Artificial intelligence technology is about to change customer support, to the great benefit of customers and the companies that serve them.

This imminent evolution presents a major opportunity for customer support teams that are able to effectively leverage AI to craft consistently amazing customer experiences, at lower cost.  At Idiomatic, we are building a novel AI system that partners with customer support teams to make it easy to do exactly that.

Starting from the developments that have brought us to this point and a survey of the current state of AI for support, this post lays out the Idiomatic view of what AI for customer support should be.

Why Now

Customers have ever-higher expectations for the product and service experiences that companies deliver.  The companies that win are those that create great customer experiences, and customer support teams are on the front lines of doing so.

However, consistently creating amazing customer experiences is currently difficult and costly, particularly at scale. We’ve seen this in both our own team’s experience and our many conversations with great support teams at beloved companies.  It requires being able to consistently delight customers, rapidly solving even the most complex customer problems and empathetically connecting with and helping even the angriest customers.  Great support teams also communicate actionable insights based on all of those customer interactions to the rest of the company to improve the entire customer experience.  These tasks are non-trivial to execute (see our previous blog posts for some workflow best practices), even for a large and experienced support team.

Fortunately, even as customer expectations reach greater and greater heights, rapid advances in AI technology are creating new state-of-the-art capabilities for language understanding, speech recognition, and many other domains.  These AI advances, intelligently applied, promise to make support better, easier, and less expensive.

The Current State of AI for Support

Unfortunately, existing applications of AI to support do not deliver on its promise. All too often, existing systems create poor customer experiences in order to yield cost savings and are limited in their usefulness.

chatbotAnyone who has interacted with chatbots knows how quickly, and frustratingly, they reach their limits.  As a result, some companies have instead adopted a human-plus-chatbot model in which a chatbot suggests responses and a human agent can override them.  This approach better avoids the poor customer experiences that occur when fully autonomous chatbots fall short, but it only helps with one component of a great support team’s workflow: drafting the text of short chat snippets for customer issues that have been observed in the past.

portal-deflection-onTicket “deflection” tools take a different approach by attempting to divert customers away from actually speaking with support, generally by sending them impersonal prefabricated responses or links to knowledge base articles.  These systems’ shortcomings are immediately clear even from their name: their goal is not to create great customer experiences but rather to reduce the number of customer contacts by deflecting customers away from conversations with support, without actually driving fixes to the root causes of those contacts.  Additionally, this approach is only feasible for some types of customer issues and frequently results in frustrating customer experiences.

The Idiomatic View

We believe that it is time to actually deliver on the promise of AI for support: consistently amazing customer experiences, at lower cost.  So, we are building a novel AI system that is designed from the ground up to partner with support teams and empower them to be much smarter about how they accomplish both of those things.

We believe that AI systems for support should not result in poor customer experiences in order to reduce costs, nor should they avoid solving the hard problems inherent in fundamentally improving the customer experience and customer support teams’ workflows.  We are building Idiomatic’s AI system accordingly.

In order to accomplish all of this, an AI system for support must have a number of key characteristics (some of which have been similarly recognized in recent efforts in other domains to create AI systems that operate in the wild in close contact with humans).  Our system, in addition to satisfying standard infrastructural requirements such as high availability, is designed to be

  • Customer-centric: As discussed above, many existing AI systems for support produce poor customer experiences, often focusing on optimizing only low-level statistical metrics, such as prediction accuracy, for standard machine learning tasks.  However, an AI system for support should focus on the mission of support: consistently creating amazing customer experiences at or beyond the level of a veteran human support agent, while also reducing costs.
  • Workflow-aware: An AI system for support should meaningfully address the core tasks of a great support team and integrate seamlessly with its workflow.  Those tasks include routing tickets to the right agents, diagnosing customer issues, actually resolving those issues rapidly and consistently, responding to customers empathetically and delightfully, monitoring performance on all of the above, creating and deploying self-service resources such as knowledge base articles, and driving fixes for the problems that cause customers to contact support in the first place.
  • Easy-to-use: Support teams should not need to go to great lengths to clean, organize, or label data in order to use an AI system, nor should they require a team of engineers or data scientists.  An AI system for support should function well, and continue to function well, out of the box.
  • Robust: Customer issues are varied and complex, and new issues continually arise, particularly as products change and new features are released.  An AI system for support should provide consistently great customer experiences even as it encounters this variability and complexity.
  • Transparent: An AI system for support should be able to explain itself and its actions.  For example, such a system should be able to tell you how many customers have complained about a given issue, or why it routed a particular ticket to a particular agent.  This capability ensures that valuable insights are not lost and that the system can be trusted and effectively controlled.
  • Controllable: Much as a support leader can rapidly change the way in which their team handles different customer issues, so too should they be able to rapidly and reliably change the way in which their AI system does so.

In developing the AI techniques and technology needed to deliver on all of the above, we’ve designed our system to include humans in the loop as a key ingredient.  The hybrid intelligence that results from this partnership between humans and AI allows both to do what they do best, each enabling that which cannot be done by the other in isolation.  Machines can act rapidly and consistently, automate simpler repetitive tasks, and effortlessly assimilate (and optimize based on) large amounts of information and past experience.  Where they fall short, they rely on human supervisors to add knowledge of business context and an empathetic touch, as well as a robust ability to handle previously unseen or complex customer issues and language.  In turn, humans are freed to focus on more complex and valuable issues and tasks, aided by machines to the extent possible.  Machine intelligence and human intelligence and empathy each elevate and assist the other.

The first version of our system is already providing our clients with unprecedented continuous insight into what their customers are saying at scale, in a precise and human-understandable way; this insight is a powerful lever for improving support operations and fixing the root causes of customer contacts.  And, we’re hard at work on bringing AI-driven capabilities even more directly to bear throughout support teams’ workflows.

AI is coming to customer support, and we’re solving the hard problem of making it easy for companies to seize that opportunity.  Stay tuned for more dispatches from the journey!