The Chatbot Implementation Checklist

2020 Edition

January 26, 2020

Today, chatbots -- and to an extent: AI -- are used in large, medium and small enterprises to simplify business workflows and to improve customer service and reduce the cost of operations. In theory, chatbots are meant to provide seamless self-service options for your customers and employees. They help your IT support, human resources and customer service teams to focus on productive work and reduce the time and effort involved in laundering and repetitive tasks.

The sky isn’t always blue: the implementation and deployment of enterprise chatbots are fraught with many challenges. Organizations cannot get much of their chatbot implementation without a strong and continuous bot implementation strategy, as well as good planning for key governance, privacy and security issues. Based on commonly available studies published by those who set up chatbots for Fortune 500 companies, here are some of the best strategies to help you and your organization better plan a chatbot execution and to get the maximum business value out of your chatbots.

Security and privacy

Any organization that runs chatbots should be concerned with both privacy and security. The chatbot infrastructure implementation must ensure that the components comply with GDPR as well as any other industry-specific or location-specific regulations and policies. It is important that users provide information based on their acceptance levels to ensure confidentiality of information. User identity authentication, intent-level authentication, channel authentication, end-to-end encryption, and purpose-level privacy are all ways to increase the security and privacy of your chatbot. Each of those must be addressed in a coherent implementation strategy.

Running chatbots without experience can be expensive

An attempt to deploy home-grown chatbots without prior practical knowledge of the do's and don'ts of such an enterprise can turn the implementation into a flawed, random, and expensive project. Specifically, the implementation of customized chatbots may require a great deal of lead time and technical knowledge. Of course, one option is to choose “off-the-shelf”, ready-made solutions produced by experienced chatbot service providers. However, those so-called solutions may only provide a partial solution to the problem that the chatbots were planned to address, and thus leave open significant gaps in the overall business automation workflow.

 

Setting the right expectations

Due to the impetus surrounding artificial intelligence in the media, chatbots are widely regarded as one-stop solutions that make all business operations effective. However, this is an exaggerated and flawed view of reality. It is essential that the service provider sets the right expectations for their clients. Users should be aware of the chatbot's capabilities and limits before deploying them. Here are some sticky points to take into consideration:

Internal Marketing: Before implementing a chatbot, all users should be aware of the various use cases, capabilities, and benefits of this new tool. If employees are not fully aware of the bot’s purpose, operation and limitations, you will end up with a low adoption rate, lower returns on the project and, more than likely, a project’s failure.

Identifying a champion User: Most teams or internal departments will be able to work with, or around, the chatbot that you are implementing, however, it is important to identify a champion from each of these target groups and to keep that user in charge of the bot's operation.

The right time to market: Large corporations may have thousands of employees in many departments scattered across different countries and languages. It can take a long time for the implementation specialists to take into consideration the needs and nuances of all business disciplines and cultures involved in that organization. Forcing large sections of such an organization to wait for a chatbot to catch up can dampen the excitement and lead to a limited adoption.

NLP and Machine Learning

Your users should not only accept the chatbots but also find them genuinely useful, relatable and reliable. In short, your users should like/love your chatbots. These users should be well aware of the capabilities of the chatbot. Since these “animals” are not fully involved in all human-level conversations, they must provide a sense of meaningful communication and people must be satisfied with the response, down to the words and accent that they use to interact. Incorporating NLP and Machine Learning into your chatbots is an essential component of personalized user experience.

Measuring the success of your chatbot

You need to measure the user experience of your chatbots. Get feedback from your customers: ask them about their experience and learn how to improve your tools. This will help you discover if there are any development areas you should be addressing. You can also assign some business KPIs to your chatbot performance. Decreasing KPIs are the result of a matching decrease in CS/HR/IT costs and the difference in the number of tickets raised. Some indirect KPIs include employee engagement and customer experience.

Make your chatbot is future proof

Chatbot technology is evolving rapidly. Make sure that your chatbot can take advantage of any AI service available today and build it so to scale it for future services. This can be achieved by selecting bot platforms that include cognitive capture. This abstract layer ensures that you are not locked into any specific AI vendor or product.

Enhance your chatbots capabilities with data

The data collected by chatbots are used for user statistics. This data allows you to understand your user’s needs and to continually improve the capabilities of the chatbot. Powerful enterprise chatbot platforms are usually integrated with chatbot analytics, enabling you  to utilize that data fully.

Human hand-off

There will always come a time when a chatbot cannot bring a question to its conclusion, so your organization needs to make sure that there is always someone who can carry on the conversation when the chatbot fails. The option of transferring the conversation to the human should be as seamless as possible, so as to reduce its impact on the user’s experience.

High resistance

Many employees may fear that AI and chatbots threaten their jobs. Employees need to be aware of the real goals of your implementation; they need to understand that the bot has the potential to shield them from their repetitive work and to enable them to be more productive.

Match your brand identity

Bots must be made to fit your brand identity and voice. Doing so also helps to improve the overall user experience.

There is no doubt that chatbots are one of the enterprise AI technologies. But to build value from them requires extensive technical and operational skills and a strong execution-style.

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