7 bugs to avoid while implementing Conversational AI solutions

Conversational AI solutions are one of the most effective applications of AI and machine learning. In addition, advances in natural language processing have improved the quality of text generation and speech processing in machines. Conversation AI solutions lead to effective use in cases like Chatbots and Virtual Assistants. Although growth in this area has been significant in recent years, the slightest mistakes in the implementation of these solutions can still downgrade the results and outcomes.

7 bugs to avoid while implementing Conversational AI solutions

Let’s explore the 7 common mistakes while implementing conversation AI solutions:

  • Starting a conversation AI project without proper strategy and planning

The goal of implementing the conversation AI project shapes the process of developing solutions such as chatbots, smart bots and virtual assistants. Since 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 goals.

A good strategy should focus on a specific goal that addresses specific user intentions. The best way to build a strategy is to first analyze the behavior of the audience. Depending on the results of the previous techniques, behaviors, the tone of conversation AI can be adjusted while the solution is being developed. This leads to optimized targeting and appropriate segmentation of the audience for conversational AI solutions.

Example: Conversation bots with a generalized library of words should not be used to implement any conversation solution. Instead, an optimized strategy supported by proper research should be implemented to select the library of words.

  • Not identifying the correct use case

Identifying the correct use case is crucial, especially in the start-up phase. The best way to do this is to start with a narrow utility case with a limited set of intentions. Once implemented, user behavior can be analyzed to further scale the conversation solution. This approach helps to identify and address the implementation and deployment challenges at an early stage.

  • Targeting many KPIs in the start-up phase

It is always good to focus on a few areas of KPI for strategic implementation and it can help achieve the primary goals of a business.

As they say, “Too much is too bad”, so targeting too many KPIs in the start-up phase hampers the potential of the primary goals. Focusing on different KPIs can also lead to intervention in the AI ‚Äč‚Äčstrategies to achieve too many goals in a short amount of time. In addition, the start-up phase is defined as the crucial part of a solution, and thus utilization can in every way make the company vulnerable.

There are various KPIs to evaluate the role of Chatbot. Each parameter associated with the KpIs for chatbots can help bring a new insight to the table. Some of these KPIs are user experience, call duration, engaged users, new users, chat volumes, fallback rate, activation rate and many more. Targeting each of them initially can lead to chaos as it takes some time to interpret the insights generated from KPIs.

Example: Targeting new users and committed users can lead to conflict in the strategies, as the strategy to increase new users is to impress through sales arguments for the company, but to increase the value of committed users, the content must be engaging in terms of describing the points that a particular user might be interested in, otherwise the user will lose the attention and interest of the company.

Additionally, targeting activation rate while focusing on the previous two KPIs can further create more chaos. The activation rate is the evaluation of the number of activities performed by users suggested by chatbots. The strategy for implementing this goal involves chatbots that ping users to perform actions. Thus, there is a possibility that a new user or an existing user can redirect from the website or application.

  • Isolation of stakeholders in the planning and implementation phase

Not involving all stakeholders is one of the crucial mistakes in the planning and implementation phase. Building an intelligent virtual assistant as a conversation interface can automate various redundant and repetitive tasks. Therefore, input from any stakeholder is needed to design such an assistant. Also, automation of a task can affect a particular stakeholder indirectly. It can thus lead to poor management of business operations.

It can be difficult to consider any opinion from all stakeholders for planning a strategy, but updating the strategy later due to change requests from stakeholders who were not in the planning phase becomes even more difficult. Therefore, it facilitates business operations to include all stakeholders in the planning of the conversation AI project.

  • Poor conversation design

The backend algorithm for text generation and voice processing is the basis of conversation AI solutions. So an inappropriate algorithm and data set leads to a poor conversation design, making the conversation AI solution a bit less interactive. This drives users away and defies the purpose of automating tasks and conversations.

  • Has no fallback strategy for the Conversational AI solution

Conversational AI solutions are software programs integrated to create widgets such as chatbots and virtual assistants. Therefore, any technical error or unaddressed intentions can fail the processes or create errors, so having a backup in case of errors ensures reliability and makes a good impression on the users. Therefore, backing up a call AI solution is very important for companies.

Example: Most chatbots or virtual assistants are built to address a set of intentions and work with API requests. In the event of an out-of-scope intent or an API error, there should be a provision to handle the error. This could redirect to a new application or a human agent. This makes the company look more professional and ensures that users come back to the website.

  • Missing feedback loop built into the solution

There is only a scope for improvement in a business strategy or operation when there is feedback. Otherwise, it is difficult to correct the mistakes and understand what is not working for an organization. Since conversation AI solutions are an interactive way to stay in touch with users or customers, conversation data and user feedback can be collected for further analysis and use to improve the conversation application.


Keep up to date with the latest AI trends, and avoid making these mistakes while implementing AI solutions for conversation.

Stallin Sanamandra

Stallin Sanamandra

Experienced business leader and marketer with over 10 years of progressive experience in helping companies succeed in challenging markets.

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