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Transforming the Future of Customer Experiences with Generative AI

Transforming the Future of Customer Experiences with Generative AI

Reimagining the Future of Customer Experience with Generative AI

As part of Solutions Review’s Contributed Content Seriesa collection of contributed articles written by our enterprise tech thought leader community—Byron Fernandez, the Group CIO and EVP at TDCX, explains how generative AI is transforming the future of customer experiences for companies across industries.

It’s nearly impossible to let a day go by without hearing about generative artificial intelligence (AI). Given its potential, companies are hugely interested in using this technology to boost business performance. The applicability of generative AI to businesses across all sectors has also led to Grand View Research’s projection that its market size will reach more than $109 billion by 2030, thanks to an expected compound annual growth rate (CAGR) of 35.6 percent.

Providing higher value and more rewarding jobs for human agents 

AI is well-suited to perform many tasks, such as pattern recognition, information gathering, and responding to frequently asked questions. From the end customer’s perspective, this helps by providing a consistent and efficient response to the query, which leads to a positive customer experience.

However, with advances in AI and generative models, there are increasing areas where we see AI augmenting our human customer experience (CX) specialists in resolving complex customer issues, especially in serving business-to-business (B2B) customers. For example, generative AI can provide more effective self-service customer support on various topics, including being trained to generate personalized responses based on customer data and history. This frees up an agent’s time from queries with more straightforward answers. It gives agents more time to handle complex cases, such as Know-Your-Customer (KYC) checks or helping SMEs with digital advertising campaigns, requiring more critical thinking skills.

The use of human agents for higher-value tasks is similar to other automation trends that our sector has experienced previously, such as workflow automation and voice recognition, and adapted accordingly through the upskilling of human talent. Additionally, as generative AI’s natural language processing capabilities continue to grow and mature, it can help agents carry out their tasks more effectively. One example is providing options on how to respond to customers in a more engaging manner, such as their word choices or how to explain certain concepts more simply.

Generative AI can also empower human agents. I think of it as a dedicated personal coach who can train, coach, and bring new agents up to speed more quickly, wherever they are. This offers companies multiple benefits, from reducing the time to onboard new agents to quickly establishing the CX expertise needed to support new product launches.

Business customers’ needs tend to be less straightforward. They may require customized solutions or face issues that require deeper technical expertise, such as network infrastructure and security issues and optimizing software performance and scalability.

Hyper-personalized experiences for customers 

With generative AI, we can better analyze big data and generate deeper, more granular insights. This will provide us with stronger predictive capabilities and lead to process improvements in CX delivery, such as giving hyper-personalized customer experiences for our clients.

One example is better-matching customers to representatives. This could take several forms, including routing calls to representatives who have either served the customer well before and thus already have an idea of the customer’s preferences, matching the customer to an agent who has a similar personality profile, or routing high-churn risk customers to agents who score high on retention.

With the help of generative AI, companies can document and institutionalize such process improvements more easily. In doing so, they would not have to rely on a service quality team to capture these improvements and update service playbooks periodically.

Productivity gains through process improvements

The speed at which generative AI can process information will lead to productivity gains. For example, generative AI can help employees find information faster to support their problem-solving process. It can also format responses to make them easier for customers to understand and digest.

Another example is the use of AI for speech-to-text. Agents must often multitask, taking notes while speaking to the customer and toggling between systems as they investigate the issue. With AI transcription capabilities, we can remove the need for the agent to take notes, freeing them to provide prompt and incisive resolution to the problem. Hence, such applications also create a better working environment and employee experience.

While generative AI can be a valuable tool in transforming the future of customer experiences, many unknowns remain, particularly regarding data privacy and its ability to handle complex problems. Before any technology is implemented, it is essential to conduct pilot projects and proof-of-concepts. In data security, companies should also perform a data classification before providing any information to an AI. This will help to minimize the potential for breaching various data privacy-related regulations such as the GDPR and Health Insurance Portability and Accountability Act.

Generative AI is certainly a game-changing tool, and for CX, it is revolutionizing how businesses understand and serve their customers. The technology will make the human workforce more efficient and open possibilities for those in the CX sector to transform how customers experience a brand.

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