Why Building Your Own Enterprise AI Chatbot Is a Bad Idea!
According to Gartner, “90% of all chatbot solution implementations failed in 2019.”
There are many factors behind this trend but one key aspect is the lack of data science expertise within reach of most organizations. The reality is most companies are not investing in AI solutions on the scale of Google or Facebook. It is of no surprise that the number of enterprises attempting to build their own chatbot platforms reduced to 10% in 2020 from 40% in 2019.
Despite the many failures, having a Conversational AI strategy is still top of mind of many business leaders. Why – because the fundamentals and pursuit of goals such as improving real time response times, reducing customer support costs, and delighting your employees and improved customer experience still hold true. In fact, there is now a sense of urgency, and if anything, it has accelerated the need to deploy and operationalize artificial intelligence enabled enterprise chatbots compared to a year back where most companies were satisfied with proof of concepts.
Before you decide whether to try a new vendor or build your own AI-Powered Chatbot, keep in mind these 5 key enterprise chatbot pitfalls:
- Rigid Scripted Bots with no ML/AI: Many 1st & 2nd generation chatbots fall into the category of glorified if/else script platforms. They lack modern Machine Learning and AI capabilities such as Natural Language Understanding (NLU) and Natural Language Processing (NLP) capabilities that can decipher user’s goals and intents without any manual intervention.
- Limited Conversational Capabilities: Most enterprise bots lack 3rd generation chatbot capabilities such as Context Awareness or User Profile Understanding. It is easy to trick the bot by changing your question midway and leaving the bot all confused. In order to gain acceptance from, it is essential to ensure your chatbot/virtual assistant can carry a multi-turn conservation without getting stuck in a loop.
- Cost Prohibitive: It takes a lot of AI commitment both in terms of time, money and resources to get a decent quality chatbot deployed in production. Take the example of Erica, Bank of America’s banking virtual assistant that had a development team of 100 in 2017 with an investment of $30M over 2 years.
- Black Box AI with no Automated Learning: Companies were so focused on getting the first chatbots successful that they did not plan for the long term. Chatbots cannot have a do-it-once and forget-it approach, You need to think about maintenance and fine-tuning the chatbot from Day 1 to keep up with changing user’s expectations and dynamic business system needs.
- No Clear Business Case: Many organizations embarked on a journey to offer a conversational interface without articulating or understanding what business problem they are going to solve. What is the business benefit and impact from automation? Simply adopting technology for the sake of it can actually be detrimental to the overall employee and customer service experience so upfront due diligence can save a lot of time and money and avoid chatbot failure.