Is data the only barrier to developing chatbots? Check out the pitfalls to avoid
There is no denying that adoption of chatbots is at all time high and voice-activated chatbots are laying the foundation of a more focused customer experience. But while chatbots have become an essential toolset in every company’s technology stack and delivers great value to customers, there are several barriers to developing chatbots. While AI’s future is in the voice and companies are cashing in on providing a seamless conversational experience through messaging platforms, chatbots have to evolve to provide a strong user experience and become the voice of the brand. On the technological front, enterprises face several challenges, from too little to petabytes of data, user language, functionality and security and integration issues among others.  Monitoring chatbots and keeping a track of responses to queries is also a prime concern for companies and greatly hinders successful adoption. The bottomline is to make conversational voice and text based assistants more perceptive. Analytics India Magazine lists down key challenges in developing chatbots Data: Most companies are sitting on a wealth of data – structured and unstructured and struggle to train all the data. When developing chatbots, the reigning sentiment is that either the data out there is too much or too little. The rule of thumb is that more users means more data and that’s why most chatbot developers try to populate the database with entries from real people and sometimes this can be a capital intensive task. The whole idea is that more data can lead to better conversation paths in customer automation. For a conversational chatbot, you may need to create broad events and entities that can be an intensive task and the data out there has to be processed and tagged. Facebook’s Wit.ai platform and Google’s API.AI provide developers sample default questions to create common intents for users. NLP: Natural Language Processing is the driving force behind chatbots and it’s the computational approach to find answers by parsing user’s inputted text into entities, intents, actions and contexts. Now, the need of the hour is a Natural Language learning system that can expand the chatbot’s syntax and grammar by recommending new phrases. With all the noise around AI in the last few years, developers are now pushing the boundaries by leveraging deep learning to program chatbots to be more human-like. However, the tech around NLP and most importantly deep learning is in the early stages and it will take some more time before the CX (customer experience) and engagement becomes more intuitive and robust. From the automation context, chatbots sit high on the value chain, by driving customer interaction across domains such as ecommerce, healthcare, retail and more. If businesses don’t want to be left behind in the automation wave, they must step up their investment in machine learning infrastructure, scale data collection process and iterate their algorithms and improve performance over time. Security: Most businesses are reluctant to use bots since it can’t be monitored all the time and there is the inability to guarantee the security of data. And most of the security concerns around bots arise in the financial sector while securing the transmission of client data. Although, chatbots follow two-factor authentication and boast of end-to-end encryption, security issues continue to plague bots. Let’s not forget the creepy Facebook AI story that the bots invented a new language for more interaction. Testing: Testing chatbots can be a very teething issue and it is difficult to test the correct behavior of chatbot. While human QA may not be entirely possible and some testing will have to be automated, businesses should develop a framework  to validate the functionality of chatbots. Making a difference in a crowded market: According to the findings from Juniper Research a chatbot enquiry can save upto 4+ minutes of valuable customer service time and can save businesses $8 billion annually by 2022, up from $20 million in 2017. Another survey by Oracle indicates that 80% of businesses want chatbot by 2020 and chatbot is the future of conversational commerce. While the statistics may be resounding, chatbots have become a tough territory to beat and issues such as integration, language understanding and building NLP engines have to be factored in before jumping on the chatbot bandwagon. Outlook Increasingly, businesses are latching on where customers are – on popular messaging platforms such as Facebook Messenger, Slack, Skype, HipChat and WhatsApp and integrating marketing services on these platforms. And while chatbots are a great way to build brands and they present an easy way for companies to scale mobile messaging with users, businesses should have a clear understanding of the service and customer experience they want to build. And as NLP capabilities get more sophisticated, digital assistants can usher in big gains and enable frictionless communication. With more tech behemoths launching their platforms, the entry barrier has also been lowered. Another big player that is lowering the entry barrier to AI is Amazon Lex by AWS that does all the NLP and machine learning heavy lifting for customers, helping them introduce a chatbot easily and effectively. It’s time to take advantage of the conversational commerce fueled by chatbots.
There is no denying that adoption of chatbots is at all time high and voice-activated chatbots are laying the foundation of a more focused customer experience. But while chatbots have become an essential toolset in every company’s technology stack and delivers great value to customers, there are several barriers to developing chatbots. While AI’s future […]
chatbots have become an essential toolset in every company’s technology stack and delivers great value to customers, there are several barriers to developing chatbots. W
["chatbot ai"]
["machine learning", "AWS", "AI", "chatbots", "ML", "RAG", "NLP", "deep learning", "chatbot ai", "analytics", "R"]
["AI", "machine learning", "ML", "deep learning", "NLP", "analytics", "RAG", "chatbots", "AWS", "R"]
Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.