Building Chatbots for Enterprises: 5 Things to Keep in Mind
Building Chatbots for Enterprises: 5 Things to Keep in Mind
Read on in order to learn more about chatbots, chatbot platforms, and five things to keep in mind, such as accuracy and security.
Join the DZone community and get the full member experience.Join For Free
Insight for I&O leaders on deploying AIOps platforms to enhance performance monitoring today. Read the Guide.
According to a survey conducted by Drift earlier this year, 15% of consumers have communicated with a business via a chatbot in the last 12 months, and 47% of consumers would buy something from a chatbot according to Hubspot (2017).
With AI and machine learning advancing fast, one of the areas that will experience a disruption in enterprise ecosystem is the customer channel — with chatbots.
With consumers already active on the popular messaging channels like Facebook, Twitter, Skype, Kik, Telegram, Whatsapp, etc. (depending on the geography), chatbots are the obvious addition for enterprises to the traditional customer interaction channels of a physical store, call center, web store, and the mobile app.
Chatbots not only reduce the cost to serve for a business process but since the conversational medium is more intuitive, they offer greater engagement and ease of use.
Truth be told, despite the obvious benefits, chatbots are still not mainstream in the enterprise IT world partly because there is a learning curve involved but mainly because most of the chatbots and chatbot building platforms are not yet "enterprise-grade."
What Does Being "Enterprise Grade" Mean for Chatbots/Chatbot Platforms?
Like all things, chatbots can vary in complexity from a bot that one can build in 10 mins, which basically just captures contact details and post office hours, to chatbots that assist field force agents with their entire diagnostics processes.
While each organization has its own business imperatives and IT System standards, in our experience, working with tier 1 and tier 2 enterprises, organizations expect the chatbot solution to meet a certain key criterion. There are many, but I am listing down seven key ones, just to get the ball rolling.
Since customer experience remains the paramount factor for an enterprise, a high accuracy is a must-have for enterprises. Nothing is more embarrassing for the enterprise and more frustrating for a consumer than the words "Sorry, I didn't understand that," and when it goes into a loop, trust me, at that point, the user is never coming back to the chat channel again.
Although earlier this year, for the first time, an AI model outperformed humans in reading comprehension (The SLQA+ ensemble model from Alibaba recorded an exact match score of 82.44 against the human score of 82.304, on the SQuAD dataset.). NLP solutions are still evolving mainly because it's still very difficult for NLP systems to understand the context. The second hurdle is the absence of labeled data to train models.
Until the time NLP and MRC reach human-level performance in understanding context, an alternate option that offers far higher accuracy compared to NLP led text only bots is the use of Closed Domain AI (preset context) rich messages — with cards, buttons, and various other UI elements in-chat. They not only provide far higher accuracy but also dramatically reduce the overall cycle time.
2) Leverage the Legacy Setup — Human + AI Hybrid
This is partly related to the point about accuracy, but there is a larger theme here.
All enterprises of repute, are probably already hugely invested and reliant on contact center agents for customer and employee support. Couple that with the fact that chatbots are also evolving — a step change into a Human + AI hybrid approach — makes more sense rather than an "AI only" path.
In all our hashblu.io implementations, we enable an approach of chatbot + human chat user journey pre-integrated with most of the key agent chat platforms like Live Chat, Live Agent, Intercom, etc. Whenever the user wants, they can switch to talking to a human who can assist them with a particular problem or query and then hand them back to the chatbot to continue their journey. Another popular approach is for the chatbot to engage and gather all the relevant data from the user and then hand-off to the human to assist with the task at hand.
The hybrid approach helps reduce cost, keeps the user experience optimal, and lets enterprises leverage their customer support and social media support agents most efficiently.
3) Data Privacy & Security
Consumers and enterprises are very sensitive about data privacy and data security, and rightly so. Incidents like the Facebook + Cambridge Analytica fiasco have exposed how vulnerable our personal data could be. Regulations like GDPR (especially for EU and the UK) should help in ensuring data privacy and security, but when designing chatbots and chatbot building platforms, data privacy should be a key design parameter rather than an afterthought.
Here, the simplest and most sensible thing to do is to ensure that by design, your chatbot platform/chatbot solution does not store any kind of customer private data or commercially sensitive data. The second thing is to provide complete transparency to the enterprise through an auditable interface of all the data points that you are storing and using for your analysis.
4) Supportability — It Does Not End With Launching the Chatbot, It Starts From There
While most chatbot solutions and platforms taught about the ease of building the bot and supposed launch time being under 10 mins, as their USP, in my decade and a half of experience with enterprise IT, I have realized that it is not just the go-live of an enterprise system, but the continuous supportability, maintainability, and the ability to make quick changes to the system that is equally, if not more, important to the enterprise.
This is where I believe the non-code based platforms, which allow for easy re-configuration, launching/removal of services through a GUI based interface, rather than being entirely code driven have an advantage. This approach offers high agility (for market events and campaigns), a higher degree of customization, and a greater ownership of the solution from the business aisle of stakeholders as well.
5) Enterprise — Centric Design and Easy Integration With Core Enterprise Systems, Allowing for Richer Use Cases
Unfortunately, most of the chatbot building platforms are focused on enabling what I think are relatively simpler and shallow use cases of after office hours support, lead generation, and (structured) FAQ bots for instance. This could be a function of the learning curve and the willingness of enterprises to gradually ease themselves into the chatbot ecosystem.
However, I suspect it is also partly attributable to the fact that none (at least the 10-12 I have seen) of the chatbot building platforms have been designed or built from the ground up, keeping the requirements of the large and medium enterprises in mind.
The hashblu.io platform, through the use of RESTful APIs and web services, enables easy integration with backend systems, so that chatbots can be used to expose and work in conjunction with core enterprise systems like RPA (Robotics Process Automation), service management, field force management systems, and CRM. This presents an option to enterprises to also explore richer use cases and bring out the same cost reductions and operational efficiencies but at a much larger scale.
Needless to say, there are many more factors that influence an enterprise's decision when selecting a chatbot solution. If you are keen to understand more, or if you are in the midst of a similar selection process yourself, please do feel free to reach out and my colleagues and I would be more than happy to assist you!
I do hope you found this post useful in getting some basic understanding around building chatbots for enterprises. As always, do leave your comments and thoughts — including any aspects that I might have missed. I will be more than happy to incorporate them.
Disclaimers: The above post in no way claims any copyright to any of the images presented.
Published at DZone with permission of Somnath Biswas . See the original article here.
Opinions expressed by DZone contributors are their own.