Generative AI for Customer Experience: 17 Cases from Global Brands

These AI-driven voice assistants handle a wide range of customer inquiries and tasks, from checking account balances and placing orders to providing real-time support and assistance. Voicebots create a more convenient and hands-free customer experience, allowing customers to engage with businesses anytime, anywhere, using just their voice. By analyzing customer data and behavior, Generative AI creates tailored content and recommendations that resonate with customers.

Now, generative AI increasingly infuses CX with ingenious new capabilities and conveniences that delight and empower customers like no other resource to date. Ethical considerations, such as data privacy, transparency and fairness are crucial when implementing Generative AI for customer experience. Ensuring ethical AI practices and compliance with regulations is essential to maintain customer trust and loyalty.

Generative AI identifies at-risk customers by learning from churn patterns, allowing pre-emptive action to boost customer retention. Product innovation was slowed by a lack of customer-specific insight, resulting in generic, less impactful offerings. For example, Sprinklr AI+ can help you tap into unstructured conversations to map out emerging trends in your market. It helps you filter out positive, negative, and neutral activity around your business and your industry to surface invaluable insights that can be used to build striking marketing campaigns. Conventional marketing methods lacked the capability to adapt to the fluid patterns of customer engagement swiftly. Generative AI often utilizes advanced neural networks like Generative Adversarial Networks (GAN), and Natural Language Processing (NLP) to render natural, highly contextual responses each time you feed it a well-engineered prompt.

Conversational AI combines the capabilities of chatbots, virtual assistants and voicebots to deliver a more seamless and natural conversational experience. These advanced AI systems understand and interpret customer intent, engage in meaningful dialogues and provide contextually relevant responses. Conversational AI enhances the quality and depth of customer interactions, making the customer experience more interactive, engaging and human-like.

Avoid AI for AI’s sake

The retailer introduces a new dimension to the industry with the beta release of its AI-powered assistant. The brand sees Generative AI-inspired fashion as a path to a more customized, engaging shopping experience. Their conversational tool offers clients an innovative way to find outfits that match their unique style and needs.

Despite the hype around gen AI, we’re still in the early days of the AI-driven business. It’s a certainty that AI will transform every corner of our digital universe and yet we’re continuing to learn how. With new applications conceived daily and development of next-gen generative AI models underway, innovators are fast at work reshaping the future of work. As organizations tiptoe into gen AI, linear solution development processes will be favorable for proof-of-concept development at speed.

To avoid this happening, the onus should be on the technology developers themselves. Generative artificial intelligence (AI) has burst into the public consciousness this year, thanks to the launch of ChatGPT in November 2022. In its first six months, it garnered more than 100 million users, while images generated from AI art tool DALL.E were viewed more than 4.2 billion times.

With commercial use cases emerging rapidly, executives will need to consider where generative AI can enrich customer journeys; how it might be integrated and what the potential implications are for employees. The integration of Generative AI in automotive promises to transform how drivers interact with their vehicles. The system Chat GPT analyzes driver choices and behavior to proactively suggest routes based on traffic patterns and daily routines. It even provides personalized news updates or tunes into your favorite entertainment. Seamlessly introduce generative AI into your current tech stack like CRMs, communication channels, analytics tools, etc.

• In countries such as China, India, and Mexico, where wage rates are lower, automation adoption is modeled to arrive more slowly than in higher-wage countries (Exhibit 9).
• The chatbot engages in conversations, recommending products based on user preferences and needs.
• Large Language Models can also accelerate responses to public inquiries about historical government department orders.
• As all companies are learning, work with suppliers to understand their own findings, partnerships and interest areas.
• The rules of engagement continue to rapidly evolve as practical experience refines our thinking on the possible.

Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code. AI trained on these models can perform several functions; it can classify, edit, summarize, answer questions, and draft new content, among other tasks. Smaller language models can produce impressive results with the right training data. They don’t drain your resources and are a perfect solution in a controlled environment.

Behind the scenes, though, gen AI solution development adds layers of complexity to the work of digital teams that go well beyond API keys and prompts. Companies that adopt generative AI at a cultural level, going beyond asset production and chat interactions to elevate all common touch-points for customers and employees alike, will see the biggest gains in the coming years. Employee engagement is an exciting space for gen AI with the potential to impact recruiting, onboarding, team-building, performance management, support and more.

It goes without saying that improved CX boosts customer satisfaction and spurs loyalty and advocacy. Personalization demands that data ensure responsible protection, transparency, and responsibility, not to mention customer comfort—approval that their data is handled responsibly and used only in ways that they condign. Companies owe their customers a rewarding and secure as well generative ai customer experience as personalized experience. For example, safeguarding consumer data against unauthorized access, beach, theft, and misuse is a major concern, as is maintaining the privacy of PII—personal confidential details of consumers. Leaders employing generative AI are responsible for ensuring that their creations don’t have a negative impact on humans, property and the environment.

Take a young company like Runway that is democratizing content creation for web and social media channels. Combining AI with VR/AR creates personalized experiences that surpass what’s possible in the “real” world. The end result is a personalized customer experience, whether exploring a virtual landscape, learning a new skill, or embarking on a game. The engagement is tailored to customer preferences, generating awesome potential for ROI.

Building robust virtual agents with gen AI: Putting it all together

It requires a

single and secure data model to ensure enterprise-wide data integrity and governance. A single platform, single data model can deliver frictionless experiences, reduce the cost to serve, and

prioritize security, exceeding customer expectations and driving profits. Previous generations of automation technology often had the most impact on occupations with wages falling in the middle of the income distribution.

Resource optimization

Sustainability is the challenge of this generation of business. Generative AI can support sustainability efforts by optimizing resources and material mix for minimized waste and environmental friendliness. It can take regulatory processes into account, report on data and even affect subsequent production processes for both software and physical goods.

• The IP established through smartly leveraging Generative AI in this space will reshape industries and establish new leaders.
• To avoid this happening, the onus should be on the technology developers themselves.
• At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence.
• Generative AI is a branch of artificial intelligence that can process vast amounts of data to create an entirely new output.

Despite the promising applications and benefits, organizations face several challenges in implementing GenAI. A significant barrier is the lack of a clear GenAI strategy, with only 9% of leaders familiar with their organization’s adoption of GenAI. Only a tenth of organizations feel fully prepared to comply with upcoming AI regulations. You can foun additiona information about ai customer service and artificial intelligence and NLP. According to SAS study, Early adopters report improved employee experience (89%), cost savings (82%), and higher customer retention (82%). As organizations navigate the complexity of real-world implementations, it becomes crucial to purposefully implement and deliver repeatable and trusted business results from GenAI.

Airlines use advertising, flight crew compensation, good customer service, and operational excellence to meet those customer expectations. Quantum computers are also becoming indispensable for discovering new pharmaceuticals and for helping healthcare organizations run more efficiently and deliver much-needed customer service improvements for people worldwide. With 3.5 quintillion bytes of data generated daily, people are both fascinated and apprehensive about using AI models heavily reliant on user data. Personal and corporate data can inadvertently find its way into generative AI training algorithms, exposing users to potential data theft, loss, and privacy violations. It’s natural for people to gravitate to the familiar, comfortable, and trustworthy brands. Increasing positive experiences through generative AI chatbots and other resources will drive loyalty and consistent purchases over competitors.

An electronics manufacturer aimed to enhance CX and boost sales with a new direct-to-consumer channel. Master of Code Global (MOCG) developed an Apple Messages for Business chatbot with a Gen AI component for their website. It also answers questions accurately and streamlines the purchase process through Shopify integration. Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand, generate, and manipulate human language. They are foundational in generative AI, trained on extensive text data, and excel in tasks like translation, summarization, and answering questions.

Our updates examined use cases of generative AI—specifically, how generative AI techniques (primarily transformer-based neural networks) can be used to solve problems not well addressed by previous technologies. And as it matures, you’ll find new and more advanced use cases and a better way to implement it in your tech stack. However, since it’s new and comes with many challenges and risks, you need to be careful when using it in a customer-facing environment.

Additionally, many cloud providers cannot offer the storage space these models need to run smoothly. Generative AI built into a broader automation or CX strategy can help you deliver faster and better support. Generative AI, the advanced technology behind ChatGPT, Google’s Bard, DALL-E, MidJourney, and an ever-growing list of AI-powered tools, has taken the world by storm. Not knowing if you’ll catch your flight, you open the airport’s app and inquire about available options. Generative AI then quickly assesses various factors such as your airport arrival time and if there’s a chance of a flight delay.

We modeled scenarios to estimate when generative AI could perform each of more than 2,100 “detailed work activities”—such as “communicating with others about operational plans or activities”—that make up those occupations across the world economy. This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. The pace of workforce transformation is likely to accelerate, given increases in the potential for technical automation. Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.

Generate training data

One of the biggest challenges is training the AI ​​models on different datasets to avoid bias or inaccuracy. The AI must also adhere to ethical standards and not compromise privacy and security. We hear a lot about AI co-pilots helping out agents, that by your side assistant that is prompting you with the next best action, that is helping you with answers. I think those are really great applications for generative AI, and I really want to highlight how that can take a lot of cognitive load off those employees that right now, as I said, are overworked. So that they can focus on the next step that is more complex, that needs a human mind and a human touch. And that’s where I think conversational AI with all of these other CX purpose-built AI models really do work in tandem to make a better experience because it is more than just a very elegant and personalized answer.

Overall, the use of Generative AI for personalization mediates a consolidated planning experience and deeper user engagement. While some financial advisors see this as a disruption, JPMorgan envisions it as a way to enhance existing services. The company’s proactiveness positions them as leaders in customer-focused Generative AI solutions for fintech. In less than a year, advances in generative AI have made it a transformational force in creativity and work, redefining the way consumers, schools, and businesses think of everything from image to text generation.

Helvetia also prioritizes transparency and security, addressing the potential for AI-generated errors. This positions the company as a leader in both customer service and the responsible use of Generative AI within the insurance industry. In this article, we will explore how 17 well-known brands have successfully implemented Generative AI for customer experience enhancement. We’ll also determine specific use cases that enabled these organizations to excel within their industries.

By collecting and analyzing customer feedback, the company might find frustrated users because of the chatbot’s inability to handle complex inquiries. In response, the company could train the AI to escalate these inquiries to a human agent more quickly, ensuring a more satisfying customer experience. By making customer-centricity the core of our AI strategies, we build lasting relationships and drive sustained success in an ever-competitive market by consistently delivering value. Generative AI improves planning, production efficiency and effectiveness throughout the marketing and sales journey. As the technology gains adoption, asset production cycles will see a marked acceleration with a range of potential new asset types and channel strategies becoming available. Further, self-service channels will become more personalized and impactful while sales staff will increase their productivity and knowledge to focus more time on driving successful customer engagements.

Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. With their ability to replicate human-like responses, Gen AI tools are the next big thing for companies looking to improve the customer experience. Gen AI-based customer service tools can quickly respond to customer inquiries, provide personalized recommendations, and even generate content for social media. Voicebots leverage the power of natural language processing and speech recognition technologies to enable customers to interact with businesses using voice commands.

Generative AI creates and adapts marketing content in real time, ensuring relevance and resonance with changing customer interests. Here’s what it looks like to create highly targeted, relevant content using the generative model on Sprinklr AI+. They need to understand not just the technology, but the impact on existing processes and in turn the impact on the culture of the enterprise. Generative AI is exceptionally good at sifting through massive user data and interpreting it to benefit a company’s business goals.

You can experience that moment of serendipity, but now, it’s not just luck — it’s by design. The same principles are applied to understand what a person’s emotions are at the moment based on AI analysis of voice, tone, intonation and changes in breathing patterns. A responsible AI framework must ensure that models are fair and unbiased, transparent and explicable with adequate corporate governance and accountability over data and its use. Ethical concerns around generative AI are well known when it comes to copyright conflicts or stolen data, hallucinations, inaccuracies, biases in training data, cybersecurity vulnerabilities, and environmental considerations, among others. Today’s customers are flexing their muscles and showing little mercy to organizations lacking proactive CX agility; the ease with which customers can switch to competitors makes generative AI indispensable.

Tools like Bard, ChatGPT, Jasper, and X’s Grok are prime examples of how LLMs enable sophisticated, human-like interactions with AI. Their reliance on training data can sometimes yield outdated or factually inaccurate output. These training data sets are built from the ocean of information available online to ensure an iterative, creative content production. Consolidate listening and insights, social media management, campaign lifecycle management and customer service in one unified platform. The next step is for the enterprise to develop a plan to bring together the right team to blend Generative AI into existing customer experience programs.

We have connected the customer data, harmonized it into a customer graph, and made it available to all departments in the organization. Enhanced customer experience as customers enjoy shopping and switching among channels for an interesting, stimulating experience. You can also highlight products/services through social media posts; and then provide a more detailed view via blogs. Creating a seamless customer journey requires uniting sales, marketing, services, and other business processes. Customers must be able to switch channels with agility, maintaining a consistent CX as they navigate these touchpoints.

Let’s discover together how AI-amplified solutions can elevate your client support quality to the next level. When it comes to the most important things companies should do when using new generative AI technologies, consumers ranked responsibility #1, with 34% prioritizing actions like having guardrails in place to encourage responsible use. Thirty percent of consumers said it is most important to use generative AI to improve customers’ experiences and 15% prioritized actions that would enhance employees’ experiences, like making work easier and more efficient. Nine percent of respondents said the most important consideration for companies adopting generative AI is that they use it to make the business more financially successful. Now, take that eureka moment and amplify it across every interaction your customers have with your business.

That’s why it’s such an attractive first step for gen AI and contact center transformation. Generative AI is reshaping industries by offering unparalleled efficiency, personalization, and strategic foresight opportunities. For example, generative AI might be used to quickly generate code snippets or automate certain tests, speeding up the development process. A human developer should always review AI-generated code for nuances, integration with other systems, and alignment with the project’s overall architecture, however.

Clara chatbot, powered by Gen AI, takes the online insurance journey to the next level. Consumers enjoy round-the-clock access to simple, informative answers about coverages and pensions. Through the power of a Generative AI-based financial solution, the ZAML platform unlocks credit opportunities for traditionally underserved groups. Its algorithm analyzes a vast array of data and paints a more complete picture of borrower behavior. Empowered by these statistics, let’s now look at a few success stories from leading global brands. We’ll learn how exactly companies are using Gen AI to exalt client engagement and loyalty.

Key insights

With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance. Generative AI tools can facilitate copy writing for marketing and sales, help brainstorm creative marketing ideas, expedite consumer research, and accelerate content analysis and creation. The potential improvement in writing and visuals can increase awareness and improve sales conversion rates.

This leading automotive marketplace introduces a ChatGPT plugin for a conversational search. Shoppers are provided with a more personalized and intuitive way to find their ideal vehicle. Users input prompts, either broad or specific, to receive tailored recommendations directly from the listings.

As new generative AI capabilities continue to become more readily accessible, you might now be wondering where you can apply them within your own organization. Idea generation

The ability of Generative AI applications to work with trained models while evolving those models (and the application’s outputs) with the consumption of real-time data can unlock compelling use-cases for product idea-generation. Rather than relying on surveys and user reviews for qualitative data, Generative AI agents might deliver new concepts frequently based on real-time analytics.

Creating code that drives the apps and software we have all grown accustomed to is a complex and complicated process. This requires a human-centric approach, where developers maintain ownership of the code, validate https://chat.openai.com/ outputs rigorously, and prioritize quality. «We are thrilled about the potential of Gen AI to revolutionize our customers’ experience,» said Gerry Smith, chief executive officer of The ODP Corporation.

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development. In the process, it could unlock trillions of dollars in value across sectors from banking to life sciences.

Generative AI can also help complete the after-call work by generating the follow-up letter, communication, and one-day contract. In other implementations, the Salesforce-owned chat app Slack has integrated ChatGPT to deliver instant conversation summaries, provide research tools, draft messages, and find answers in relation to various projects or topics. Generative AI has the potential to revolutionize the entire customer operations function, improving the customer experience and agent productivity through digital self-service and enhancing and augmenting agent skills. The technology has already gained traction in customer service because of its ability to automate interactions with customers using natural language. Crucially, productivity and quality of service improved most among less-experienced agents, while the AI assistant did not increase—and sometimes decreased—the productivity and quality metrics of more highly skilled agents. This is because AI assistance helped less-experienced agents communicate using techniques similar to those of their higher-skilled counterparts.

Customer service chatbots play a crucial role in automating and optimizing customer interactions, leading to improved satisfaction and efficiency. The market size for generative AI in chatbots is projected to reach approximately USD 1,223.6 million by 2033, up from USD 119.0 million in 2023, with a CAGR of 27% anticipated during the forecast period of 2024 to 2033. For too long, customers have been let down by companies with outdated customer service processes.

According to Gartner, in 2026, generative AI is expected to be integrated into 80% of conversational AI offerings, marking a substantial rise from the 20% seen in 2023. Virtual assistants take the concept of chatbots to the next level by providing more advanced capabilities and personalized experiences. These AI-driven virtual assistants understand context, learn from previous interactions and give more nuanced and tailored customer assistance. From scheduling appointments and managing tasks to offering product recommendations and personalized advice, virtual assistants enhance customer experience by providing intelligent and personalized support.

Generative AI scales the quality of customer interactions and enables businesses to ingeniously and cost-effectively improve CX. To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades.

Taken as a whole, these research findings suggest that generative AI has a bright future with both consumers and brands. Most customers and brand professionals are ready and excited to see generative AI improve products, services, and experiences — now it’s up to brands to harness this technology to deliver on both the possibilities and expectations. Generative AI is a subset of artificial intelligence that specializes in creating unique content by analyzing and learning from extensive data sets. It identifies and replicates complex patterns, styles, and structures from its training data, which allows it to generate new outputs, such as text, images, codes, product designs or audio clips that closely resemble those produced by humans. Relying on NLP, generative AI, and the communication skills of large language models (LLMs) and image generation models, people can now understand requests with keen accuracy and relevance. These abilities make NLP part of everyday life for millions, empowering search engines, and prompting chatbots for customer service via spoken commands, voice-operated GPS systems, and digital assistants on smartphones.

And with increasing demand for great service experiences, companies are being pressured to act

now or risk losing profit. Recent industry research indicates that 69 percent of customers say they’re likely to switch brands based on a poor customer experience and 84 percent say they’re

likely to recommend a brand based on a great customer experience. Quite simply, a great experience can be the difference between lost and loyal customers. As a result, many leaders are turning to

AI and generative AI, recognizing its potential to speed resolution times and reduce friction.

It assists in generating personalized marketing materials, blog posts and social media updates. Generative AI creates compelling content that engages customers and drives meaningful interactions. While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. As the CEO of a global tech company, I understand the immense pressure businesses face to stay competitive, and the subsequent pressure this places on our engineering and product teams.

They want to be doing meaningful work that really engages them, that helps them feel like they’re making an impact. And in this way we are seeing the contact center and customer experience in general evolve to be able to meet those changing needs of both the [employee experience] EX and the CX of everything within a contact center and customer experience. Creating the most optimized customer experiences takes walking the fine line between the automation that enables convenience and the human touch that builds relationships. Tobey stresses the importance of identifying gaps and optimal outcomes and using that knowledge to create purpose-built AI tools that can help smooth processes and break down barriers.