A Guide to Transforming Your Enterprise with Conversational AI
AI Chatbots are conducting complex interactions across platforms. Learn why they’re a primary driver of global business growth.
By Gustavo Parés
Today, the most evident use of AI applications in the private sector involves companies using Conversational AI technologies, often simply called “chatbots”. Chatbots are quickly becoming one of the primary ways in which businesses can embrace digital transformation and new growth opportunities.
For those unfamiliar with the technology, but curious about understanding how it can benefit their business, it’s important to understand how enterprises can design and implement Conversational AI.
Understanding and Preparing Your Data
The first step towards implementing a chatbot solution for your organization is knowing the type of data you have on hand, and what forms of communications are most effective for your organization. Interaction data can come in the form of text messages, contact center audio call recordings, social media messaging platforms, and emails, among others. This data can reside on Excel, CVS, JSON files, and even within SQL databases.
Regardless of the origin and format of the data, it must be processed in order to eliminate elements that are useless and do not have any value, such as incomplete elements and special words.
As part of this cleaning process, the dataset must be normalized through specific techniques such as tokenization and lemmatization, using scripts developed in Python, using Jupyter Notebooks, and different libraries such as NumPy, Pandas and NLTK. The library best suited for your business depends on the type of inquiries the chatbot is likely to receive.
Once the data is cleaned, it must be transformed and mined for insights. Using programs such as Word2Vec, your Conversational AI provider will find the relationships between relevant words and phrases in order to feed them into the natural language processor (NLP), which is at the heart of chatbot technology. Using a Latent Dirichlet Allocation, the relationship between subjects and topics are identified, detecting correlations between words in different contexts. For example, the word “head” in banking could refer to being the head of a household, whereas in retail, it might be found in inquiries of individuals searching for hats.
Training Your Conversational AI
Chatbots differ significantly across industries. Retail chatbots, such as those used for online shopping, are among the most complex applications due to the number of and variance (e.g., sizes & colors) of products available, as well as other functions such as product inquiries, returns, and complaints. Industries such as manufacturing are often more simple to implement, as many of the processes and terms are standardized across the industry. Other complex factors to consider when developing your chatbot are your end-user demographics and regional language differences.
Implementing Your Chatbot
Conversational AI has an orchestrator in charge of communicating and coordinating activities between the different components of the chatbot solution, such as session management, backend system integration, sentiment analysis, and more. The orchestrator can be developed in Net Core 5, using C# as the programming language. Some of the benefits of using these technologies include the possibility of using a multi-platform solution that can be deployed in containers or virtual machines with a very low footprint, and using a multi-stage Docker compilation to decrease the size of the containers.
On the backend, the solution is based on a microservices architecture and can be deployed in Kubernetes clusters, transforming it into a cloud-agnostic solution that can be deployed in any service, such as IBM Kubernetes Service, RedHat OpenShift, Google Kubernetes Engine, AWS Kubernetes Services, Rancher Kubernetes Engine, and many others.
Kubernetes in particular provides benefits to portability, the ability to utilize horizontal auto-scaling of each component on its own, increased failure tolerance, and higher availability of the solution that allows for any number of users at the same time. Deployed in an elastic infrastructure that increases or decreases depending on the demand, the solution only consumes the necessary resources, which translates into significant savings.
All of the information gathered through your chatbot enables a continuous improvement cycle that sees a Conversational AI solution learning from datasets generated over the course of its deployment. It allows for the solution to improve its own accuracy through time and usage, while also incrementing the quantity and quality of questions or topics with available responses. Essentially, this form of continuous monitoring and retraining allows the technology to grow along with your organization.
A Path Forward With Conversational AI
Virtually every industry sector has had to rapidly adapt new methods and technologies in order to maintain their operations during the COVID-19 pandemic. For some, the accelerated pace of digital transformation has been a wake-up call. In competitive markets, organizations that do not look to modernize risk lag behind, as their peers gain a competitive edge.
Developing and implementing a chatbot is a complex process, but the value they can deliver to your organization is tremendous. With the ability to augment human capabilities and to automate routine tasks and inquiries, chatbots can unlock new-found efficiencies and revenue for your organization.
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