Exploring the Biggest Issues With Enterprise Chatbot Development 

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Enterprise chatbots can be a godsend to small businesses that desperately need to automate repetitive tasks while maximizing customer satisfaction. The need for the latter is especially profound because 76% of customers immediately switch brands after one bad experience. Some of those bad experiences can be attributed to chatbots lacking any sort of rapport. Plus, not understanding the customers they interact with. 

Enterprise chatbot development focuses too much on data and statistics without applying strong general learning principles. Or ensuring they achieve dynamic context. A lot of enterprises rush development as well, motivated by competition. Thereby, only implementing chatbot solutions to mirror what other businesses are doing. Enterprise leaders see the revenue-generating and strategic benefits they provide by automating repetitive tasks. But they fail to factor in necessary elements like hyper-personalization, scaling, and more. 

There is a lack of repeatable and reliable models for intelligent chatbot development, and it all comes back to an overreliance on traditional development and implementation issues, leading to diminished customer experiences and maintenance headaches. Here are some of the most noticeable challenges with today’s enterprise chatbots. 

Too Much Emphasis on Testing Leads to Enterprise Chatbot Failure 

Fully functional and versatile enterprise chatbots leverage conversational AI and natural language to establish rapport with the people they interact with regularly. Chatbots can generate response rates of just under 90% when they generate highly-engaged customer experiences. Furthermore, enterprise chatbots improve productivity and workflow across verticals, ranging from help desks to IT and procurement. 

However, during the development stage, little time is spent on engagement as enterprise leaders and IT developers are married to stringent testing procedures, leading to malfunctions that negatively impact business performance. 

The evolution of chatbots has been increasing as natural language models develop, meaning that some amount of testing is required to maintain accuracy. However, chatbots fail when lots of training data and labeling are leveraged, thus ‘confusing’ the bots. The flawed thinking says that to change the ‘minds’ of chatbots, just add more data to help them understand more of the language. However, if the chatbot lacks a memory to begin with, then what is adding data going to achieve? 

Ontologies solve the testing problem because they allow chatbots to process specific meanings and synonyms. Thus they develop a contextual understanding from the beginning. Enterprise chatbots understand, test, and train service-specific properties. This enables businesses to accumulate business knowledge in one go rather than constantly drilling information into them. Or heavily scripting them so they can have meaningful conversations. 

Enterprise Chatbots Must Readily Address Declining Attention Spans

Another issue with enterprise chatbot development is that enterprise leaders and developers fail to realize that more people are demanding instant customer experiences. People increasingly adjusted to on-demand service, meaning increased response times and a greater desire for immediate replies. The average attention span for customers is eight seconds or less. They don’t have enough time to be stuck mingling with brands for longer than normal, especially for queries or tasks that should be quick enough to handle. 

As a result, the demand for chatbots with extraordinary functionalities has increased. Yet, there aren’t enough of those chatbots within the marketplace because too many chatbots lack the necessary personalization and comprehension capabilities to match increased customer demand for seamless, instant service. Many chatbots struggle to learn interactively, have no reasoning ability, and have shallow, statistics-based comprehension. These prevent them from understanding context. 

Enterprise chatbot development needs the best practices of conversation AI to mimic human conversations, leading to optimized customer experiences and management. Chatbots need deep contextual parsing to develop high comprehension levels, as well as dynamic personalization, ditching the traditional hard-coded or fixed personalization methods. As a result, chatbots develop a strong, sharp memory that allows them to adapt to situations flawlessly. 

With personalization and contextualization becoming more dynamic, enterprise chatbots can match today’s way of interaction. It is more social than technological. Enterprise chatbots should be more dynamic, creative, emotional, and natural. This matches customer expectations because their way of interaction centers around these important traits. They should be able to replicate human context and understanding to provide solutions. Solutions that not only cater to their needs but allow businesses to focus more time on mission-critical tasks. 

Providing More Context to Enterprise Chatbots 

Without having the context to make meaningful and helpful responses to help the people who interact with them, conversations become vague. The bots will have no use. Creating chatbots with contextualization in mind can prove challenging for enterprises that don’t have the knowledge base or infrastructure required to utilize them. 

Thankfully, with natural language processing (NLP), enterprise chatbots can understand conversations in great depth. Plus, they comprehend data and convert it to offer meaningful responses in real-time, meeting customer needs consistently. 

Addressing Challenges Will Lead to Increased Enterprise Adoption 

Even before the pandemic started, enterprise adoption of chatbots and conversational AI had been gradually increasing. However, such adoption will need to accelerate as the transformation to a digital workplace continues. Technological advancements built to further automate customer interactions will become more commonplace. This will necessitate that chatbots deliver hyper-personalized customer experiences at scale. 

Improving the quality of chatbots and how they extract/deliver value during customer conversations streamlines workflows. Plus it promotes interactive unsupervised learning. This means that they potentially garner key insights from conversations without someone constantly watching over them. With integrated short and long-term memory, plus hyper-personalization being based on individual goals, objectives, and history, enterprise chatbots can deliver exceptional experiences. 

They can deflect calls for call centers, scale supply-related activities, handle procurement requirements and spearhead IT helpdesks. Various fields such as fintech and ERP are enhanced as well. The use cases for enterprise chatbots are wide-ranging and more promising once the development aspect focuses more on making chatbots relatable without relying on the overuse of data to make them fully functional. 

Enterprises can become robust industry leaders with the help of conversational AI tools that position them strongly in the minds of the customers they work with. By successfully navigating chatbot development challenges, enterprise leaders can achieve bigger business goals. 

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Srini Pagidyala is a seasoned digital transformation entrepreneur with over twenty years of experience in technology entrepreneurship. In 2017, he Co-Founded, a new category “chatbot with a brain” that delivers hyper personalized conversational experiences.