As I researched various trends going into 2016, several common themes emerged: the usual suspects of social, mobile, analytics and cloud (SMAC), digital transformations, distributed processing, intelligent devices, always-on sensors.  Add to this machine intelligence, virtual assistants, personalization, contextualization and customer-centricity.  Frog Design, a company that specializes in innovative design, discusses several intriguing trends[1] in its annual technology predictions, from block chain/crypto-currency (i.e., technology that enables Bitcoin) to data-driven design, automated human-centered design, virtual reality applications and several flavors of artificial intelligence. (However my favorite was the last one, about “friendship as a service”)

Gartner speaks to several trends[2] regarding interconnected, always-on, distributed processing capabilities and devices.  The “device mesh” describes everyday objects that will have sensors and be connected. “Ambient user experience” is the way the environment will respond using the information the sensors gather and report – from knowing when you enter a room, to recognizing your preferences and adjusting functionality of such resources as home entertainment systems.

Information will flow from the collection of machine, sensor, and environmental data that is constantly streaming from traditional and nontraditional sources. “Advanced machine learning” will be required to make sense of all of this data, identify patterns, provide trends and insights, and separate the signal from the noise. “Autonomous agents and things,” which Gartner identifies as an outgrowth of the sensors, connected devices and machine learning, will allow intelligent devices to adjust to a particular condition, solve a problem, or accomplish a task with little or no human intervention.

These trends are all highly interrelated and have several implications for business and technology leaders.  The first implication is that contextualizing all of that information is more important than ever – this requires understanding where value is created in your organization and how you are reaching customers with that value.  Traditional value chains will be disrupted for a distributor of industrial supplies when equipment automatically detects low supplies and orders replenishment directly from the manufacturer.  An insurance company’s value chain will be disrupted when the owners of fleets equip them with sensors and sell that data to the insurer whose risk algorithms more accurately values and prices that risk.  A technical field service organization may lose its place in the value chain when the equipment manufacturer creates autonomous controls and programs that optimize functionality based on data collected from sensors rather than having a field tech perform that tuning.  Product capabilities are increasingly software controlled; therefore, data from customers usage patterns can be fed back into the design process.  User experience and usability can be based on real-time feedback.  

The Customer Is Still at the Center

Forrester’s 2016 predictions centered on customer experience[3] including raising the bar in personalization by creating more contextualized experiences. Companies that don’t make integrated approaches to customer experience a priority will suffer in the marketplace and customer experience imperatives from the CEO will disrupt leadership structures in the enterprise, resulting in increased power for the CMO, or the hiring of Chief Digital Officers and Chief Data Officers).  Forrester also recognizes that customer- centricity is a cultural shift even more than a process or technology shift in the enterprise. Impediments to change caused by turf wars, business-as-usual mindsets, and departmental silos can be addressed by changing the ways that people do their jobs and understand their role in the customer-centric culture.  

Deeper analytics capabilities and continued investment in digitization of end-to-end processes are the cost of admission, and organizations that are not making sufficient investments in these areas are at a significant competitive disadvantage. On the positive side, companies that do make these investments and operationalize them,, will become less vulnerable to disruptive startups and innovators because they will have incorporated some of the innovations already.  These observations are more about continued emphasis rather than new approaches; however, the implication is that the enterprise needs to remove impediments to customer-centricity, whether technological, cultural, or process in nature.

Virtual Digital Assistants: Where Machine Learning Meets Analytics to Transform the Customer Experience

At the intersection of many of these trends is the concept of the virtual digital assistant.  Gartner suggests that these are 5 - 10 years from being practical, and are at the “innovation trigger” point of their hype cycle[4] (the first the first stage) and refers to them as a class called “autonomous business technologies” or “smart machines.”

Some examples of virtual digital assistant applications with limited functionality are emerging in the marketplace (Siri, Cortana) and chat bots tuned to answer narrow context questions such as assisting with shopping[5].  According to natural language and AI expert Mark Stephen Meadows, virtual assistants are increasingly able to handle simple tasks and are in fact the testbeds for tomorrow’s personal robots[6]. 

Predictive Analytics, Personalization, and Virtual Digital Assistants. 

Personalization is entering the “slope of enlightenment,” [7] (a more mature stage of development) and according to Gartner, is 2 - 5 years from mainstream adoption.  Predictive analytics is still on the innovation trigger curve[8], but has the same timeframe to reach widespread adoption.  Virtual assistants leverage search algorithms, natural language processing, predictive analytics as well as other technologies in order to contextualize and personalize the experience. 

Some believe that virtual assistants, precisely tuned to an individual’s preferences because they are monitoring and analyzing the individual’s choices, will replace search engines. The same principles can be applied to a B2C ecommerce site or to an industrial manufacturer selling complex technical solutions.  One vendor in the space “believes [that] in five years people will interact with smart, artificial-intelligence-based personal assistants in lieu of search engines.”[9] The content marketing industry is beginning to ask how content can be tuned for such agents[10], as the content and information access landscape shifts. 

Emerging Artificial Intelligence and Application to Customer Experience

All of the industry experts predict continued advances in machine learning and artificial intelligence applications.  These applications are rapidly evolving and will be emerging in specialized applications like autonomous vehicles and controls and in more generalized components of improved customer experience in the form of corporate knowledge and support base applications.  Advanced personalization and more precise search functionality use many of the same mechanisms that support virtual assistant functionality.  These technologies depend on content and data sources that represent the domain of the problem being solved.  Curating, structuring and organizing that information will improve the ability for both humans and agents to access and make sense of. Some form of AI drives intelligent agent technology and content and data are at the heart of these applications.     

The coming year will see a great deal of progress in the AI space.  We will also likely see these AI applications being oversold, over-hyped and poorly deployed as companies fall into what Gartner calls the “trough of disillusionment” which is the final stage before embarking on the “slope of enlightenment” where organizations travers the learning curve and tools are gradually leveraged and then finally  the “plateau of productivity” as they become mainstream. 

Business and technology leaders need to keep in mind is that content structure can be tuned for virtual assistant applications with similar approaches as those used for human consumption.  What this means for the enterprise is that curating content is a worthwhile endeavor both for the short term and for the longer term while these technologies mature.  Beginning with highly curated, structured content will be more effective for intelligent agent deployment than starting with poor quality, disorganized information.  

Virtual digital assistants represent the convergence of several major trends for 2016. To learn more about them, be sure to listen in on our webinar on Wednesday, January 20^th.