language processing and deep learning.

Pattern recognition with machine learning

As I have discussed elsewhere on this site, machine learning will fuel smart online experiences where machines find patterns amid massive amounts of data without being explicitly programmed to do so. Training sets are the key to success -- that is, having prior examples that enable machines to learn about relationships. In business situations, WCM platforms are going to enable a virtuous circle for empowering machine-learned inferencing, leveraging capabilities to process textual and numerical data.

It's important to recognize that the information architecture matters. The more well-defined the content is upfront when it is approved for publication and stored, the better a machine will be at detecting patterns and producing intelligent results.

There's a big payoff when authors and editors tag content by terms within predefined thesauri or semantic networks, in addition to ad hoc words and phrases. Organizations need to make the upfront investments and align their information architecture with their business goals.

Understanding text with natural language processing

Natural language processing (NLP) seeks to decode and make sense out of stream of consciousness words and phrases, whether spoken or typed. (Speech-to-text transformation techniques convert spoken sounds into text strings.) NLP produces meaningful responses that either provide answers to questions or that recommend the next best steps. The WCM platform provides the raw materials, which is the stored content in all of its many renditions.

NLP begins as an effort to define the linguistic rules for understanding parts of speech, grammar, relationships among words and phrases, and overall semantic meanings of text. It includes multiple algorithms for analyzing text within a large collection of documents. Through trial and error testing, experts refine the algorithms to improve the results. Notably, while this technology predates machine learning, it now leverages pattern recognition capabilities to refine the precision of a priori rules.

Shaping digital user experience with deep learning

Deep learning is the latest addition to commercial AI. Beyond the ability to find patterns in text, machines can now recognize digital objects, determine what they are and infer how sets of things are interrelated. Deep learning applies complex algorithms and the power of highly scalable systems (almost always delivered within a cloud environment) to detect and make sense out of seemingly disparate objects. WCM platforms provide the fuel, which is the various types of stored content. With deep learning, machines can find patterns, learn from them and act on them.

Think about it for a minute. As we produce innovative digital user experiences, we are going to rely less on words and phrases and more on a cacophony of other digitized things -- static photos, videos, musical tracks and medical images, to name a few. And, these things, in turn, contain various digital tidbits -- such as people and scenes within a photo or sound patterns within a musical track.

New is our ability to make sense of these discrete tidbits and to shape innovative digital experiences around them. For instance, some consumer-oriented photo apps now automatically assemble a few choice snapshots of our friends and family members -- selecting from the thousands of photographs we store online and automatically recognizing people, places and objects. These apps get better over time as we make corrections for recognizing peoples' names and faces and describing particular scenes.

Look for the adoption of deep learning within business-oriented photo apps and visual experiences within the next year or so. Leading real estate sites have already announced their intention to apply AI to the whole process of house hunting.

AI adds value to WCM

AI is going to challenge our assumptions about WCM in two ways. First, we are going to need a more comprehensive information architecture. So far, we have mainly thought about managing content as text and structured data. Rich media content types -- such as images, streaming audio and video, and 3D objects -- are managed by special purpose repositories and delivered as disconnected digital user experiences, primarily to specialists.

Needed are information architectures that manage all of the content types in a holistic manner and that create the customer journeys for addressing ordinary business activities.

Second, to deliver on the promise of AI, we are going to need much better metrics about how content is being delivered and used. It's not enough to count page views, click-throughs and optimized search terms. We need to build the customer journeys enabled by AI, determine the success of these journeys and assess the overall satisfaction of the people who experience them.

When all is said and done, AI adds value to WCM. For companies that are already investing in a WCM platform, AI is going to raise the bar for contextual content delivery. The future begins by managing all of the types of content with common and consistent web services. Savvy business leaders can then identify the content-powered innovations needed to deliver next-generation, smart digital user experiences. 

Next Steps

How has the WCM landscape evolved to today's changing digital experiences?

How do you gauge if your digital content strategy is successful?

Develop a content strategy before beginning a web content migration

Dig Deeper on Text analytics and natural language processing software