Conversational AI solutions are one of the most effective applications of AI and machine learning. Additionally, the progress in natural language processing has improved the quality of text generation and speech processing in machines. Conversational AI solutions lead to efficient use in cases like Chatbots and Virtual Assistants. Although the growth in this field has been significant in the past years, still the slightest of mistakes in deploying these solutions can downgrade the results and outcomes.
7 Mistakes to Avoid While Implementing Conversational AI Solutions
Let’s explore the 7 common mistakes while implementing conversational AI solutions:
Starting a Conversational AI Project Without a Proper Strategy and Planning
The objective of implementing the conversational AI project shapes the process of developing the solutions like chatbots, smart bots, and virtual assistants. As these solutions completely depend on the users, dataset, and machine learning algorithm, proper planning of a development strategy is necessary to achieve the target goals.
A good strategy should focus on a particular objective addressing specific user intents. The best way to build a strategy is, to begin with, analyzing the audience’s behavior. Depending on the results of the former techniques, behavior, the tone of the conversational AI can be adjusted while developing the solution. This leads to optimized targeting and appropriate segmentation of the audience for conversational AI solutions.
Example: Conversational bots with a generalized library of words should not be used to implement every conversational solution. Instead, an optimized strategy backed by proper research should be implemented to choose the library of words.
Not Identifying the Correct Use-Case
Identifying the correct use case is crucial especially in the starting phase. The best way to go about it is to start with a narrow use case with a limited set of intents. Once deployed, the user behavior can be analyzed to further scale the conversational ai solution. This approach helps identify and address the implementation and deployment challenges at an early stage.
Targeting too Many KPIs in the Starting Phase
It is always good to focus on a few areas of KPI for strategic implementation, and it can help achieve the primary objectives of a business.
As they say, “Too much is too bad”, thus targeting too many KPIs in the starting phase inhibits the potential of the primary objectives. Also, focusing on various KPIs may lead to intervention in the AI strategies for completing too many goals in a short interval of time. Additionally, the starting phase is defined as the crucial part of a solution, and thus exploiting in every way can make the business vulnerable.
There are various KPIs to evaluate the role of Chatbots. Every parameter associated with the KPIs of the chatbots can help bring in a new insight to the table. Some of these KPIs are user experience, conversation duration, engaged users, new users, chat volumes, fallback rate, activation rate, and many more. Targeting each one of them at the start might lead to chaos as it takes some time to interpret the insights generated from KPIs.
Example: Targeting on new users and engaged users might lead to conflict in the strategies, as the strategy to increase new users is to impress through selling points of the business, but for increasing the value of engaged users, the content has to be engaging in terms of describing the points on which a particular user might be interested in, else the user will lose the attention and interest in the business.
Additionally, targeting activation rate while focusing on the former two KPIs can further create more chaos. Activation rate is the evaluation of the number of activities performed by users which are suggested by chatbots. The strategy for implementing this goal involves the chatbots pinging the users to perform actions. Thus there is a possibility that a new user or an existing user may divert from the website or application.
Isolating Stakeholders in Planning and Implementation Phase
Not involving all the stakeholders is one of the crucial mistakes during the planning and implementation phase. Building an intelligent virtual assistant as a conversational interface can automate various redundant and repetitive tasks. Thus input from every stakeholder is necessary for designing such an assistant. Also, automating a task might affect a particular stakeholder indirectly. Thus it can lead to mismanagement of the business operations.
It might be difficult to consider every opinion from all the stakeholders for planning a strategy, but updating the strategy later due to change requests from stakeholders who were not included in the planning phase becomes even more difficult. Hence, including all the stakeholders for planning the conversational AI project eases the business operations.
Poor Conversation Designing
The backend algorithm for text generation and speech processing is the foundation of conversational AI solutions. So, an inappropriate algorithm and dataset lead to a poor conversation design, making the conversational AI solution a bit less interactive. This drives away the users and defies the purpose of automating tasks and conversations.
Having No fallback strategy for the Conversational AI Solution
Conversational AI solutions are software programs integrated to form widgets like chatbots and virtual assistants. Hence, any technical glitch or unaddressed intents can fail the processes or create errors, thus having a backup in case of failure ensures reliability and makes a great impression on users. Therefore, backing up a conversational AI solution is very important for businesses.
Example: Most of the chatbots or virtual assistants are built to address a set of intents and work with API requests. In case of an out-of-scope intent or failure of an API, there should be a provision to handle the error. This could redirect to a new application or a human agent. This makes the business look more professional and ensures that users come back to the website.
Lack of Feedback Loop Built into the Solution
There is a scope of improvement in a business strategy or operation only when there is feedback. Otherwise, it is difficult to rectify the mistakes and understand what is not working for an organization. As conversational AI solutions are an interactive way of being in touch with the users or customers, the conversational data and user feedback can be gathered to further analyze and use to improve the conversational application.
Stay abreast with the latest AI trends and avoid making these mistakes while implementing conversational AI solutions.
The post 7 Mistakes to Avoid While Implementing Conversational AI Solutions appeared first on ReadWrite.