Deploying AI Service Chatbots

Deploying AI Support Chatbots in 2025

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Introduction

By now, everyone has interacted with AI support chatbots with varying degrees of success. Some chatbots are extremely sophisticated and claim to be human companions. Examples include Botify AI and Replika, which simulate full conversations with humans. Some responses might not be appropriate, and there is usually a refresh function. These are based on the Large Language Memory. You can read the first post in this series for a quick refresher on the various AI models.

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Advantages of Support Chatbots

The latest generation of support chatbots uses generative AI to answer questions with natural language. Even the first generation of support chatbots using the Reactive Model remains highly useful today. Despite their limits, such as the lack of long memory and heuristic improvements, these chatbots have a very long service life. 

Regardless of which type of AI is used, your chatbot should:

  • Answers FAQs.
  • Triage the requests.
  • Open support tickets for future human follow-up.
  • Track customer orders.

For those who reported bad experiences with chatbots, it’s not because of the customer but the programming. Sometimes the customer needs help beyond the scope of the chatbot. For example, a customer is calling a mortgage company because he needs to change his bank routing number. If the bot can only take payment and list the current balance and has no option for the customer to speak to a live person, then the customer leaves the interaction dissatisfied. This example shows the value of point 3 above, where chatbots need to be able to hand off requests beyond their scope to human support, so customers’ issues are ultimately resolved.  

The End Goal

Before we discuss best practices, consider this all-important question: What is your end goal? 

In other words, what kind of workload are you reducing? Chatbots are meant to supplement, not replace, human interaction. Chatbots are also meant to supplement, not replace, your company website or a knowledge base. Site visitors should be able to read all the content for themselves and then use a bot if they have additional questions and concerns. For example, many colleges and universities have live chat bots during business hours. During off-hours, an AI-powered version takes over. The bot can still answer most of the questions an admissions counselor would. The end goal is still to get a prospective applicant’s contact information for further follow-up. 

Less complex situations, such as a customer tracking the status of an order, have simple end goals. Take FedEx or UPS, or other logistics companies; all the customer wants to know is the location of his package: still at the origin, in transit, at an intermediate warehouse, or out for delivery. The customer has usually retrieved the information he wanted and the interaction ends.

Best Practices

Native or Third-Party Deployment

One important technical consideration is whether your customer-facing platform supports a chatbot. Customer-facing platforms could be your company website, hosted on WordPress or Squarespace, or a CRM like Zoho. If your platform does have a native chatbot function (example: Zoho’s ZohoIQ), this chatbot is already developed for that platform. Regardless, you should research whether the native functions are suitable for your needs. Even if it automates only 75% of your needs, it is still useful.

If your customer-facing platform, like Squarespace, does not have a native chatbot but does allow for certain third-party bots from an approved integration list, again, research which one best suits your needs. It may come down to the bot’s look and feel.

The third possible scenario is using an external third-party vendor’s chatbot. This is especially true for open-source platforms, which draw from a community of developers. You should ensure this vendor’s chatbot can be integrated with your customer-facing platform. 

Costs

Another important fact is the cost of the chatbot. Is this chatbot already packaged with your platform? Or do you have to purchase it as a separate package? Or do you have to update to a higher subscription tier? Or can you afford not to have a chatbot?

Besides financial costs, there are also time and development costs. In the open source context, consider how much time you need to spend to develop the bot on your own. Even with the help of generative AI to shorten coding time, you need to spend time testing the bot before deployment. Is there a more suitable turnkey solution from another open-source developer?

To save development costs, some platforms offer “No Code Required” bots. These are a good option for non-developers and novices. Even then, there is still a learning curve.

The Critical Out

A quick Google search on “Who still wants to talk to a human in a support call?” showed that the pollsters found answers ranging from 50% to 75%. In other words, despite the best AI programmers and all the advances as of 2025, overwhelmingly, people still want to talk to a live person. 

In programming the bot, this is where you must decide at what point the virtual interaction ends and the customer talks to a human. While you cannot account for every edge case and complex situation, you should design the workflow clearly for the scenarios you do know. From a technical perspective, will the bot call the next available service representative? Or switch to the live chatbot? Or in Amazon’s case, have the next available rep call the customer?

Metrics and Evaluation

Consider a data-driven approach. What were your key metrics pre-deployment? After deployment, did those metrics change significantly? Examples would be a reduction in customer support calls for situations that could have been resolved by self-service or an uptick in engagement and conversion. 

Some platforms include a feedback functionality. Here, customers may rate their satisfaction with their recent interaction with the chatbot. That is another good way to track the usefulness of a chatbot. It could be that a chatbot is not necessary at all.

Multilingual Support  

If your company has a global reach, make sure that your chatbot is also multilingual. Fortunately, many chatbots in 2025 are multilingual. The most common languages other than English, such as French, Spanish, and German, are covered. Nevertheless, translation accuracy remains an issue, so verify that the prompts and results are accurate. This is doubly so for technical language.

The latest multilingual chatbots designed for enterprise are:

Each one has its strengths and weaknesses and costs will vary.  

Conclusion

Ultimately, the implementation of an AI chatbot should solve at least one or more problems without creating more problems. Tracking key metrics is a very good way to ensure that the implementation is beneficial to your business. And while AI is often touted as the wave of the future and even disruptive technology, the need for human contact is still highly desired. Thus, AI chatbots should continue to supplement, not replace, human customer support interactions.

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