6-minute read

Quick summary: Generative AI is enhancing customer portals and other self-service tools through advanced interactive capabilities.

Since the first version of ChatGPT brought generative AI to the forefront of public consciousness in late 2022, the Gen AI revolution has been reshaping the landscape of business operations. Nowhere is this more evident than in reimagining the customer experience. In a recent Gartner poll, executives ranked customer experience No. 1 on their list of focus areas for generative AI investments—ahead of revenue growth, cost optimization, and business continuity.

The emphasis on personalization and self-service, across all channels, is of particular note. In Salesforce’s latest State of the Connected Customer survey, 61 percent of respondents expressed a preference for self-service options.

In a previous article, we explored how traditional/conversational AI expands the possibilities for meeting and exceeding customers’ self-service expectations. Today, we’ll delve into how generative AI is raising the bar in unique ways, offering businesses an opportunity to take their customer experience to new levels—with guardrails in place to ensure these technologies are implemented effectively and responsibly. Customers interact with businesses across a variety of channels; here we will focus on portals as they often represent a range of use cases, ripe for self-service capabilities.

In Salesforce’s latest State of the Connected Customer survey, 61 percent of respondents expressed a preference for self‑service options.

Generative AI vs. traditional AI: transforming customer experiences with advanced capabilities

In the developing landscape of customer service technology, understanding the differences between traditional and generative AI is key, especially in the context of self-service portals. Gen AI’s unique advantages play a significant role in empowering businesses to enhance customer experiences and drive greater customer loyalty.

Generating meaningful responses: important context

Traditional AI-powered features in customer portals rely heavily on pre-programmed responses; such solutions are limited by our ability to define the possibilities. These pre-programmed responses, which represent structured data, enable AI to match the customer’s intent with the appropriate response. They are time intensive for the business and implementation teams to define, much like an FAQ document. This traditional approach, while effective for predictable queries, limits the scope of customer interactions to situations we can plan for.

In contrast, Gen AI draws from both structured and unstructured data, increasing the AI’s understanding of intent and potential response options exponentially to address both the planned and the unplanned. This capability allows for more nuanced and contextually relevant interactions with customers, transcending the limitations of traditional, closed models.

Generative AI–powered customer experiences also benefit from the large language models (LLMs) that underlie Gen AI’s language-related functions. Applications of traditional AI in customer service technology have relied on natural language processing (NLP), which was limited to basic speech recognition in the context of phone calls and other voice channels. LLMs enable the generation of text that is contextually relevant in addition to being linguistically coherent.

For example, imagine a customer reaching out via a chatbot regarding an issue with a product. Traditional NLP systems might recognize keywords like “problem” and “not working” and generate a standard response, such as “Please reset your device.” However, an LLM-driven chatbot would understand the context better. If the customer says, “I tried to reset my device following the instructions in the manual, but it still won’t start,” the LLM could generate a more nuanced response like, “I see you’ve already attempted a reset. Can you tell me if there were any error messages or lights during the process?” This response not only shows understanding, but also moves the conversation forward in a more constructive way, demonstrating LLMs’ superior ability to interpret and respond to the nuances of human communication.

Handling complex cases

Traditional AI is adept at handling simple to moderately complex situations, which suffices for straightforward customer queries such as checking the status of a shipment. Gen AI brings an enhanced level of sophistication to customer portal interactions. Its improved context awareness and ability to analyze a broader spectrum of data enable it to handle more complex and varied customer interactions. The result is a more dynamic and responsive customer service experience, where the intricacies of each individual case can be successfully addressed.

Personalizing experiences

While traditional AI can offer personalized responses within its pre-programmed limits, generative AI takes personalization a step further. Gen AI can leverage its understanding of subtleties in customer data and behavior patterns to deliver highly personalized, relevant responses. Advanced analytical capabilities enable Gen AI-powered tools to discern and adapt to the unique preferences and needs of each customer based on varied inputs from a broad range of data sources.

While traditional AI has laid the foundation for automated service within customer portals, Gen AI is revolutionizing the field by offering more adaptable, nuanced, and personalized customer experiences.

Enhancing customer interactions: Gen AI in chatbots and IVR systems

Two of the most prominent areas where Gen AI is redefining customer self-service are the enhanced conversational intelligence of virtual assistants, a.k.a. chatbots, and the evolved functionality of interactive voice response (IVR) systems. Both these applications demonstrate how Gen AI is setting new standards in creating more human-like, context-aware, and personalized customer experiences, significantly advancing the quality and effectiveness of customer service interactions.

Chatbots: a new level of conversational intelligence

Large language models (LLMs) significantly enhance virtual assistants’ understanding of context in customer interactions and enable a more human-like conversational tone. Unlike their traditional counterparts, Gen AI-powered chatbots can access and interpret an expansive array of data sources. This capability allows them to handle unique customer situations with unprecedented accuracy and relevance, providing responses that are not only accurate, but also contextually appropriate.

IVR systems: beyond scripted responses

Historically, IVR systems have a well-earned reputation as the channel customers love to hate. In one study, 61 percent of customers affirmed that IVR negatively affects their service experience. Fortunately, generative AI is transforming these systems from rigid, script-based monoliths into dynamic tools for customer engagement in several ways:

  • Natural language understanding (NLU) capabilities within LLMs allow IVR systems to better comprehend callers’ accents, dialects, and word choices to better direct them to the appropriate resources.
  • Gen AI enables IVR systems to provide context-relevant, human-like responses in real time, enhancing the customer experience and shortening the time to resolution.
  • Gen AI-based IVR systems can customize interactions based on a caller’s preferences and call history. Responses are not only contextually accurate, but also tailored to individual customer profiles, elevating the level of personalization.


The use of generative AI in chatbots and IVR systems represents a significant leap forward in customer self-service. By providing more human-like, context-aware, and personalized interactions, Gen AI is setting a new standard in customer engagement and support.

Gen AI-powered chatbots can handle unique customer situations with unprecedented accuracy and relevance, providing responses that are not only accurate, but also contextually appropriate.

 Implementing Gen AI responsibly: essential precautions for protecting customer data

Before integrating generative AI solutions into customer-facing operations, it’s essential to establish robust guardrails for protecting customer data. The following precautions can help ensure not only technical readiness for Gen AI, but also responsible and ethical use of the technology.

Start with internal pilots

Begin by deploying Gen AI solutions in controlled internal environments, allowing teams to identify potential challenges and fine-tune the technology in a low-risk setting. Lessons learned from these pilots can then inform broader implementation strategies, enabling a smooth transition to customer-facing applications.

Establish governance structures

AI governance frameworks ensure fair and ethical use of Gen AI technologies, monitoring how the technology interacts with data and the responses it generates. A strong governance structure will help maintain the integrity of customer interactions and safeguard against biases or inappropriate uses of AI.

Align with data privacy laws

Alignment with all applicable data privacy regulations is non-negotiable. Generative AI systems often deal with vast amounts of sensitive data, making compliance with laws like GDPR, CCPA, and others critical. Organizations must ensure that their use of Gen AI is not only effective, but also legally compliant, respecting customers’ privacy and safeguarding the security of their data.

Additional resources on Gen AI readiness

If you’d like to delve deeper into the intricacies of preparing for generative AI implementation, the following articles provide valuable insights:

A strong governance structure will help maintain the integrity of customer interactions and safeguard against biases or inappropriate uses of AI.

Realizing the potential of Gen AI in customer self-service

The dawn of generative AI ushered in a transformative era in customer service, opening doors to optimized experiences that significantly enhance customer relationships. This technological leap enables new levels of interactivity and personalization, catering to customers’ growing preferences for effective self-service. By integrating Gen AI into customer service portals, organizations can provide the efficient, engaging, and satisfying self-service experiences customers demand, not only boosting customer satisfaction but also enhancing brand perception and driving repeat business.

With the appropriate safeguards in place, customer service organizations can leverage generative AI to elevate their self-service experiences, reaping the benefits of improved customer satisfaction and robust customer relationships. In an increasingly customer-centric business environment, the successful adoption of Gen AI for customer experience represents a significant competitive advantage.

Person reading papers in front of laptop screen

Digital transformation done right

We create powerful custom tools, optimize packaged software, and provide trusted guidance to enable your teams and deliver business value that lasts.

  • RPA
  • Virtual assitants
  • Solution engineering
  • Cloud solutions
  • Enterprise architecure
  • Digital strategy

Like what you see?

Ben Higa

Alexis Greenwood is a Senior Manager in the Logic20/20 Digital Transformation practice. In her experience as a technical project manager and systems analyst, she has enabled change through implementation of many customer-facing solutions.