Virtual Assistant Update

 

We recently published “Virtual Assistant Update.” It’s a broad and not too deep update on virtual assistant technologies, products, suppliers, and markets from the perspective of the five leading suppliers: [24]7, Creative Virtual, IBM, Next IT, and Nuance. These are the leaders because they:

  • Have been in the virtual assistant business for some time (from 16 years for [24]7 via its acquisition of IntelliResponse to four years for IBM).
  • Have attractive and useful virtual assistant technology
  • Offer virtual assistant products that are widely used and well proven.
  • Want to be in the virtual assistant business and have company plans and product plans to continue.

The five suppliers are quite diverse. There’s the public $80 billion IBM and the public $2 billion Nuance. Then there are the private [24]7, a venture backed company big on acquisitions and the more closely held Creative Virtual and Next IT. Despite these big corporate-level differences, the five’s virtual assistant businesses are quite similar. Roughly they’re all about same size and the five compete as equals to acquire and retain virtual assistant business.

By the way, across the past 12 to 24 months, business has been good for all of the five suppliers. Customer growth has been very good across the board. Our suppliers have expanded into new markets and have introduced new and/or improved products.

Natural Language Processing and Machine Learning

Technologies are quite similar, too. All five have built their virtual assistant offerings with the same core technologies: Natural Language Processing (NLP) and machine learning.

Virtual Assistants use NLP to recognize intents of customer requests. NLP implementations usually comprise an engine that processes customer requests using an assortment of algorithms to parse and understand the words and phrases in a customer’s request. An NLP engine’s processing is guided by customizable and/or configurable deployment-specific mechanisms such as language models, grammars, and rules. These mechanisms accommodate the vocabularies of a deployment’s business, products, and customers.

Virtual assistants use machine learning technology to match actual customer requests with anticipated customer requests and then to select the content or execute the logic associated with the anticipated requests. (Machine learning algorithms learn from and then make predictions on data. Algorithms learn from training. Analysts/scientists train them with sample, example, or typical deployment-specific input then with feedback or supervision on correct and incorrect predictions. A trained algorithm is a deployment-specific machine learning model. The accuracy of models can improve with additional and continuing training. Some machine learning implementations are self-learning.)

Complex and Sophisticated Work: Consultant-led or Consultant-assisted

The work to adapt NLP and machine learning technology implementations for virtual assistant deployments is sophisticated and complex. This is work for experts: scientists, analysts, and developers in languages, data, and algorithms. The approach to this is work differentiates virtual assistant suppliers and products. The approach drives virtual assistant product selection. Here’s what we mean.

All the virtual assistant suppliers have built tools and package predefined resources to make the work simpler, faster, and more consistent. Some suppliers have built tools for the experts and these suppliers have also built consulting organizations with the expertise to use their tools. Successful deployments of their virtual assistant offerings are consultant-led. They require the services of the suppliers’ (or the suppliers’ partners’) consulting organizations.

Some suppliers have built tools that further abstract the work and make it possible for analysts, business users, and IT developers to deploy. While these suppliers have also built consulting organization with expertise in virtual assistant technologies and in their tools, successful deployments of their virtual assistant offerings are consultant-assisted and may even approach self-service.

So, a key factor in the selection of a virtual assistant product is deployment approach: consultant-led or consultant-assisted. Creative Virtual, Next IT, and Nuance offer consultant-led virtual assistant deployments. [24]7 and IBM offer consultant-assisted deployments. For example, IBM Watson Virtual Agent includes tools that make it easy to deploy virtual assistants. In the Figure below, we show the workspace wherein analysts specify the virtual assistant’s response to the customer request to make a payment. Note that the possible responses leverage content, tool, and facilities packaged with the product.

ibm watson va illos

© 2017 IBM Corporation

Illustration 7. This Illustration shows the Watson Virtual Agent workspace for specifying responses from the bot/virtual assistant.

 

Which is the better approach? Consultant-assisted is our preference, but we’ve learned over our long years of research and consulting that deployment approach is a function of corporate, style, personality, and culture. Some businesses and organizations give consultants the responsibility for initial and ongoing technology deployments. Some businesses want to do it themselves. For virtual assistant software, corporate style could very well be a key factor in product selection.

 

 

 

 

The Dialog Feature of IBM Watson Engagement Advisor

We just updated our Product Evaluation Report on IBM Watson Engagement Advisor. It’s an update to our July 10, 2014 report. Both the scientists at IBM Research and the developers in the IBM Watson Group have been busy improving Watson and Watson Engagement Advisor, busy and productive enough to drive us to do the update. Here are the highlights:

  • Dialog is a new feature of Watson Engagement Advisor that provides facilities to support complex interactions between virtual assistants and customers. These interactions include prompt and response conversations as well as business processes, transactions, and supplementary questions. Dialogs can guide customers through the necessary steps to an outcome or help answer customers’ vague and ambiguous questions.
  • Knowledgebase additional input file support. MHT and ZIP files can be ingested into Watson’s knowledgebase, adding to HTML, Microsoft Word, and PDF file formats.
  • Watson Experience Manager is the visual toolset that subject matter experts use to configure, train, test, and administer Watson Engagement Advisor deployments. Improvements include new tools to configure Dialog conversations.
  • The Cognitive Value Assessment (CVA) is a consulting offering designed to help organizations identify use cases and benefits through examination of issues and pain points in their customer and end user business processes.
  • Product positioning. First contact self-service resolution

Dialog is the most important product improvement. Watson Group used technology from its May 2014 acquisition of Chatswood, New South Wales, AU virtual assistant software supplier Cognea to help build Dialog. The feature helps IBM catch up with its virtual assistant software competitors. The leading suppliers—Creative Virtual, IntelliResponse, Next IT, and Nuance—had all been offering Dialog-like capabilities for some time. Prompt and response conversations have become a key customer service requirement for virtual assistants. These conversations been the approach for answering vague or ambiguous customer questions, helping virtual assistants gather information from customers that concretizes or disambiguates questions (or problems) like, “I can’t get my printer to work,” or “What’s my balance?”

And, on that “What’s my balance?” example, prompt and response conversations are also an approach for supporting personalized tasks and transactions, a hot trend and an emerging requirement for virtual assistants to “act” more like live agents within their interactions with customers. Personalized tasks and transactions take a bit of application integration—therefore some programming—to access, retrieve, and use customer profile data and account data, but, once a virtual assistant has that data, it can perform a wide range of activities for customers in addition to delivering answers and solutions. Think of the Nuance Nina virtual assistant taking pizza orders for Domino’s (https://www.youtube.com/watch?v=noVzvBG0GD0) or Sara, a Creative Virtual V-Person virtual assistant helping consumers with online and mobile banking presenting account balances at Commercial Bank of Dubai (https://www.youtube.com/watch?v=rCvMJYQ0OT0).

Dialog is the mechanism that enables Watson Engagement Advisor virtual assistants to act more like live agents and web concierges. With that bit of application integration/ programming, they can perform personalized and/or transactional tasks for customers. Watson Engagement Advisor’s virtual assistants also have the advantages of Watson’s cognitive technology, which lets them have more flexible, more varied conversations. Watson can answer many types of questions, question that have answers like a simple fact, the definition of a term, the description of a topic, yes/no or true/false, and the steps in a procedure, or an approach to trouble shooting a problem. Dialog lets customers interject relevant but out of band questions within prompt and response conversations then, after the virtual assistant delivers the answer, either return to the Dialog or continue out of band interactions, perhaps, entering other Dialog flows. For example, in a customer/Watson Engagement Advisor virtual assistant session about property and casualty insurance, the customer might interject a question about extra coverage for jewelry in the middle of a Dialog implementing the application for a standard property policy. The virtual assistant can answer the jewelry question, answer any additional jewelry coverage questions, return to the policy application conversation, or even complete an application for a jewelry rider. For new customers, the policy application conversation will collect appropriate customer data and pass it to the external app. For existing customers, the virtual assistant will access the appropriate external app for the customer data. Live agents might do that data access manually from their desktops. Virtual assistants must do it with programming.

In the current Watson Engagement Advisor release, Dialog functionality and the tools are essentially what Cognea had built and offered. Watson Group’s developers are working hard to integrate the functionality more seamlessly within Watson Engagement Advisor and to integrate and improve the tools on the Watson Experience Manager toolset. We don’t think that Dialog is part of any of the five or so live Watson Engagement Advisor deployments, but going forward, we think that it will become part of most deployments. In fact, every virtual assistant should provide the capabilities to perform actions on behalf of customers.