Artificial Intelligence for Customer Experience: What Lies Beyond Chatbots

With all the buzz surrounding artificial intelligence (AI) lately, it has become something you need to have. After all, everyone else seems to have it and you don’t want to be left behind, right?

Well, I intend to shed some light on what AI really means, why it’s here and its different applications for improving customer experience, besides the traditional and well-known virtual agent (or chatbot, chatterbot, virtual assistant, etc.).

These days, everything I see or read about enhancing customer engagement through AI is about virtual agents, so I decided to write my own analysis on the topic and further examine what AI really means in our region.

Basics of AI: What’s It Really About?

As it is the case with so many other buzzwords in the contact center industry (e.g.: digital transformation and omnichannel), and in spite of its many virtues, AI is still poorly understood by most businesses.

Essentially, as the volume of data continues to grow exponentially, it has become way too much for humans to handle — there simply isn’t enough time or people to translate it into useful information, and, therefore, exploit the effective use of that data.

AI provides a way to overcome the limitations of human analysis, as well as providing capabilities to deliver a technology that, once trained, requires no human involvement in extracting meaning from large data sets.

It comprises machine learning, intelligent agents (IA), deep learning and natural language processing (NLP), which are all technologies that seek to emulate human cognitive capabilities.

Machine learning can learn a task without being explicitly programmed to perform that task. It uses models or algorithms to enable the recognition of data patterns in an application and make correlations between them.

Deep learning, on the other hand, such as DeepMind’s AlphaGo, and Tesla’s Autopilot, comprises several levels of machine learning that are used to parse up a problem. It is an approximation of a brain-like structure or neural network.

NLP applies pattern recognition technologies to understand either spoken or written human language, while IAs apply all of the aforementioned technologies to provide a human-like interface to customer-facing applications (e.g.: Apple Siri and Amazon Alexa).

Artificial Intelligence Application in Contact Centers

Even though survey data indicates a heavy commitment to deploy AI for customer experience across market verticals, contact center companies are hoping AI will be transformational and are making commitments to invest in it without really understanding what it is or what it can do. In fact, according to a recent Frost & Sullivan report, almost 29% of Latin American companies find it crucial to implement AI, while 35.6% find it very important to do so.

Even though the most common application of AI used by Latin American contact center companies has been the Intelligent Agent – also known as chatbots – there are many other possible customer or enterprise-facing applications of AI that can directly or indirectly benefit the customer experience.

Examples of this include predictive analytics, pattern recognition, voice assistance, cybersecurity, information and visual discovery and extraction, visual recognition, virtual assistance and robots.

Also, these applications can be used across a variety of different segments such as retail, healthcare, home automation, and CRM, all with the aim to provide a seamless customer engagement.

Real-World Use Cases

A great example in healthcare is from Microsoft. Grupo Oncoclínicas and Microsoft have joined forces in a project that will use AI to promote advances in cancer treatment in Brazil. In radiotherapy, the use of Microsoft AI will allow delineating structures of organs adjacent to the tumor or considered of risk much more quickly. Thus, the technology offers a series of information so that the specialist can establish a treatment plan that contemplates the design of the area to be irradiated, reducing hours of evaluation to just a few minutes.

AWS, on the other hand, provided visual recognition solutions to Francisco Marroquín University (UFM), from Guatemala. UFM innovates through artificial intelligence with new technologies, such as Amazon Polly, Amazon Lex and Amazon Rekognition, to manage students’ information and identity, as well as their grades and certifications on virtual platforms. UFM also uses the new solutions to transcript information to audiobooks.

Preparing for Artificial Intelligence Success

AI is not a standalone technology, and as such, it depends on a data infrastructure; in other words, it requires a clean, current data set. This means that, in order to extract the maximum value from an AI investment, a substantial amount of attention needs to be devoted to building a trusted data pool.

This attention and devotion is key, and involves investments in data preparation technology, as well as the big data infrastructure necessary to store and retrieve relevant data. As a result, data security becomes an even bigger responsibility for all contact center industry participants, thus making the development of strategic partnerships and investments to enable access to data, funds, information, domain expertise, and higher computing prowess crucial.

In conclusion, companies should focus on the fact that AI-enabled solutions are expected to be the most significant disruptors across business capabilities. The forward looking capabilities with prescriptive and predictive insights are likely to impact customer experience like no other technology ever has, so be prepared for it, and be a part of it.