Six Limitations of Conversational Artificial Intelligence

Conversational artificial intelligence refers to a set of technologies that allows machines to simulate conversations. These include chatbots, messaging apps, and speech-based assistants. Using these tools, businesses engage with their customers at scale—to quickly resolve their queries, help them select appropriate products and services, and improve overall customer experience.

Current market and projected growth

At present, the most common use of conversational artificial intelligence in businesses is in the form of chatbots, virtual assistants, and in social media platforms. Natural Language Processing (NLP) is a subfield of artificial intelligence, which enables machines to understand human language in the way it is written. Today, deep learning models have made it possible to translate a language, summarize text, and even analyse sentiments.

With businesses looking to deploy chatbots and virtual assistants to strengthen customer service, the market adoption of the technology is on the rise. According to Markets and Markets, the market for conversational AI is projected to grow at a CAGR of 21.9% from around $4.8 billion in 2020 to nearly $13.9 billion in 2025.

Limitations obstructing adoption at scale

There are several drawbacks in chatbots and other communication tools, which is causing obstruction to the adoption of conversational AI at scale. We list six such limitations, in no particular order, here:

• Basic assistance: Probably, the biggest drawback that conversational AI suffers is that it cannot handle complex queries or hold conversations with humans—it can handle only basic questions. This means chatbots, based on conversational AI, cannot help with customer retention.
• Lack of human context: Bots can respond to humans only to an extent they have been trained. If a customer query is beyond what the machine has been trained for, it cannot understand or respond, which can frustrate customers.
• High installation costs: Although bots can be available 24/7 to handle a large volume of queries simultaneously, deploying them is a costly proposition. This is because every bots needs to be programmed and trained individually, according to the business it must serve. Further, customer queries keep evolving with their expectations. Addressing these evolving queries requires additional programming and training, which increases the costs further.
• Decisioning, or the lack of it: Machines can neither take decisions nor help customers take decisions. The lack of the ability to discern between good and bad can have serious consequences. One of the classic examples is that of a bot that Microsoft built for Twitter. Within a day, based on the content received from the users, the bot became rogue and racist, as it could not decide what was good and bad.
• Repetition: Machines are trained to provide standard answers to customer queries. In a situation when a customer does not find the answer satisfactory and rephrases the question, the machine still provides the same answer. This is a give-away to the customers that they are in fact interacting with a machine and not a live human being. This can prove irritating and prevent customers from proceeding any further.
• No empathy: Machines lack emotions so they cannot empathize with a customer who may be feeling low, angry, or frustrated. Live, human customer agents can understand and relate to the sentiment of the customer. However, this is not the case with machines. Therefore, chatbots cannot establish a connection with the customers, which often goes a long way in business growth.

Preparing for the future

An understanding into the current limitations chatbots face have led programmers to become more mindful when programming chatbots. Keras, TensorFlow, and PyTorch are some of the advanced frameworks that are helping them develop better chatbots. A lot of R&D work is presently underway to make chatbots understand human behavior and respond accordingly.

The article has been written by Neetu Katyal, Content and Marketing Consultant

She can be reached on LinkedIn.