Exploring Generative AI-powered Chatbot Use Cases and Benefits for Enterprises Generative AI

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Written by Neurons Lab partner and CBDO Lilia Udovychenko

The worldwide conversational AI market is set to be worth $45.5 billion by 2030 according to research from McKinsey & Well. But what are the reasons behind such a high demand?

In this guide I explore the benefits and different business use cases for intelligent generative AI based chatbots, one of the main types of solution using conversational AI.

Enterprises in most industries are already using chatbots for a wide range of scenarios. For example:

• The retail sector is one of the leading adopters of chatbots to support customer service teams, and this trend will likely continue.
• Use cases for GenAI in banking include leveraging predictive analytics to identify the need for new financial products.

Every industry can benefit from adopting GenAI, according to PwC. In some sectors, the potential increase in operating profit margin is almost 20%.

GenAI-driven conversational e-commerce is already having a real impact for businesses adopting it. According to BCG:

• Early adopters have reduced their customer service costs by approximately 30%
• The speed of sales is 4x faster compared to traditional conversational commerce
• Customer satisfaction scores have improved significantly
• Consumer interest is high, with 66% in the US very keen to try GenAI-based conversational commerce

Chatbots can understand and respond to text or speech. By learning from conversations and complicated queries over time and drawing on proprietary information from knowledge bases, a well-trained model can provide a strong user experience.

However, many simple chatbots fail to meet expectations. Without rigorous planning, design, and knowledge-base implementation basic chatbots can misinterpret user needs, overlook the context, and fail to provide empathetic responses. Such chatbots decrease satisfaction levels and fall short of enterprise-specific requirements.

Therefore, it’s crucial to create chatbots that understand user intent. They must provide empathetic, personalized support – tailoring interactions to each user’s unique needs.

How does our conversational AI solution work?

High-quality conversational AI solutions use machine learning, natural language processing (NLP) and foundation models to accurately replicate the qualities of regular conversations.

By leveraging GenAI, it can understand complex queries and adapt over time to a user’s style of speech. It can even convey emotion – for example, striking an appropriate empathetic tone for sensitive topics.

Whereas pre-GenAI solutions required programming in advance with answers to anticipated questions, the latest models can produce fresh content. Outputs can include a combination of text, image, and sound, further replicating natural interactions. 

Such a solution can handle multiple languages and translate content into other languages, making it suitable for multi-market use.

It uses retrieval-augmented generation (RAG) and large language models (LLMs). But implementing a solution that uses RAG is not enough alone – finding relevant information from a company’s knowledge base helps ensure accurate, informed responses.

Furthermore, this type of solution can perform analysis tasks – summarizing content and making logical predictions. Over time, the model continues to learn and improve automatically, using algorithms to incorporate learnings from previous interactions.

In this video, our CTO and co-founder Alex Honchar explains the capabilities of our conversational AI solution and how it stands out compared to regular chatbots:

What are the use cases for enterprises?

By 2025, most business executives – 85% – say their customers will interact with GenAI, according to IBM.

There are many different scenarios where chatbots can provide efficiency through automation. Here are some of the most popular examples:

• Customer support: Helping customers by answering their queries, onboarding them, booking appointments, resolving issues, and much more. Handling routine queries and speeding up customer support representatives’ responses to more challenging ones, chatbots can free up their time for higher priority tasks.
• E-commerce: Chatbots can give online shoppers instant access to a virtual sales assistant, answering specific queries to help make a sale. Nearly half of shoppers – 47% – would consider purchasing a product through a conversation with a chatbot, according to HubSpot research.
• Business development: By integrating a chatbot with your CRM platform, it can capture leads, qualify them, build a contact list, and even book demo appointments for prospects.
• Marketing: During a conversation, chatbots can show customers product recommendations and update them about upcoming products, all while communicating using on-brand language.
• Internal support: Not all chatbots are customer-facing. They can also provide internal support for employees. For example, to reduce the number of queries HR or IT has to handle, staff can ask a chatbot to retrieve policy documents, troubleshoot technology issues, and receive fast answers.
• Analytics: Chatbots’ ability to summarize vast amounts of information and make predictions or recommendations in a conversational format provides powerful analytical tools. You can use a chatbot to gain insights into your clients, learning about their common pain points and preferences.

Let’s take a look at some practical examples of chatbots providing benefits for businesses:

Case study #1: Solar Manager

Scaling support operations for a growing customer base with an AI chatbot assistant for Solar Manager

Neurons Lab provided Solar Manager with a solution that leverages Anthropic Claude 3.5 architecture to increase customer support team productivity. The objective was to implement a chatbot assistant on AWS infrastructure. This initiative aimed to streamline support ticket responses, improve efficiency, and maintain high customer satisfaction.

We connected Solar Manager’s data and knowledge base to the architecture, ensuring seamless integration and AI validation. Our solution demonstrated the capabilities of GenAI and machine learning in optimizing customer support operations.

Solution

1. Frontend layer: This is the user interface with which the support agent interacts.
2. API layer: This layer, powered by AWS Fargate running FastAPI, handles the API requests – under the hood it runs Langchain, working with the LLMs.
3. Data layer: This layer contains the databases and storage services that house the data required for the AI to generate responses. It includes the Amazon Aurora PostgreSQL optimized for vector searches, Amazon Neptune for graph database queries, and Amazon ElastiCache Redis for caching data for quick access.
4. LLMs: Claude 3.5 and Amazon Bedrock process the data to generate accurate responses in this layer.

Results

Results for the Solar Manager customer support team included:

• Scaled support operations to accommodate a growing customer base while still ensuring high customer satisfaction levels.
• Facilitated knowledge transfer and training for new support team members by leveraging AI-driven responses and insights.
• Optimized the support team’s time allocation, allowing staff to focus on more complex cases by automating responses to regular, routine inquiries.

More info: Scaling support operations for a growing customer base with an AI assistant chatbot for Solar Manager

Case study #2: Xauen

Building a voice and text-based cybersecurity chatbot for Xauen

Xauen offers a virtual CISO (Chief Information Security Officer) evolving Laguun platform. As part of this, users fill in a questionnaire about their company’s security practices, with around 80 questions.

Xauen aimed to innovate how companies assess their security by leveraging AI for a conversational security assessment chatbot. The chatbot needed to offer an intuitive, human-like interaction experience to help CISOs evaluate their company’s security practices.

Neurons Lab developed an AI-powered conversational chatbot that transformed Xauen’s cybersecurity questionnaire into an interactive text and voice-based assessment. The chatbot provides a seamless and engaging user experience while ensuring thorough coverage of all security aspects.

Solution

We used the following AWS architecture to achieve this:

1. The chatbot API leveraged AWS services – including Amazon Bedrock and AWS Fargate.
2. The AI agent, built with an LLM and RAG architecture with a vector database, consistently follows the dialogue flow to ask all required questions.
3. Dialog consistency and fact-checking guardrails are in place for the LLM to ensure accurate responses.

Integration with Deepgram and Amazon Polly enables voice-to-text and text-to-voice functionality.

Results

Developed by Neurons Lab during a tight timeframe of only a few weeks, the innovative chatbot was ready in time for an AWS Summit, where Xauen successfully showcased it.

High chatbot response accuracy was essential for ensuring users received reliable and relevant information throughout the assessment process. Low latency was crucial for providing a smooth and engaging user experience.

By minimizing the time users wait for the chatbot to respond, Xauen can ensure that the assessment process remains interactive and efficient.

Consistently meeting the latency target will help to maintain user satisfaction and encourage the adoption of the chatbot for cybersecurity assessments.

More info: Building an innovative voice and text-based cybersecurity chatbot for Xauen

Case study #3: Entertainment

Enhancing the customer experience for a Top-10 tennis tournament with an advanced AI chatbot

The tournament organizer needed to improve digital engagement and the customer experience with a generative AI-powered chatbot, requiring a partner able to:

• Build a chatbot that uses structured tournament data, answers questions quickly, and maintains a conversation via WhatsApp.
• Organize knowledge about the tournament based on its website and closed information sources as a vector database ready for semantic search and retrieval.

The project began only six weeks before the tournament started, so the delivery deadline for a ready-to-launch chatbot was very tight.

The project vision emerged from the following business challenges:

• Needing a better digital engagement and digital experience to empower tournament visitors with timely answers to their questions based on the most recent information.
• Requiring more efficient usage of data collected from digital interactions – with actionable insights that lead to long-term customer retention and upsells.

Solution

Neurons Lab created a chatbot that uses GenAI powered by AWS Bedrock in just six weeks. Key features include:

• Conversational AI: The chatbot provided a natural language interface for users to ask questions in different languages and receive informative responses about the tennis tournament.
• Context awareness: By leveraging a stored chat history, the chatbot can maintain conversation context and provide relevant responses based on previous interactions.
• Data integration: The solution incorporates various data sources, including web crawling, document storage, and other enterprise knowledge bases, ensuring the chatbot can access comprehensive information about the tennis tournament.

Results:

The final product, delivered in record time, was a GenAI-based chatbot capable of:

• Providing an improved user experience and digital engagement for the tournament and empowered visitors with timely answers to their questions based on live information
• Organizing knowledge based on several forms of data, including the tennis tournament website and closed information sources, as a vector database ready for semantic search and retrieval.
• Building customer profiles to support up-sell opportunities and customer retention for future tournaments.

More info: Enhancing the customer experience for a Top-10 tennis tournament with an advanced AI chatbot

Conclusion

Without thorough development, chatbots can struggle to accurately understand user requirements. But with preparation in planning, design, and knowledge integration, chatbots like the ones detailed above can be very advanced.

It’s essential to develop chatbots capable of accurately understanding the intentions of different customers or users. AI assistants should offer empathetic, personalized support by accurately tailoring their responses to address each user’s specific requirements:

• Integrating a Knowledge Graph with a RAG system improves information retrieval while mitigating issues such as hallucinations or inaccurate responses.
• AI engineers can develop the model so that it continues to learn and improve over time, incorporating user feedback and adapting to changing circumstances or requirements.
• Chatbots also facilitate knowledge sharing and staff training, on top of freeing up time for staff to focus on higher-priority tasks.

The use cases are both customer-facing and internal, with wide-ranging efficiency benefits for enterprise employees across all industries.

About Neurons Lab

Neurons Lab is an AI consultancy that provides end-to-end services – from identifying high-impact AI applications to integrating and scaling the technology. We empower companies to capitalize on AI’s capabilities. 

As an AWS Advanced Partner, our global team comprises data scientists, subject matter experts, and cloud specialists supported by an extensive talent pool of 500 experts. We solve the most complex AI challenges, mobilizing and delivering with outstanding speed to support urgent priorities and strategic long-term needs.

Ready to leverage AI for your business? Get in touch with the team here.