Are Chatbots Nearing Their End?

There is no shortage of studies citing chatbots to be one of the technology’s most significant game-changers. It could be a good idea to stop and think again, though. Many companies had begun investing in chatbots to automate customer service. But chatbots have so far been constrained in their capacity, as the underlying AI and ML technologies have shown little improvement in their applications. Attitudes towards chatbots have remained mixed, with some artificial intelligence experts and businesses still betting that they will prove worthwhile eventually. But is the expectation justified? Let’s find out!


Blog Contents

  • Introduction to Chatbots
  • Chatbots – How Do They Work?
  • Chatbot Evolution: The Speculation vs. The Collapse
  • Generations of Chatbot
  • Few Chatbot Problems
  • Future Prospects
  • Conclusion


Let’s talk more about how chatbots have evolved and whether they are still a prospect or a failure. 


Introduction to Chatbots

A bot is a program that acts automatically, based on particular inputs and algorithms. A chatbot is a computer program designed to simulate human speaking. Users use a chat interface or voice to connect with these resources, just like they would converse with another person. Chatbots interpret the words a person gives them and provide a pre-set response.


Chatbots – How Do They Work?

There are three basic methods of classification used for operating a chatbot.


  1. To build a bot that matches the patterns. Matching pattern bots identify text and create an answer based on the keywords they see. A basic framework for these patterns is AIML (Markup Language for Artificial Intelligence). In pattern-matching, the chatbot knows only the answers to questions in their templates. The bot can’t go any further than the patterns already implemented in its system.
  2. For today’s chatbots, another choice is to use algorithms. For each type of query, the bot must have a specific database pattern to provide the correct answer. A hierarchical structure can be generated with different combinations of trends. Algorithms are how AI developers are growing the classifiers and making the system more useful.
  3. Using artificial neural networks is the ultimate essential technique for chatbots. These are solutions that provide the bots to calculate the answer to a question using weighted links and data background. Sentences given to a bot are broken down into different words with artificial neural networks, and each word is used as an input to the neural network. The neural network gets more potent and more developed over time, allowing the bot to construct a more detailed set of answers to common queries.


Chatbot Evolution: The Speculation vs. The Collapse  

Around ten years ago, first-generation chatbots appeared but struggled to live up to their around-hyped standards, with some being flat-out disasters. Behind those early failures, there were numerous causes. Workflow automation as part of early chatbot implementation was best limited, as the emphasis was on reducing human interaction and related costs.

However, the most significant explanation for these early failures was the NLP technology was inexperienced to the point where many bots had to hand over quickly to a person who could understand and address their question, defying the promise of reduced manual interference.


Generations of Chatbot

The primary use cases for bots in this first step of evolution were primarily driven by customer service and not by other market areas. Most were introduced with inadequate skills or abilities to do more than answer simple customer service questions, and consumers were sometimes left more irritated than ever before. On both the customer experience and the company result lines, First-gen bots failed conventionally.

Around 2015 the second generation of chatbots appeared, providing more realistic and multilingual assistance combined with automating simple process tasks. But these ventures were not without substantial time and capital commitment. For example, Erica chatbot from Bank of America cost an estimated $30 million, and it took two years to build with a 100-person Certified Artificial Intelligence Experts team. Although this is not a traditional project, large-scale enterprise chatbot implementations are also not unheard of.


Few Chatbot Problems

Even though chatbots are becoming more useful and intuitive today, there’s still a long way to go until we’ve mastered these tools. With this tech, however, we still face a few key challenges. 

  • Security: In today’s age of data sensitivity and privacy, it is essential that customers can also trust the bots they give their details. Businesses would have to build chatbots so they can only request and collect relevant data. It will also be essential to ensure that the data collected are transmitted safely and secured over the Internet.
  • Increasing the Accuracy of Bot: Another way of influencing a bot is when the degree of accuracy needs to be increased. This may mean adding more variations of the intents, i.e., the greater the bot’s precision around specific capabilities, the less room for other capabilities.
  • Bot Maintenance Problem: A chatbot is not a one-size-fits-all project and can not be left to its own devices. Chatbots must be maintained as user experiences expand with it, eventually producing additional utterances which the bot does not know, i.e., So-called skipped utterances the bot needs to learn how to deal with.


Future Prospects

Designing the solutions with a multi-bot architecture in mind would be a relevant way of tackling single bot problems is While a single bot model works for many initial and less complicated usage cases, the way to resolve the problems mentioned above is to think of bots as having different skills, whereby combining several bots (or abilities) would satisfy the usage case needs without reaching the NLP limitations breakpoint. Artificial Intelligence Training would play a crucial role in enhancing the chatbot utility.



Chatbots can be a valuable addition to every company or contact center’s customer service strategy in today’s highly digital era. Like any other disruptive technology, however, businesses will need to make sure they know how to use these bots efficiently if they get the most out of their available technologies. Consider an Artificial Intelligence Certificate course to kick start your chatbot optimization. Chatbots are now being built with greater conversational and automation capabilities that can offer improved customer engagement and results. Don’t let current NLP limitations hamper your bot voyage.