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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. Conversational AI solutions enable efficient use in cases such as chatbots and virtual assistants. Although growth in this area has been significant in recent years, the slightest mistake in the deployment of these solutions can further degrade results and results.

7 mistakes to avoid when implementing conversational AI solutions

Let’s explore the 7 common mistakes when implementing conversational AI solutions:

  • Start a conversational AI project without the right strategy or planning

The goal of the conversational AI project implementation shapes the process of developing solutions such as chatbots, intelligent robots and virtual assistants. Since these solutions are completely dependent on the users, the data set, and the machine learning algorithm, proper planning of a development strategy is required to achieve the target goals.

A good strategy should focus on a particular objective that meets the specific intentions of the users. The best way to build a strategy is to analyze audience behavior first. Depending on the results of the old techniques, behaviors, the tone of conversational AI can be adjusted when developing the solution. This leads to optimized targeting and appropriate audience segmentation for conversational AI solutions.

Example: Chatbots with a generalized word library should not be used to implement all conversational solutions. Instead, an optimized strategy backed by proper research should be implemented to choose the word library.

  • Not identifying the right use case

Identifying the right use case is crucial, especially in the start-up phase. The best way to do this is to start with a small use case with a limited set of intents. Once deployed, user behavior can be analyzed to further evolve the conversational AI solution. This approach helps identify and address implementation and deployment challenges at an early stage.

  • Target too many KPIs in the start-up phase

It’s always good to focus on a few KPI areas for strategic implementation, and this can help achieve key business goals.

As they say, “Too much is too bad”, so targeting too many KPIs in the start-up phase inhibits the potential of the main goals. In addition, focusing on various KPIs can lead to intervention in AI strategies to achieve too many goals in a short period of time. In addition, the start-up phase is defined as the crucial part of a solution, and therefore exploiting it in any way can make the company vulnerable.

There are different KPIs to assess the role of Chatbots. Each parameter associated with chatbot KPIs can help bring new insight to the table. Some of these KPIs are user experience, chat length, engaged users, new users, chat volumes, fallback rate, activation rate, and many more. Targeting each of these early on can lead to chaos as it takes some time to interpret the information generated by the KPIs.

Example: Targeting to new users and engaged users can lead to conflicts in policies, because the strategy to increase the number of new users is to impress with the company’s selling points, but to increase the value of engaged users, the content must be engaging in terms of describing the points in which a particular user might be interested, otherwise the user will lose attention and interest in the business.

Additionally, 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 performed by users that are suggested by chatbots. The strategy for implementing this goal involves chatbots pinging users to perform actions. Thus, it is possible that a new user or an existing user will turn away from the website or application.

  • Isolate stakeholders in the planning and implementation phase

Not involving all stakeholders is one of the critical mistakes in the planning and implementation phase. Building an intelligent virtual assistant as a conversational interface can automate various redundant and repetitive tasks. Thus, the contribution of each stakeholder is necessary to design such a wizard. In addition, the automation of a task can indirectly affect a particular stakeholder. Thus, it can lead to mismanagement of business operations.

It can be difficult to take into account all the opinions of all stakeholders when planning a strategy, but the subsequent updating of the strategy due to stakeholder change requests that were not included in the planning phase. planning becomes even more difficult. Therefore, including all stakeholders for conversational AI project planning facilitates business operations.

  • Bad conversation design

The backend algorithm for text generation and speech processing is the foundation of conversational AI solutions. Thus, an inappropriate algorithm and data set leads to poor conversational design, making the conversational AI solution a little less interactive. This alienates users and defies the goal of automating tasks and conversations.

  • Have no fallback strategy for the conversational AI solution

Conversational AI solutions are software integrated to train widgets such as chatbots and virtual assistants. Therefore, any technical issue or unresolved intent can cause processes to fail or create errors, so having a backup in case of failure ensures reliability and makes a big impression on users. Therefore, backing up a conversational AI solution is very important for businesses.

Example: Most chatbots or virtual assistants are designed to meet a set of intentions and work with API requests. In the event of an out-of-reach intent or API failure, there should be a provision to handle the error. This could redirect to a new app 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 only room for improvement in a business strategy or operation when there is feedback. Otherwise, it is difficult to correct mistakes and understand what is going wrong for an organization. Because conversational AI solutions are an interactive way to connect with users or customers, conversation data and user feedback can be collected for further analysis and used to improve the conversational application. .

Conclusion

Stay on top of the latest AI trends and avoid making these mistakes when implementing conversational AI solutions.

Stalin Sanamandra

A seasoned business leader and marketing professional with over 10 years of progressive experience helping businesses succeed in challenging markets.

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