Hit the target with conversational AI

[HPE_Experts]

Explore how conversational AI can support the customer service experience and the role of HPE Private Cloud AI in ensuring a secure, scalable implementation.

Conversational AI can seem complex to understand, so let’s break it down with a straightforward use case: customer service.

Reaching out to customer service can feel like a frustrating game of pinball. Getting to the correct department to help with your inquiry is like trying to guide a pinball’s trajectory with flippers and bumpers. Although customer service systems have improved greatly over time, nothing beats being able be tended to without an exhaustive wait time or fumbling through the maze of options to choose from and most importantly getting one’s issue resolved.

If it were feasible, enterprises would staff their customer service centers so customers had a customized, personalized experience with no wait times and round the clock service. But the reality is customer service centers face multiple challenges. Peak demand periods vary, making it difficult to predict the number of agents needed difficult. Additionally, training agents to handle any customer issue at first contact poses another challenge. From a human capital perspective, it is not possible.

Now let us explore what can be made possible with the use of conversational AI.

The customer experience with conversational AI

A customer’s experience with conversational AI becomes a seamless, intuitive interaction tailored to the clients’ needs. Customers benefit from the convenience of help available 24/7, faster resolutions on first contact without the need to repeat details. They receive personalized recommendations or solutions tailored to the individual. When an issue is complex, a smooth transfer to a live agent is initiated.

.The folllowing figure depicts the flow of a basic conversational AI customer order assistance scenario. 

From a business perspective, conversational AI is transformative. It enables enterprises to reduce costs by automation and offers enterprises scalability. An AI system can manage many customer interactions at once, reducing staffing needs. It improves customer experience enabling upselling and growing revenue.

Breaking down of the core of conversational AI

Conversational AI combines natural language processing (NLP), machine learning (ML), and speech recognition to facilitate human-like interactions, automating complex dialogues in multiple languages.

The use of retrieval-augmented generation (RAG) enhances responses by integrating external knowledge bases making interactions more contextually relevant and personalized. This integration transforms customer interactions into meaningful engagements by incorporating specific and up-to-date information.

The following figure (Figure 2) shows the components of conversational AI and RAG whereas the previous figure (Figure 1) shows where they are implemented in a basic interaction between customer and conversational AI agent.

Figure 2. Conversational AI with RAG

The application of conversational AI is not limited to customer service use cases. For financial institutions, implementations can balance inquiries and perform loan eligibility checks. In healthcare, conversational AI can ease access to health services for health consultancy with doctors and health-claims. Conversational AI is a powerful tool in multiple domains.

Challenges to implementation

Implementing conversational AI comes with numerous challenges. For one there’s the need to handle the quirks of language, such as ambiguities, slang, and idioms, while keeping the conversation relevant and on track. Creating smart, diverse responses, working with different types of inputs (e.g., text and voice), and personalizing interactions without compromising privacy can be tricky. Incorporating internal data sources for the personalization of interactions is often guarded against depending on regulatory mandates. Additionally, making the system scalable and fast enough for enterprise use involves coordinating complex models and tailoring them to specific business needs. Not to mention, a misfire in infrastructure acquisition means delays and loss of return on investment. In summary, conversational AI requires robust and performant platform and infrastructure.

Erase the barriers to entry

HPE Private Cloud AI can help businesses implement scalable and secure conversational AI applications. This purpose-built solution, codesigned by HPE and NVIDIA, supports a wide range of conversational AI use cases, from basic transcription services to complex, multilingual intelligent virtual assistants. With HPE Private Cloud AI enterprises can handle varying workloads, ensuring consistent performance as demand grows.

HPE emphasizes security measures to protect sensitive data, adhering to industry standards and compliance requirements.  Customization of various conversational AI applications is possible letting businesses tailor solutions to their specific needs. By leveraging HPE Private Cloud AI, organizations can efficiently develop and deploy conversational AI solutions that are scalable and secure, and meet the demands of modern enterprises.

Read the white paper to learn more. 

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Meet Denise Ochoa Mendoza, HPE Solutions Engineer

Denise is part of HPE's Worldwide Hybrid Cloud Solutions team, where her passion for technology drives her efforts to support customers and pre-sales. With a focus on big data, AI, and advanced analytics, she is dedicated to leveraging innovative solutions to deliver meaningful impact. Connect with Denise on LinkedIn. 

HPE Experts
Hewlett Packard Enterprise

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