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A Practical Guide to Ai in the Contact Center

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A Practical Guide to Ai in the Contact Center A PRACTICAL GUIDE TO AI IN THE CONTACT CENTER making your Business Brilliant Introduction Are you interested in how articial intelligence (AI) might impact your contact center? The hype cycle for AI is nearing its peak. But before you rush to deploy an AI tool, let’s separate fact from ction. What are the practical benets of AI today? What kind of chal- lenges arise from automation? What are the underlying technologies at play? In this e-book, we will answer these questions and more. We will examine AI from a prag- matic lens and offer suggestions to minimize costs and maximize returns. A Practical Guide to AI in the Contact Center 2 What is AI? People tend to generalize their discussions of AI with all the underlying technologies. The articial intelligence denition is open to interpretation. After all, there are several levels of “intelligent.” For EXAMPLES OF AI the purpose of business, we typically consider technologies that simulate or supplant human action as PLATFORMS: “AI.” • Microsoft Azure The ambiguity of AI makes it a difcult concept to invest in. The idea of advanced technology depos- Machine Learning ing human effort has overt appeal. However, without a directive, AI is wasted intelligence. • Google Cloud Machine Learning Engine • Amazon Lex WAVENET TIP: • Infosys Mana If you want to use AI successfully, ask yourself, “What problem am I trying to solve?” • WiPro Holmes • Rainbird • Ayasdi Do you want a chatbot that can take and place If you have a clear understanding of the problem • Vital A.I. orders in lieu of call center representatives? Do you are trying to solve, AI may benet your • Meya you want a program that analyzes customer data company. AI is not a cure-all and applying it • MindMeld and serves personalized marketing materials? Do broadly could backre. Just like training a new you want an IVR that can understand natural employee, AI has a learning curve. It is only as language and route customers to appropriate good as it’s inputs and parameters. With a xed agents? goal, AI can create efcient and actionable results. Run-amuck it could sabotage preexisting Out of the box, many AI platforms are tabula processes. rasa (blank slates). They have the capacity for deep analytics, machine learning and automated function, but require initial instruction. It’s not all plug-and-play function. A Practical Guide to AI in the Contact Center 3 Today, many people are simply looking for a response. AI models that use voice recognition rely autonomous administration. They want to remove on trigger phrases and words for their understand- all of the grunt work from their ofce so they can ing. shift focus to more strategic and protable endeav- ors. This is possible through a number of technolo- When you ask Alexa or Siri what the weather is like, gies – not all classied as overt AI. you are engaging voice recognition and an NLP system. Alexa or Siri has AI that can decipher Let’s look at some of the underlying technology and language and derive command functions from syntax potential benefits: and general semantics. Applied to the contact center, this type of digital signal processing (DSP) helps Natural Language Processing (NLP) create advanced IVRs, voice transcriptions and some Understanding language is a huge part of the AI forms of advanced case management. equation. Language is complex and variable. Com- puters become more powerful and user friendly as Machine Learning they ingest language formulas and convert words to Machine learning is a foundational component of AI. command functions. Whether through audible inputs The concept of machine learning is to have a base set or chat, the more accurate the NLP, the better the AI of protocols that kick off a particular action. Then, can interpret inputs and match appropriate respons- have those protocols learn additional rules from es. responses to ascribed actions. We tend to think of machine learning as the scary doomsday element of Voice Recognition AI – robots taking on more autonomous action than When a command starts as voice, AI must turn inec- originally programmed. But actually, companies tions and tones into text script. AI then interprets today are applying machine learning with narrow that text script and delivers parameters, only allowing AI to learn specic tasks without broad variability. A Practical Guide to AI in the Contact Center 4 For example, we may have an AI system look at an Chatbots may also serve as a routing tool, rst used individual’s shopping behaviors to determine which to decipher a customer’s need and then pushing that advertisement to serve up next. The choice is A, B or context to a live agent. C. The AI looks at all of the historical interactions of the individual customer and customers of a similar Cloud prole to serve up a choice most likely to convert. Cloud creates synergy. Its main function is to relieve The AI cannot allocate a choice D; it is intelligent the strain of on premises IT infrastructure and shift within its parameters. resources to a more centralized system. Cloud allows companies to appropriate processing and data stor- Chatbots age power at scale. Companies can add more com- Chatbots are an increasingly popular use case for an puter power to serve advanced program functions, NLP and machine learning combination. You may and scale down if they need to conserve their IT have seen news of Facebook chat sessions led by fully spend. It’s a much more economical model than automated service chatbots, but the idea of autono- buying a bunch of computers that may or may not mous communication is nothing new. Similar to voice accommodate needs. Cloud is the reason many com- recognition, chatbots follow key words and phrases panies can deploy AI today. Rather than pay for their to assume an appropriate response. Often companies own super computer, companies can lease AI through limit chatbot responses to a list of potential com- the cloud network. That, or they can build their own mands. If a query does not meet answer criterion, the AI that scales according to demand rather than con- chatbot may push the chat to a live agent. Chatbots stantly monopolizing computational bandwidth. today are mostly used as FAQs or as an additional form of call deection. A Practical Guide to AI in the Contact Center 5 An Abbreviated History of AI It is important to understand several underlying technologies that converged into what we know as modern AI. Some of these technologies share an origin and several exist in a similar timeline. These technologies are components of AI – sometimes referenced as AI – but do not exclusively dene AI. The cornerstone of AI arguably dates back to Eventually, companies were able to produce the 1950, when Alan Turing published “Com- computers that could problem solve with puting Machinery and Intelligence,” which astounding speed. Computers applied mathe- proposed criterion for intelligence later matical equations to seemingly nonmathe- called the Turing Test. In his test, Turing matical scenarios and made calculated, logi- challenged that true articial intelligence cal decisions that rivaled or surpassed those could only exist if a computer could consis- of humans. tently fool a human into believing it is also human. In 1997, IBM showed how powerful machine learning could be when its supercomputer In 1957, on the heels of Turing, Frank Rosen- Deep Blue beat the world champion at chess. blatt showcased Perceptron, an algorithm for Then, 14 years later, its descendant IBM supervised learning in computers, a founda- Watson defeated all its human competitors tional brain for potential AI. This was the on Jeopardy. basis of machine learning. The leap from science ction to actual science Over the years, countless inventors and inno- appeared complete. vators rened computers to process more and more information. Generation after genera- tion layered on new intelligence and pro- grams running complex algorithms, improv- ing the “brain” of computers. A Practical Guide to AI in the Contact Center 6 In the early 2000s, computer manufacturers had could outsource their servers, and harness the managed to signicantly reduce the size of power of scalable processing, they could greatly computer hardware. However, companies run- improve their infrastructure. ning thousands of computers – requiring signi- cant computational power – still required rooms In 2002, Amazon Web Services (AWS) of dedicated servers to facilitate the massive announced a full suite of cloud-based services number of employee users operating on the including storage, computation and advanced network. intelligence. Then, in 2006, AWS expanded the service to the commercial web with its Elastic Companies needed places they could store les, Compute Cloud (EC2). This solution helped small processing power for applications and ample companies and individuals rent data centers to network bandwidth to accommodate day-long run their own computer applications. use. Server rooms required maintenance, con- stant security, proper provisioning of power and The AWS concept sparked an explosion of inter- regular updates to remain modern and accom- est in cloud technology and competing cloud modating for new software. companies spun up data centers all over the globe. The dynamic nature of cloud meant that, Computers were a resource, but also a burden. regardless of an individual’s hardware/comput- And employees couldn’t take their powerful er, customers could access limitless computa- work tools home. They needed private desktops tional power and data storage of multiple to accommodate their needs at-home. servers for a monthly fee. For businesses, this meant they could run complex applications In 1999, Salesforce pioneered the concept of without having to purchase, warehouse, and delivering enterprise applications over the web.
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