From chatbots to language models: Role of generative AI in customer experience transformation | HCLTech
Digital Process Operations

From chatbots to language models: Role of generative AI in customer experience transformation

Virtual assistance and automated customer support have been in use for the last few years, but what drives enterprises now is leveraging the true potential of AI to shape futuristic experiences.
9 minutes read
Rahul Poddar


Rahul Poddar
Global Director - CX Practice, Digital Process Operations (DPO)
9 minutes read
From chatbots to language models

CX has become a strategic metric for organizations post the pandemic and is now a critical business differentiator. Unsurprisingly, future-facing enterprises are blending in technological advancements with purpose-oriented and value-based strategies to proactively reach out to their customers to maximize revenue, expedite outcomes and optimize experiences.

Virtual assistance and automated customer support have been in use for the last few years, but what drives enterprises now is leveraging the true potential of generative AI for customer experience enhancement. A recent poll by Gartner [1] revealed that 38% of enterprise leaders are using generative AI to improve CX and customer retention. With a focus on customer centricity, enterprises must apply generative AI intelligently across multiple use cases to ensure optimal CX. As per McKinsey’s State of AI report for 2022, common AI use cases by function include contact center automation, customer service analytics, customer acquisition and lead generation. This clearly underlines the changing mood among industry leaders in their quest to thoroughly digitize their marketing and support operations to improve CX.



38% of enterprise leaders are using generative AI to improve CX and customer retention.



The evolving role of generative AI in customer experience

The global market for generative AI is valued at $11.3 billion in 2023 and is projected to be worth $51.8 billion by 2028, registering a CAGR of 35.6% in the next five years, as per MarketsandMarkets [2]. Over the next few years, enterprises will focus on leveraging technological enhancements in language modeling and generative adversarial networks (GANs), advancements in the fields of meta-learning and few-shot learning and development in multimodal data ingestion techniques. In the long term, enterprises will be aided by fully autonomous generative AI systems that not only optimize customer experience but also drive scientific research and sustainability.

Revolutionizing CX with context and personalization

has revolutionized customer service through the development of conversational agents. Chatbots and virtual assistants powered by algorithms that can understand natural language can engage in meaningful conversations and provide customers with real-time support. These agents can not only handle customer inquiries but also analyze vast amounts of customer data, including purchase history, browsing behavior and demographic information, to generate personalized product recommendations, leading to elevated shopping experiences and more relevant purchases.

Importantly, generative AI can generate high-quality text, images and videos based on predefined parameters or examples. This unlocks automated content generation for marketing campaigns and personalized emails and social media posts. Similarly, it is also being used for cutting-edge and virtualized customization using computer vision and GANs. Customers can virtually try on clothing and accessories and even experiment with different hairstyles, allowing them to visualize and customize their choices before making a purchase.

Generative AI models have the ability to automate and optimize large-scale language translation. This capability enables organizations to offer personalized real-time support, maintain consistent messaging and adapt to changing language patterns. By overcoming language barriers, these models improve communication and enhance customer satisfaction.

While the technology can disrupt traditional digital CX transformation platforms, it does not necessarily mean a slow death for them. Instead, they will evolve with the integration of generative AI, co-exist alongside specialized solutions or transform through partnerships or R&D. Until then, businesses will leverage the benefits of generative AI while continuing to use their existing CX solutions.



While the technology can disrupt traditional digital CX transformation platforms, it does not necessarily mean a slow death for them.



Diverse applications and use cases of generative AI

As with any technology that aims to revolutionize the way we engage with customers, it is important that we move in a phased manner and ensure that the organization is comfortable with the application of generative AI technology, especially CX leaders and customer-facing agents. Hence, typical planning can commence with impacting low-complexity transactions to eventually integrate with cases that either provide the right human-AI experience symphony or ones that can manage a fully AI-enabled but empathetic digital front line.

Here are several industry-specific use cases for generative AI, some of which are already being implemented:

  1. Retail

    Generative AI transforms the retail landscape by tailoring customer experiences through personalized recommendations. By analyzing individual preferences and purchase history, it generates product suggestions that resonate with shoppers, facilitating the discovery of items aligned with their needs. This personalized approach not only enhances customer satisfaction but also encourages longer and more meaningful interactions with the brand, making customers feel valued and understood.

  2. Gaming

    In the gaming realm, the technology revolutionizes customer experience through procedural content generation. This innovation not only keeps players constantly engaged but also enhances their overall experience. By dynamically creating diverse in-game content such as levels, characters and quests, it ensures that each player experiences a unique and captivating virtual world. At the same time, CX resources can offer personalized support and tech troubleshooting, create customized gaming plans and also focus on revenue generation through upsell/cross-sell and retention based on the individualized content generative AI will offer the agents.

  3. Healthcare

    The integration of generative AI in healthcare empowers medical professionals by enhancing diagnostics and patient care, ultimately improving the overall patient experience. By analyzing medical images, the technology refines image quality and assists in accurate diagnoses, reducing uncertainty and minimizing the need for invasive procedures. It also accelerates drug discovery through molecular simulations, predicting potential drug candidates and optimizing formulations, which ultimately expedites treatments and improves patient outcomes. Furthermore, generative AI contributes to personalized patient care plans by considering individual medical histories and conditions. This patient-centric approach leads to more effective and tailored healthcare strategies, increasing patient satisfaction and trust in the medical process.

  4. Financial services

    Generative AI revolutionizes risk assessment and fraud detection by analyzing intricate data patterns, enhancing transaction security and ensuring seamless financial interactions. The technology also aids portfolio management by analyzing market trends and suggesting investment strategies, providing financial advisors and clients with valuable insights for making informed decisions. Additionally, AI-powered chatbots and virtual assistants provide rapid, precise responses to customer inquiries, offering an efficient and accessible channel for addressing financial queries and concerns.

The ethical considerations for using generative AI successfully

Generative AI promises a bright, new world of opportunities for enterprises, customers and communities. However, striking a balance between technological innovation and ethical principles is crucial to ensure the responsible development and deployment of generative AI systems. Addressing the ethical considerations requires a collaborative effort among researchers, policymakers, industry stakeholders and society.

Here are some of those important considerations:

  1. Privacy and data protection: It is paramount for enterprises to ensure user privacy and compliance with data protection guidelines. Transparency in data collection, consent mechanisms and secure storage and handling of user data are essential to maintain customer trust and respect.
  2. Harmful content and misinformation: Generative AI can be used to create deceptive or misleading content, including deepfakes, posing a significant challenge in combating misinformation, protecting public trust and preserving the integrity of media. Developing robust detection mechanisms and educating users about the potential for manipulation are vital steps in addressing this issue.
  3. Intellectual property and plagiarism: Generative AI’s ability to generate content raises concerns about intellectual property rights and plagiarism. It is necessary to establish clear guidelines and regulations to protect original works, prevent unauthorized duplication and attribute credit to creators.
  4. Bias and fairness: Generative AI algorithms may learn from existing data biases. It is crucial to ensure that training data is representative and diverse and that algorithms are regularly audited to mitigate bias and promote fairness.
  5. Employee displacement and socioeconomic impacts: The automation capabilities of generative AI may lead to job displacement. It is important to consider the potential consequences on the workforce, provide opportunities for retraining and upskilling and develop strategies to address the potential inequalities.

Generative AI is the key to seamless human-machine synergy

As large language models (LLMs) evolve, generative AI will become more accurate and encompassing. GPT has already made a resounding landfall in the digital space and is being extensively used for customer support and enhanced CX. For instance, HCLTech has integrated generative AI into —a domain-intensive, role-based, single-UI platform. The integration is setting new benchmarks for streamlined workflows, enhanced collaboration and informed decision-making.

As such technological innovations contribute to the progress of GPT and other generative AI systems, we can only expect to do more for customers in less time. However, there are a few considerations:

  1. Identifying customer needs before implementation will help prioritize use cases and tailor the implementation accordingly
  2. Clearly defining goals and objectives for implementing generative AI in customer service will guide the implementation strategy — focusing on the identified customer need and then strategizing how this technology will help (rather than the other way around) is a critical step
  3. Selecting the right generative AI model by considering language capabilities, training data and customization options will help align better with requirements
  4. Training the generative AI model to improve its accuracy and relevance while fine-tuning to allow for industry-specific customization is paramount
  5. Continuously evaluating the performance of the generative AI system will help analyze any issues or gaps and improve the system iteratively
  6. Ensuring compliance with data protection regulations and prioritizing customer data privacy with robust security measures is non-negotiable

Enterprises need to begin by deploying generative AI across test cases internally to analyze the teething issues and immediate impacts. This will let them roll it out on a larger scale with minimum friction and utmost preparedness. Due diligence plays an important role in establishing the best practices and the most useful processes while also addressing strategic and ethical considerations. Transparency and security are key elements for enterprises to ensure while deploying generative AI systems as they contribute heavily to winning the trust of their customers.

In the end, it is important to understand that technology like generative AI cannot be the only source of offering competitive differentiation, considering it is available to everyone. Value will be generated when it complements your existing offerings, provides customers a channel and personalized view to better address their needs and makes their conversations with your organization more engaging.

Leading the future of generative AI and customer experiences with HCLTech

At HCLTech, we believe that the future of customer experience lies at the intersection of human expertise and AI capabilities. Our pursuit of with innovation has led us to explore and develop future-ready customer experience enablers driven by generative AI. These solutions seamlessly integrate with our extensive portfolio of solutions and accelerators, enabling organizations to build

We facilitate meaningful and personalized engagements by powering our CX services with the 3R ecosystem —rethinking customer journeys, restructuring front-line support and redefining customer interactions. This holistic approach enables us to offer real-time insights, understand our customers' needs and provide solutions that not only meet but exceed industry standards. Through genAI labs, we harness the power of advanced machine learning, natural language processing and data analytics to create intelligent solutions that adapt, learn and evolve consistently.

We not only drive technological advancements but also promote ethical and responsible AI practices. We prioritize transparency, data privacy and security in every solution we create.

Share On