Conversational AI solutions are one of the most effective AI and machine learning applications. In addition, progress in natural language processing has improved the quality of text generation and speech processing on 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 recent years, even the slightest of errors in the implementation of these solutions can degrade results and results.
7 mistakes to avoid when implementing conversational artificial intelligence solutions
Let’s explore the 7 common mistakes when implementing conversational AI solutions:
Starting a conversational AI project without proper strategy and planning
The goal of implementing the conversational AI project shapes the process of developing solutions such as chatbots, smart bots, and virtual assistants. As these solutions are completely dependent on the users, the dataset, and the machine learning algorithm, proper planning of a development strategy is necessary to achieve the target goals.
A good strategy should focus on a particular goal that addresses the specific intentions of the user. The best way to build a strategy is to start by analyzing audience behavior. Depending on the results of the above techniques, the behavior, the tone of the conversational AI can be adjusted while the solution is being developed. This leads to optimized targeting and proper audience segmentation for conversational artificial intelligence solutions.
Example: Chatbots with a pervasive library of words should not be used to implement all chat solutions. Instead, an optimized strategy supported by proper research should be implemented to choose the word library.
Failure to identify the correct use case
Identifying the correct use case is crucial, especially in the initial phase. The best way to do this is to start with a limited use case with a limited set of intentions. Once implemented, user behavior can be analyzed to further scale the conversational artificial intelligence solution. This approach helps identify and address deployment and deployment challenges at an early stage.
Targeting too many KPIs in the early stage
It is always good to focus on a few KPI areas for strategic implementation, and it can help to achieve the main objectives of a company.
As they say, “Too much is too bad”, so targeting too many KPIs in the early phase inhibits the potential of primary goals. Also, focusing on multiple KPIs can lead to intervention in AI strategies to complete too many goals in a short time frame. Also, the startup phase is defined as the crucial part of a solution and therefore exploding in every way can make the business vulnerable.
There are several KPIs to evaluate the role of chatbots. Each parameter associated with chatbot KPIs can help bring a new perspective to the table. Some of these KPIs are user experience, conversation duration, engaged users, new users, chat volumes, recovery rate, activation rate, and many more. Targeting each of them up front can create chaos, as it takes some time to interpret the insights generated from the KPIs.
Example: Targeting new users and engaged users can lead to conflict in strategies, as the strategy to increase new users is to impress through the company’s outlets, but to increase the value of engaged users, content must be attractive in terms of describing the points a particular user might be interested in, otherwise the user will lose attention and interest in the business.
Also, targeting the activation rate while focusing on the first two KPIs can create even more chaos. The activation rate is the evaluation of the number of activities carried out by users that are suggested by chatbots. The strategy for implementing this goal involves chatbots pinging users for actions. Therefore, there is a possibility that a new user or an existing user will be diverted from the website or the application.
Isolate stakeholders in the planning and implementation phase
Not involving all stakeholders is one of the crucial mistakes during the planning and implementation phase. Creating an intelligent virtual assistant as a chat interface can automate several repetitive and redundant tasks. Therefore, the participation of all stakeholders is necessary to design such an assistant. Additionally, automating a task could indirectly affect a particular stakeholder. Therefore, it can lead to mismanagement of business operations.
It can be difficult to consider all the views of all stakeholders when planning a strategy, but updating the strategy later due to stakeholder change requests that were not included in the planning phase becomes even more difficult. Therefore, including all stakeholders in planning the conversational AI project makes business operations easier.
Poor conversation design
The backend algorithm for text generation and speech processing is the foundation of conversational AI solutions. Therefore, an inappropriate algorithm and data set leads to poor conversation design, making the conversational AI solution a little less interactive. This alienates users and defies the purpose of automating tasks and conversations.
Not having an alternative strategy for the conversational AI solution
Conversational AI solutions are integrated software programs to form widgets such as chatbots and virtual assistants. Therefore, any technical failure or attempt not addressed can fail in the processes or create errors, so 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 chatbots or virtual assistants are designed to address a set of intentions and work with API requests. In the event of an intent outside the scope or failure of an API, there must be a provision to handle the error. This could redirect to a new application or human agent. This makes the business appear more professional and ensures that users return to the website.
Lack of feedback loop built into the solution
There is scope for improvement in a business strategy or operation only when there is feedback. Otherwise, it is difficult to correct mistakes and understand what is wrong for an organization. As conversational AI solutions are an interactive way to stay in touch with users or customers, conversation data and user feedback can be collected for analysis and used to improve the conversational application.
Stay up-to-date with the latest AI trends and avoid making these mistakes when implementing conversational AI solutions.