Major Challenges faced by the Chatbot industry
The chatbot industry was valued at about 17 billion USD in 2019 and is projected to reach over 100 billion USD by 2025. With about a probable 34% cumulative annual growth rate, the industry shows significant promise in automation. Besides, 65% of customers worldwide showed an inclination towards chat connectivity over customer call support.
Challenges faced In ChatBot Industry
- Chatbots have limited responses. Completely autonomous chatbots based on deep learning algorithms are yet to reach a scalable state.
- Chatbots require human input for addressing customer queries. Usually, only a specific set of solutions are available via chatbots. Any issues beyond those require human intervention.
- The limited database can also throw customers in a loop of repeating responses resulting in frustration.
- Chatbots running on conventional AI are incapable of addressing complex issues.
- Development and integration of advanced AI are cost and labor-intensive, which defeats part of the purpose.
Moreover, chatbots are not practical for all businesses. Some businesses are far too complex to integrate chatbots for serving customers feasibly.
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Businesses specializing in luxury items attract customers who expect personalized services for the premium they are paying. In such cases, chatbots give a bad rap to the associated brand.
Only 13% of the worldwide customers expect to buy an expensive item through chatbots. It shows a clear indication of the reluctance of luxury item consumers to use chatbots for receiving services.
These can also be cumbersome, as they have to zero in on probabilities based on customers’ chronological responses. Individuals requiring swift solutions are not keen on such services. Besides, 37% of customers worldwide expect chatbots to provide quick answers during emergencies.
Chatbots have proven successful in repetitive tasks rather than novel ones. With changing patterns of requests, the incapacity of chatbots is highlighted.
Modern consumers have a varying set of requirements, which cannot be addressed with specific types of requests that are chatbot friendly.
Chatbots require constant maintenance. As a company grows, the range of interactions expected by associated stakeholders also changes. Hence, setting up a chatbot and forgetting about it is out of the question.
Chatbots need to evolve with the associated company, requiring constant monitoring, integration with updated databases, and accumulated information for tailored and insightful responses. The lack of feelings and emotions in these instant chatbots also inhibits them from addressing consumer distress with expected empathy.
Data security is a concern when it comes to chatbots. Securing the channel for transmitting sensitive information from chatbots to associated departments is often a challenge. The potential risk of a data breach can have cautious individuals favor human assistance over chatbots.
Chatbots for learning purposes have several vulnerabilities. They can be tricked and bypassed to advance in associated learning programs. Language learning applications utilize chatbots for receiving input and providing feedback on a learner’s progress.
As the human element is missing, computer-generated responses that are more accurate than a new learner’s responses can trick these chatbots.
Chatbots for education have yet to prove their feasibility as linear programs are incapable of addressing academic issues arising from various contexts. The mere exchange of information is not adequate for a sufficient educational experience. It is mainly attributed to “decision trees” rather than “advanced AI” in chatbots for education.
Chatbots were supposed to replace human beings as a mode of delivering services. However, the lack of human element has neutralized the conditions. Human agents need to have digital fluency and the capability to work alongside chatbots to stay relevant.
On the other hand, chatbot success stories are rare. There are very few cases where chatbots could revolutionize a process or bring about practical solutions to real problems.
Existing stereotypes inhibit the potential uses of chatbots. They are considered to apply to customer service sectors only.
Conventional AI-based chatbots are being used across financial institutions and banks to deal with many repeatable requests regularly. Even though they can have significant education and healthcare applications, their untapped potential is yet to be explored.
Besides, healthcare and education sectors are sensitive and far too important to leave it at the hands of chatbots unless they can replicate human responses, which brings us back to the concern for advanced AI.
Early chatbots often failed, as they were not able to process diverse responses. This gap has been minimized significantly, but the associated complexities have also increased.
Modern chatbots are highly specialized, but still operating within vocabulary and documentation constraints. They are better suited for structured operations that function within limited information. Unstructured operations are too farfetched and, in most cases, inapplicable for chatbots.
Voice assistants are iterations of chatbots with a different set of variables. Natural language processing capabilities limit them. Unless these assistants are maintained by large corporations like google or amazon, having a sustainable financial backup, technical expertise, and access to state-of-the-art technology, they cannot reach full potential.
