Every business is trying to figure out how to utilize AI-powered solutions and you jumped on the bandwagon too. Soon you realize that the nuance goes beyond just training on your data. You need to train the model for a use case that benefits your business and your customers the best and provides a truly innovative approach.

In this blog, we’ll explore why Gen-AI bots are better than traditional Chatbot integrations, some of the many possibilities for how to add these bots to your product, and the ways to go about it. Let’s dig in:

Why AI Chatbots are Better than Traditional Chatbots

Traditional chatbots, often limited by predefined scripts and rigid programming, provide a mechanical and sometimes frustrating user experience. In contrast, AI-based chatbots harness the power of machine learning and natural language processing to offer more dynamic, intuitive, and personalized interactions.

These AI chatbots can understand context, learn from interactions, and improve over time, making them far superior to their traditional counterparts. Their ability to handle complex queries and engage in meaningful conversations makes them an asset for any business because they get better over time. Some of the tangible benefits of AI Chatbots are:

Contextual Understanding:

AI chatbots can understand and interpret user context, enabling more accurate and relevant responses.

Learning and Adaptation:

These chatbots continuously learn from interactions, improving their performance and user satisfaction over time.

Complex Query Handling:

AI chatbots can manage complex and multi-step inquiries, providing comprehensive solutions without the need for human intervention.For technical leaders, it’s a boon when they want to allocate their engineering resources efficiently for better resource utilization, reducing operational costs, and enhancing user engagement.

‍Examples of How AI Chatbots Can Be Used Beyond The Traditional Ways

While Chatbot integrations have been happening for a long time across businesses of different scales and sizes, the one core advantage of AI-based chatbots is that instead of becoming obsolete they learn with time. Let’s look at some of the Chatbot integration examples from a user experience as well as the approach for the technical implementation of the same:

Smart Assistants

AI chatbots can be deployed as intelligent virtual assistants, providing users with personalized support across various tasks. For instance, in a SaaS environment, a smart assistant could help users navigate the software, suggest optimal workflows, and troubleshoot issues.

‍Technical Implementation: Integrate APIs for accessing user data and application functionalities, enabling the chatbot to provide contextual and actionable recommendations.

Inquiry Bots

These bots can search a brand’s website or knowledge base to find specific product information and relevant links, enhancing user self-service capabilities.

Technical Implementation: Utilize search algorithms and NLP techniques to index and retrieve information from internal databases and documentation repositories.

Transaction Bots

AI chatbots can manage end-to-end transactions, from product selection to payment processing, thereby streamlining the customer journey.

Technical Implementation: Integrate with payment gateways, inventory management systems, and order fulfillment APIs to facilitate smooth and secure transactions.

E-commerce Personal Shoppers

AI chatbots can act as personal shoppers in e-commerce platforms, providing users with personalized shopping experiences. These chatbots can recommend products based on user preferences, past purchases, and browsing history.

Technical Implementation: Leverage machine learning algorithms to analyze user data and generate personalized product recommendations in real-time.

Financial Advisors

AI chatbots can serve as virtual financial advisors in the finance sector, helping users manage their finances, track expenses, and provide investment advice. These chatbots can analyze a user’s financial data and offer personalized recommendations.

Technical Implementation: Integrate with financial data APIs and employ predictive analytics to provide users with tailored financial advice and insights.

Healthcare Assistants

AI chatbots can play a crucial role in healthcare by assisting patients with scheduling appointments, providing medication reminders, and offering preliminary medical advice based on symptoms.

Technical Implementation: Connect with electronic health record (EHR) systems and utilize natural language processing to understand and respond to patient queries effectively.

Educational Tutors

In ed-tech, AI chatbots can act as virtual tutors, providing students with personalized learning experiences. These chatbots can answer questions, provide explanations, and recommend study resources based on individual learning needs.

Technical Implementation: Integrate with learning management systems (LMS) and employ adaptive learning algorithms to tailor educational content to each student's requirements.

Real Estate Assistants

In real estate, AI chatbots can assist potential buyers or renters by providing information about properties, scheduling viewings, and even guiding users through the mortgage application process.

Technical Implementation: Utilize property listing APIs and scheduling software to offer users real-time property information and appointment booking services.

HR and Employee Support

Internally, AI chatbots can assist with HR functions, providing employees with instant access to information about policies, benefits, and more.

‍Technical Implementation: Connect with HR management systems and databases to fetch and update relevant employee information dynamically.

How Can Businesses Add These Chatbots to Their Product

The process of adding an interactive ai-based solution has 2 main component before getting to deployment: figuring out the intelligence logic and training, and figuring out the way the chatbot will be presented to the end user i.e. the chat tools and features. 

Let’s look at both these components, what they entail, the deployment and the processes and requirements after:

STEP 1: Building on Top of General AI Models

Engineering teams can train existing AI models like ChatGPT, Gemini, Claude, etc. over their data  to meet specific needs or build custom chatbot flows using tools like Dialgoflow, Voiceflow, etc. This would require them to do:

• Data Preparation: Collect and preprocess the data on which you want to train the model
• Model Training: Train the chosen model with the prepared data
• Testing: Test the model using some test data
• Ongoing Training: Ensure that you update your bot knowledge periodically

STEP 2: Decide Between Building Your Chat Tool or Opting For A Third Party SDK For It

At this point you need to have a face to your smart bot. For that you can opt for building the infra, the logic and the UI in-house and work simultaneously on figuring out compliaces or you can opt for a 3rd party solution that can do this all for you at a lesser cost. This step would require you to consider:

• Selection: Evaluate SDKs based on features, customization options, and compatibility with existing systems against all the cost and other parameters when building in-house and opt for an alternative that is better suited for you.
• Customization: Customize the chatbot’s functionalities and user interface to align with the brand’s requirements. If opting for a third party Chat SDK always check if the brand offers pre-built themes or not since this step can become time-consuming without those and impact the overall time to go live.

Also read: how to choose between building or buying chat solution

Step 3: Deploy

Ensure ongoing maintenance and updation of training data and launch it for your end users:

• Deployment: Integrate the SDK into the product’s architecture, ensuring seamless communication between the chatbot and other system components.
• Ongoing Management: Manage and update the chatbot regularly to incorporate new features and address any emerging issues.

Benefits of Having AI Chatbots Within the Product

Even though it’s a no-brainer to have Chatbot integration, generative AI-based chatbots provide additional advantage for a variety of business and product needs. let us discuss some of them:

Product Adoption And Improvements

AI chatbots can significantly enhance the onboarding process for new users, providing real-time assistance and personalized guidance through a contextual understanding of user challenges and needs. By simplifying the product to have minimal effort for an end user, businesses can expand their customer base by tapping into newer demographics who may not be as tech-savvy.

User Retention

Interactive chatbots can go beyond just answering a user’s query and instead ask relevant questions, provide relevant product links (inquiry bot), assist in a transaction, and help with after-purchase support.

These chatbots can also check in with users periodically, offer personalized content and add-on offerings based on user behavior, and provide solutions to common problems before they escalate. This form of continuous engagement through AI chatbots without the intrusiveness of constant ads, emails, and promotional messages can improve user retention by addressing issues proactively and maintaining high levels of user satisfaction. 

Business Benefits

Implementing AI chatbots can positively impact various business metrics, such as reducing customer support costs by handling general queries and requiring human agents at a minimal level, increasing sales conversions through personalized recommendations, and enhancing data-driven decision-making based on the information gathered from all the interactions with a user.

Conclusion

The potential applications of AI-based chatbots extend well beyond traditional customer support, offering significant benefits in various business functions. By leveraging Gen AI bots, businesses can enhance product adoption, improve user retention, and positively impact critical business metrics. For technical teams including product managers, heads of engineering, and CTOs, understanding the technical considerations and strategic implementations of these chatbots is crucial before deciding what kind of chatbot integration to have in their product and what’s the best approach for it.

Embracing AI chatbot integration is not just a technological upgrade but a strategic initiative that can drive innovation, efficiency, and competitive advantage.

As businesses continue to explore the capabilities of AI chatbots, those who invest in advanced integrations and continuous improvement will be best positioned to deliver exceptional value and stay ahead in a competitive market.

About LikeMinds

LikeMinds elevates businesses in unlocking the true potential of their users through their in-app community and social network. Using LikeMinds, businesses achieve higher conversion and retention, by building custom community experiences in their existing platform unlocking community-led growth.

With LikeMinds, businesses get an easy-to-implement and highly scalable infrastructure with a fully customizable UI. All of this with a customization time of 3 days and a deployment time of 15 minutes.

Our Chat and Feed infra have pre-built widgets such as image carousels, PDF slides, short videos, polls, quizzes, events, forms, and more for user engagement and retention along with moderation capabilities to ensure frictionless community operations.