Why does AI Chatbot fail to deliver and how to make it right
While AI Chatbot is one of the trending technologies most businesses are taking up, the complexities are not addressed in depth. Many chatbots fail due to the lack of awareness of their limitations.
Why exactly are AI Chatbots failing? We have put them into a few main reasons here:
Doing Too Much
Overly ambitious AI Chatbot tend to fail in simple tasks simply because there are potentially many similar-but-different intents going around inside.
Either that or AI Chatbot be trying too hard by so much that users do not know what to do with it. Some may remember M, Facebook Messenger which was designed to automatically complete tasks for users back in 2017. It was quickly discontinued and taken as an initiative to ‘learn more about its users’. When the role of an AI Chatbot is not clearly defined, it is set to fail in the long run.
Training Effort
One of the top misconceptions of AI Chatbot technology is that machine learning can magically work on its own without human intervention. This is not true.
An AI Chatbot is only as intelligent as what we contribute to its ‘brain’. From collecting data to building contexts and relationships in dialogues, the technology requires us to spend time to make it work the way it is intended.
Understanding Conversation Dynamics
B2C serves an ultra-wide range of customers and AI Chatbot simply cannot understand the great variety of consumer behaviour, more so when we are only relying on words to communicate. Although NLP is here to save the day, the technology has not yet matured to the quality that we are expecting it to be. When dealing with different languages, most AI chatbot requires a massive effort to address the inconsistencies.
Underestimating Complexities
Many resources available on the internet do not emphasise enough the potential complexity in building a good AI Chatbot. This leaves many businesses jumping on the bandwagon only to realise it takes much effort to build a smart AI Chatbot. The good news is, unnecessary challenges can be avoided if you spend time to find the right tool and test them out to make sure it suits your long-term businesses needs.
So the million-dollar question is, “How do we build a smart AI Chatbot?”
There is no direct route to success but we think these steps are key to determining the success of your AI Chatbot.
Define Roles and Responsibilities
Why are the problems you are trying to solve? How can AI Chatbot take away these challenges? Identifying key challenges and opportunities help to map the plan to achieve the result that you want. At XIMNET, we recommend an AI Chatbot to focus on three key areas and do them well within the limitations.
In this process, businesses should also move beyond achieving short-term goals and set goals at a strategic level in mind. Once these are clearly mapped out, it is time to break them down into smaller initiatives and roll them out progressively to observe the successes (and failures) of your AI Chatbot before moving to the next initiative. This development framework is also known as agile — an iterative approach to project management and software development.
Spend Time To Engage
The success of an AI Chatbot project depends on the knowledge it gathers across departments. CIO (Chief Innovation Officer) is often the person who brings the latest technologies to maximise business productivity — that includes leading how AI Chatbot is implemented in an organisation most of the time.
However, it does not limit to c-suite level engagement. Engaging executives who truly represents the voice of your customers is just as important in the process. Such engagement takes both the business and customer perspectives together to form a more well-informed solution.
Being dynamic and inclusive in the process of building the solutions together forms the sense of ownership in making the implementation a success for all.
Expect A Marathon
Celebrate when your AI Chatbot is finally launched but don’t stop there. Knowing that the journey has just begun prepares you to keep making it better as you get more feedback and insights from the implementation.
Setting measurable goals and tracking your AI Chatbot performance is key to continuous improvement. Seek opportunities to increase customer satisfaction even further.
These steps have helped us to set a solid foundation in our AI Chatbot projects with our clients and bring measurable long-term results such as an increase in sales conversion rate, higher customer satisfaction as well as creating an avenue for businesses to serve their customers effectively through the digital channels in this uncertain global pandemic period.
Here are some of the AI Chatbot on XTOPIA.IO with hybrid development method:
The Future of AI Chatbot
While there’s nothing certain about how fat AI Chatbot will go at this point, here are some projections for the market in 2021:
- By 2021, more than 50% of enterprises will spend more per annum on bots and chatbot creation than traditional mobile app development (Gartner).
- By 2021, nearly one in six customer service interactions globally will be handled by AI (Gartner).
- $5 billion will be invested in chatbots by 2021 (Chatbots Magazine).
- By 2021, customer service interactions globally will be handled completely by AI, will increase 400% from 2017 (Gartner).
We think AI Chatbot is yet to peak, considering the ongoing research and development in areas such as NLP (Natural Language Processing), NLU (Natural Language Understanding), Predictive Analytics and so much more.
Well, what do you think? Share your thoughts with us by leaving your comment below.
Interested to find out more about AI Chatbot? Here are some good reads we’d like to recommend to you:
XIMNET is a digital solutions provider with two decades of track records specialising in web application development, AI Chatbot and system integration.
XIMNET is launching a brand new way of building AI Chatbot with XYAN. Get in touch with us to find out more.
