AI-Powered Customer Service: What You Can Do Today
The world is revolving around AI, and it’s becoming hard to distinguish what’s possible from what’s merely a promise in AI-powered customer service.
Most people want something very simple: a fully automated service, hassle-free, that works for contextual inquiries.
For example, they want a system that works almost like magic — someone visits a website, clicks on a chatbot, and asks any question related to the services.
A common question might be: “I made a purchase on day X and want to know when it will arrive.” The system needs to identify the user’s order and provide tracking information.
This capability already exists and, in fact, has been around for quite some time. But never before with Generative AI.
Today, AI-powered customer service systems can engage with users in natural language, avoiding the need for clicking through menus or disruptions caused by poorly configured chatbots.
In this article, we’ll explore precisely these aspects. How advanced will AI-powered customer service be by 2025?
Generative AI for Natural Language
One key point to clarify is that AI-powered customer service isn’t necessarily revolutionary in the broader context of customer service.
By “revolutionary,” I mean something that completely transforms the landscape of customer service. AI doesn’t introduce entirely new functionalities but rather opens up new possibilities.
Currently, there are three main ways to provide customer support on a website:
- Human-only support: All tickets, emails from the “Contact” section, WhatsApp messages, and social media inquiries are handled by your human team. Typically, employees from various areas handle support requests and forward them to the responsible team.
- Human support combined with menu-driven chatbots: Chatbots are used on websites, WhatsApp, Telegram, or social media platforms. Most larger stores already offer automated chatbot support, at least on WhatsApp. Critical issues are usually escalated to human agents, and the chatbot assists users in connecting with an agent. This approach requires creating chatbot flows.
- Chatbots with Natural Language Processing: Here, the process doesn’t always involve human agents (as in the first point) and isn’t entirely automated either. A support team is still necessary. However, these chatbots go beyond menus, directly responding to any question at any time in natural language.
➡️ Read also: What is Natural Language Processing?
This third approach is what most companies are interested in — and for a good reason.
Our research shows that personalization in marketing is consistently the best choice.
AI-powered chatbots have the major advantage of not being restricted by menus. Do menus work? Sure. But they significantly limit the service experience.
For instance, switching topics requires navigating back to the initial menu and selecting a new item before addressing your needs.
Natural Language chatbots eliminate this issue, allowing users to discuss anything, anytime, while receiving relevant responses.
But do chatbots offering this type of service already exist? Spoiler alert: absolutely.
Generative AI-Powered Customer Service Tools
Leadster itself offers this type of AI-driven service today, catering to institutional websites, service providers, subscription clubs, and e-commerce platforms.
Leadster.AI resolves up to 78% of support requests using Generative AI contextual responses.
Beyond natural, menu-free interaction, Leadster.AI can read the website pages where it’s installed (including multiple pages) to respond to direct queries.
For instance, if you install Leadster.AI on a category page of your site, it can answer any question about that category.
On product pages, it can provide detailed answers about individual products.
However, Leadster isn’t the only chatbot solution available with this functionality.
Another well-known option is Zendesk, though its focus is exclusively on customer service, while Leadster prioritizes support and lead generation.
Co-Pilot for Human Agents
Well, we understand that an AI agent providing personalized customer service is entirely possible.
In fact, it’s more than possible: you can test Leadster.AI right now and deploy an AI agent on your site in less than 10 minutes.
Let’s say your operation is too complex for AI agents to handle even basic queries.
Consider a travel agency, for example.
If the agency offers concierge-style real-time traveler support, AI might struggle to address complex requests like: “I need to change my lodging and will arrive in 30 minutes.”
In such cases, AI can act as a co-pilot for human agents, reading messages and suggesting responses.
Zendesk excels in this scenario. Let’s take a quick look at what it offers:
AI Co-Pilot Tools for Customer Support
Zendesk has been offering this service for a few years, making it one of the most well-developed solutions on the market today.
The copilot works in a few distinct ways.
In its simplest form, it acts as a virtual assistant, just a click away for agents, providing insights such as the customer’s mood, the urgency of the request, and which department can resolve the issue.
The other approach involves real-time content creation.
In this model, you can generate responses with direct AI assistance.
The earlier example fits perfectly here. A person wants to modify their hotel reservation. Instead of the AI handling the conversation and resolving the issue alone—which is very challenging—the AI suggests responses for the human agent, who only needs to click and select.
Does this seem like it doesn’t automate much? It depends. From the agent’s perspective, not having to type long responses already saves a significant amount of valuable time.
AI-Powered Customer Service with Business Data
This is another crucial aspect of AI-powered customer service — the AI must be capable of being fed with your company’s data, creating a comprehensive database to ensure responses are always the best they can be.
Leadster.AI, for example, performs this task with the data from your page. It can analyze the information on the page and respond accordingly.
Some other tools, specifically designed for customer service, also offer this functionality.
Here, we encounter a challenge. Current AI-powered customer service systems do not perform this type of task in the way we would hope.
There still isn’t a system capable of directly extracting information from your database, regardless of its size, to respond to user requests.
However, there is something similar that doesn’t exactly fit the AI-powered customer service model but can be used for this purpose with a capable development team.
Let’s explore this now:
Tools for Direct Database Interaction
This functionality is especially useful for larger e-commerce businesses managing extensive SKUs and robust databases.
A quick anecdote, but based on true events, to help you understand the importance of interacting with databases
I, the person writing this article, once worked editing photos and managing products for a multinational toy company.
The interface for that database was a very rudimentary intranet. We had to upload product photos to the database so the manufacturer’s websites would display the correct images.
However, navigating this intranet was a nightmare. All products were listed alphabetically, even though the database had tags for categories.
With AI capable of interacting with the database, I could have simply requested a list of products from a specific category — and half the work would have been done.
Today, AI solutions that come close to offering such functionality include:
- AskYourDatabase — the most commercial option;
- TARS — allows you to create a chatbot, connect it via API to a database, and use it to run queries and deliver responses to users;
- Oracle — much more complex, requiring an Oracle database and chatbot programming.
As you can see from the examples, it is entirely possible to build a chatbot that addresses the exact needs we are illustrating here.
However, at the same time, it requires prior construction and development work.
As NLP (Natural Language Processing) models continue to evolve in terms of integrations, this process will become easier.
Our “Fortune Teller” prediction for 2025 is that the next revolution in AI will happen precisely in this area. Let’s wait and see!
Speaking of integrations:
AI-Powered Customer Service + API Integrations
The first phase of AI-powered customer service was undoubtedly the development of LLMs (Large Language Models).
In other words, training AIs to communicate like humans.
In Brazil, the translation is “Grande Modelo de Linguagem.” Natural Language Processing is a critical component in building LLMs.
However, the practical use of AI, especially in customer service, requires more. A chatbot that knows today’s weather won’t help when you need to track a delayed product delivery.
That’s why we’re now entering the second phase of AI development: integration with other tools via API.
This phase has been accelerated by specialized AI tools in the market—not the big players like ChatGPT, Bard, or Gemini, but smaller models designed for specific purposes.
We’ve published two articles on this topic: one about AI for content creation and another on AI for generating images.
But what about AI-powered customer service? What’s the current situation? Let’s take a look now:
AI-Powered Customer Service Tools with API Integration
This feature is easy to find in the market. Most high-quality AI customer service solutions offer integrations.
For example, the following tools provide API integration:
- Leadster.AI
- Dixa
- Certainly
- Ada
- Intercom
- Zoho
It’s important to test these tools to understand how far their integrations can go, right?
And to determine if they are suitable enough for your strategy. Perhaps the integration you need is a bit more complex.
Most tests are quite straightforward. Leadster’s, for instance, takes less than 10 minutes to set up, starting from the moment you click on the banner just below.
AI-Powered Omnichannel Customer Service
Today, those working with larger e-commerce platforms need to be omnichannel.
The concept of omnichannel is simple: the customer, lead, or prospect can reach out to the company through various different channels.
Or better yet, to align with Omni (everything): the brand must provide support across all the channels it is present in.
Thus, an AI-powered customer service chatbot must do the same. It should be able to respond to customers in all possible contexts and also remember those contexts.
This functionality is closely tied to the integrations offered by the chatbot.
For example: it’s not possible to use a chatbot when sending an email. But it’s highly likely that, if you offer a chatbot for support, users will mention an email they’ve already sent.
Messages like “I received a suspicious email with your branding, and I suspect it’s a scam” need to be interpreted and verified by the chatbot.
At the same time, the chatbot also needs to provide omnichannel support so that users don’t have to search for multiple other chatbots in the market, one for each platform.
Let’s take a closer look at the tools that offer omnichannel support as a key feature:
AI-Powered Omnichannel Customer Service Tools
The list isn’t much different from the one we saw earlier in the section about API integrations.
However, it’s worth highlighting HubSpot Breeze here, especially for those already using HubSpot.
It’s an excellent tool, and if HubSpot is already part of your marketing strategy, you can combine its omnichannel capabilities with integrations to deliver truly remarkable results.
Some options include:
- Zendesk
- HubSpot Breeze (launched in late 2024)
- Netomi
- Dixa
- Certainly
- Ada
- Intercom
AI Customer Service + Native QA
QA stands for quality assurance, a fundamental part of system development.
In the daily life of developers, QA is responsible for analyzing whether the code provided by developers meets the quality standards expected by the brand and the service.
Chatbots also need QA, but it manifests a bit differently.
It’s essential that the chatbots you choose provide performance dashboards and the ability to modify certain lines of dialogue here and there.
For example, every time someone mentions the phrase “credit card,” you can include a personalized message explaining how payments are processed.
But if the payment method changes, you must be able to update that information directly in the chatbot.
And even outside of such extreme cases. Perhaps the AI’s tone differs from what you expect for your brand. Can it be edited?
You need tools that allow for this customization. First, because personalized marketing is crucial for boosting sales.
And second, because AI demands personalization. If you don’t do it, other brands certainly will.
Virtually all the tools we’ve mentioned so far offer QA at some level.
It’s important to test them to understand how far their QA capabilities go and whether they’re sufficient.
So, does it make more sense now what AI-powered customer service chatbots can already do?
All these functionalities are present, in one form or another and at varying levels of complexity, in Leadster.AI.
In fact, it goes even further: it also provides support for creating product descriptions for your e-commerce.
Take a test today and see for yourself what it can do. I’ll be waiting for you—just click the banner below.
Thanks for reading, and I’ll see you in the next article!
