What's Holding Back NLP In The Enterprise?
Dan Turchin is the Chief Executive Officer of PeopleReign, the system of intelligence for IT and HR employee service. getty  A toddler learns language by seeing and doing. At peak learning age, 3-year-olds learn about 400 words a month. For example, hand a toddler a cup of water and pronounce the word "cup." Most toddlers immediately understand properties about it that are phenomenally complex. Cups have weight, volume and hold fluid. They spill when turned over. They're lighter when empty. Physics majors spend years learning the math behind physical principles a toddler learns instantly through observation and practice. Artificial intelligence doesn't observe the world like a toddler. "Intelligence" is a misnomer when teaching "skills" to machines. They learn by codifying patterns in data that can often be inconsistent or incomplete. They make recommendations and predictions using algorithms that are vulnerable to biased data. They don't introspect or experiment like toddlers. As a result, most enterprise AI projects fail. Solving Business Problems With Technology  At work, we often expect AI to learn like toddlers. As a result, we overpromise what it can do. If, instead, we define projects based on business objectives, we can spend less time enamored by technology and more time helping improve employee productivity. When deploying enterprise virtual agents, for example, commit to improving at least one key performance indicator (KPI) associated with service quality. Here are the big five KPIs for employee service:  1, Reduce mean time to resolve (MTTR) issues. 2. Reduce cost per issue. 3. Reduce call volume. 4. Improve customer satisfaction. 5. Improve self-service adoption ("shift left" strategies). The One Mistake To Avoid  When using natural language processing (NLP) to improve employee service, don't solve the language problem. Solve the understanding problem. Focus exclusively on what's required to identify the best action to take in order to answer the question or fulfill the request in as little time as possible with as little friction as possible. In the context of employee service, achieving that with NLP is a two-step exercise:  1. Detect the user's intent. 2. Take appropriate action. Detect User Intent  Detecting the user's intent from natural language can be achieved with a high level of accuracy in narrow domains. For example, knowing the user will be requesting IT service makes it significantly easier to detect the intent of an utterance like: "My network is failing." Without knowing the request applies to IT, it could be about health benefits reimbursement or social media posts. With domain awareness, the request is obviously related to connectivity, configuration or access. To improve domain awareness, add domain ontologies and use metadata to infer context. 1. Add domain ontologies. Domain ontologies are structured vocabularies that identify relationships between concepts  often expressed in the form of hyponyms (specific examples of a broader group), hypernyms (broader groups that include specific items) and synonyms (topics with similar meaning). Enriching utterances with concepts and relationships from domain ontologies can significantly boost intent detection accuracy. 2. Use metadata to infer context. Using metadata can remove the need to use language tricks to know an utterance is related to IT or HR. For example, the URL of the request, the page title or the value of a user-selected field should be enough to determine the domain. Once the domain has been identified with certainty, the intent detection exercise (especially using domain ontologies) can proceed in a way that seems intelligent without unnecessary user interaction. Take Appropriate Action  Most enterprises mistakenly attempt to solve the language problem. Common indications of that include:   Making the virtual agent seem human. Don't anthropomorphize the bot. Focus on user experience and call deflection. Bots that are perceived as more "human-like" are expected to behave like humans and fail by comparison.  Not recognizing known unknowns. When confidence in detected intent is low, ask for clarification instead of responding with the wrong answer (a false positive) or not responding at all (a false negative). Often, asking the user to rephrase the request turns unknown unknowns into known unknowns  or better yet, known knowns.  Prioritizing "small talk" above "big talk." In NLP, "small talk" refers to greetings and closings  dialog that is ancillary to "big talk," which is the actual purpose of the user's interaction. Using NLP tricks to identify user gender, time of day or other unrelated attributes are unnecessary at best and detrimental at worst. Instead, focus on "big talk" accuracy and action-taking to deliver the best user experience. For example, a customer in the retail apparel business insisted that the virtual agent have a human-like avatar and greet users with a comment about the weather before asking about the user's problem. It would then guess whether the user was requesting IT, HR or finance-related service. Not surprisingly, satisfaction scores were poor, abandonment rates were high and adoption spiked initially before trailing off within a month. All three metrics improved dramatically once the human avatar was replaced with the company logo, the superfluous small talk was replaced by "how can I help," and the user was asked to select from a list of issue types: IT, HR, finance or other. Ensure The Success Of Your AI Project  When configuring your virtual agent, consider the shortest path to achieve the only metric that matters: autonomous call deflection. Consider the impact on productivity if even a third of all emails and calls could be autonomously resolved. For a typical organization, the benefits are immediate: user wait times drop precipitously and cost per issue is reduced by 80% or more. NLP as a technology is ubiquitous in our personal lives. Google uses it to deliver answers versus search results, Amazon uses it to navigate more than 80,000 active Alexa skills, and Facebook uses it to serve ads and decide which posts to share. In enterprise contexts, however, it's often used to solve the wrong problem. Use it judiciously. To achieve the benefits you expect, configure your virtual agent to solve the understanding problem. Leave it to academics intent on passing the Turing test to solve the language problem. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.