This is the final part of the Business Leader’s Guide to Launching Programmes series. In this series, I am demystifying the perceived complexities from launching AI Chatbots by providing an intuitive step by step guide that business leaders can follow. In this final part, we continue from step 9.
Design Conversation Flows
This is the stage where we put pen to paper to design. In this stage, a script is developed to model the conversation between the Chatbot and user. A script needs to be generated for each intent defined in Step 4, and the script should define the full conversation up till the user achieves the required goal.
The product manager, along with other key stakeholders need to be involved in this process.
For example, a sample conversational flow for an open bank account intent could look like below:-
Bot :- hello, how can I help you today?
User : I would like to open an account.
Bot : okay great, can I take your first name please?
User : Tolu
Bot : Thanks Tolu. Can you tell me your surname?
User : Adelowo
The selected character of the Chatbot must be used as a guideline when crafting the responses of the Chatbot. For example, a professional Chatbot could respond “Thanks Mr. Tolu” while an informal Chatbot could respond “Thanks Tolulope”
Read Also: Emerging technologies are fueling digital revolution in the Oil and Gas industry – Ayinde
Select Development Tools
At this stage, the development team should select the most appropriate tool for creating the chatbot. The selection of the development tools could possibly happen earlier but it is better to use the learnings of the previous steps as a guide in selecting the most appropriate tools.
The key development tools required for an AI chatbot implementation are listed below:
Programming language
Database
Conversational Engine – this component helps manage and keep track of the dialogue between the Chatbot and the user.
Natural Language Processing platform : chatbots with unstructured conversational Styles require a natural language processor to interpret user input and predict the user’s intent.
Live Agent platform : It is important for a chatbot to be able to pass the conversation over to a human agent if Chatbot is having issues understanding the user. The Live Agent platform is the platform to be used to continue that conversation between a human agent and the user. Examples of Live Agent Platforms are Zendesk, Intercom, Zoho etc
Create Training set
Creating a training set is optional depending on if a Structured or Unstructured Conversational Style Chatbot will be implemented.
If going with an unstructured conversational Style Chatbot, then a Natural Language Processing platform will be required. Natural Language Processing (NLP) platforms analyze customer input and attempt to predict the user’s intent.
Like a toddler learning to communicate for the first time, the NLP must also be trained to communicate with the users. The process of training the NLP consists of feeding the NLP with past customer conversations possibly from a Contact Center, emails, contact forms. This process helps the NLP understand how the user speaks. The data to be used for this training is called the Training Set.
Define Test Cases
As with any technology project, testing must be done after implementation is complete. At this stage test cases need to be defined to guide the testing of the Chatbot after implementation. The Test cases should be defined for each intent.
Implement and Train
At this stage, the software development team will implement the Chatbot and train the NLP.
Test
During and after implementation, the chatbot should be tested using the test cases developed earlier.
Retrain
Learning is a lifelong vocation even for Chatbots. If implementing an unstructured conversational Style chatbot, the NLP will require regular retraining as it interacts more with users. In particular, the NLP will need to learn from failed attempts to predict the user’s intent so it can get better for next time. As more conversation history is generated, the Chatbot should be retrained with the updated conversation history on a regular basis.
As a final thought, enterprise Chatbot projects are not Deploy and Forget Projects. A Chatbot is like a child that requires constant nurturing and monitoring to become better. It is not unheard of to keep the project team behind for a period of 2 -3 years while they continually improve the Chatbot from feedback. If is therefore important to think medium-to-long-term when planning for starting an Enterprise Chatbot Programme.
Tolu Adelowo is the Chief Executive Officer of Cousant Limited – a Technology consulting company that works with clients to solve the increasing complexities in managing technology products, projects, people and operations in Africa. Tolu is also an accomplished author that has written two e-books which are the ‘The Case for decentralized workplaces in developing economies’ and ‘The Rise of the Emerligent Economies – How African economies can win the AI war’.
