Imagining a new era of customer experience with generative AI

Whatever the vertical, we’re certain that generative AI changes the game; there’s a tremendous amount of value now being unlocked, and the tech landscape is changing in real-time as a result. So enterprises are surging into amazing new customer service apps and clever new lures like easy payment systems. Some businesses, however, are either procrastinating or playing catch-up, with negative consequences.

• Generative AI for customer experience enables businesses to explore new and creative ways to engage with their customers.
• The time to act is now.11The research, analysis, and writing in this report was entirely done by humans.
• The chatbot assists with meal planning and suggests anti-waste solutions, promoting sustainability.
• With the internet and accelerated business digitization, data availability and IT funding expand to drive practical AI applications.

This often starts with defining the KPIs of gen AI solutions (aligned to responsible AI principles) and ensuring that processes, governance and tooling are in place—made possible by LLMOps—to monitor and influence those KPIs. The following two pages provide an introduction to LLMOps but remain too high-level to sufficiently detail the orchestration of people, tooling and processes required to operationalize these practices. Build trust and drive understanding through silo-breaking collaboration and rich communication across users and stakeholders, allowing them to understand AI systems and system outputs within their own, personal context. Unlike the software solutions of the pre-generative AI world, generative solutions cannot be built, tested, and released into an ecosystem without continuous oversight.

Generative AI tools can enhance the process of developing new versions of products by digitally creating new designs rapidly. A designer can generate packaging designs from scratch or generate variations on an existing design. This technology is developing rapidly and has the potential to add text-to-video generation. For example, our analysis estimates generative AI could contribute roughly $310 billion in additional value for the retail industry (including auto dealerships) by boosting performance in functions such as marketing and customer interactions.

While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries. However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). Some of this impact will overlap with cost reductions in the use case analysis described above, which we assume are the result of improved labor productivity.

The economic potential of generative AI: The next productivity frontier

Going well beyond the cost savings of a joint investment, with enriched data, access to more skills and beyond, these partnerships might benefit both parties in dramatic ways when executed well. Consider the role of each key supplier within your service or product delivery and move the discussion beyond what they can do with AI for you. By establishing specific initial goals for a cross-functional pilot project team to pursue, organizations can create disruptive proofs of concept and establish an internal POV. As new products go, any amount of friction (cost, risk, etc.) can have a chilling effect on adoption. But generative AI isn’t simply a new product; it’s a transformative technology that can change the world in striking, progressive ways. The evolved role of quality assurance’s (QA) teams and tooling within the delivery process will be a critical focus area for organizations seeking to deploy LLMOps.

By continuously analyzing customer data and feedback, Generative AI enables businesses to adapt and optimize their strategies as needed, ensuring they always deliver the best possible customer experience. Generative AI could have a significant impact on the banking industry, generating value from increased productivity of 2.8 to 4.7 percent of the industry’s annual revenues, or an additional $200 billion to $340 billion. On top of that impact, the use of generative AI tools could also enhance customer satisfaction, improve decision making and employee experience, and decrease risks through better monitoring of fraud and risk. The growth of e-commerce also elevates the importance of effective consumer interactions. Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Generative AI could have an impact on most business functions; however, a few stand out when measured by the technology’s impact as a share of functional cost (Exhibit 3).

This is largely explained by the nature of generative AI use cases, which exclude most of the numerical and optimization applications that were the main value drivers for previous applications of AI. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to https://chat.openai.com/ autonomous-driving features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Zalando: Tailoring Suggestions in Real-Time

It is also important to ensure you are using generative AI to solve real customer problems — making feedback and transparency with customers critical. AI lacks the ability to fully grasp the nuances and intentions behind complex software architectures, which can lead to suboptimal design choices. Additionally, AI-generated code often suffers from poor documentation and readability, complicating future development and debugging efforts. Automated code generation has also resulted in less rigorous code review processes, increasing the likelihood of undetected errors and vulnerabilities.

Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications. In 2012, the McKinsey Global Institute (MGI) estimated that knowledge workers spent about a fifth of their time, or one day each work week, searching for and gathering information. If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. The latest generative AI applications can perform a range of routine tasks, such as the reorganization and classification of data. But it is their ability to write text, compose music, and create digital art that has garnered headlines and persuaded consumers and households to experiment on their own. As a result, a broader set of stakeholders are grappling with generative AI’s impact on business and society but without much context to help them make sense of it.

At Next ’23, we also launched a CCAI-P “Intelligent Virtual Agent only” option, which gives you a way to access all of our gen AI services with a light touch pipeline from your existing contact center to Google Cloud. This feature allows you to work with whatever infrastructure you have, whether you are on-premises or using a CCaaS platform outside of the Google Cloud partner program. Vertex AI extensions can retrieve real-time information and take actions on the user’s behalf on Google Cloud or third-party applications via APIs. This includes tasks like booking a flight on a travel website or submitting a vacation request in your HR system. We also offer extensions for first-party applications like Gmail, Drive, BigQuery, Docs and partners like American Express, GitLab, and Workday. By clicking the button, I accept the Terms of Use of the service and its Privacy Policy, as well as consent to the processing of personal data.

The Impact of Gen AI on Client Experience

In the first two examples, it serves as a virtual expert, while in the following two, it lends a hand as a virtual collaborator. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy. For use cases aimed at increasing revenue, such as some of those in sales and marketing, we estimated the economy-wide value generative AI could deliver by increasing the productivity of sales and marketing expenditures. As companies rush to adapt and implement it, understanding the technology’s potential to deliver value to the economy and society at large will help shape critical decisions.

We also modeled a range of potential scenarios for the pace at which these technologies could be adopted and affect work activities throughout the global economy. Generative AI tools can draw on existing documents and data sets to substantially streamline content generation. These tools can create personalized marketing and sales content tailored to specific client profiles and histories as well as a multitude of alternatives for A/B testing. In addition, generative AI could automatically produce model documentation, identify missing documentation, and scan relevant regulatory updates to create alerts for relevant shifts. First, they can draft code based on context via input code or natural language, helping developers code more quickly and with reduced friction while enabling automatic translations and no- and low-code tools.

And I think that’s one of the big blockers and one of the things that AI can help us with. They recognize its revolutionary potential to create substantial value and unlock previously unreachable levels of content efficiency, productivity, and customer personalization and engagement. We’re entering new frontiers of customer experience and moving to an era of experience empowerment. We believe the generative AI is a tool that can not only enable efficiency and enhanced creativity, but it can significantly empower both customers and employees.

Real-World Examples of Generative AI in Customer Experience

In the wake of ChatGPT’s emergence, it’s safe to say that every enterprise was abuzz with cautious excitement about the potential of this new technology. While QA automation has become an area of strength for many mature engineering organizations, traditional approaches are insufficient for generative AI. The scope of QA and test automation has changed, with new driving factors to consider for AI-based applications.

With over 900,000 customers in the beta program, users are already experiencing the benefits of tailored driving. Mercedes-Benz is committed to guaranteeing a more intuitive and individualized experience. JPMorgan is taking a strategic leap forward with IndexGPT, a potential ChatGPT-based service. As a result, Chat GPT MetLife has seen a 3.5% increase in first-call resolutions and a 13% boost in consumer satisfaction. The focus on AI-driven empathy ensures customers feel heard and supported from their very initial interaction. This directly improves the customer experience for millennials and thin-file individuals.

It really depends on how things are set up, what the data says and what they are doing in the real world in real time right now, what our solutions will end up finding and recommending. But being able to actually use this information to even have a more solid base of what to do next and to be able to fundamentally and structurally change how human beings can interface, access, analyze, and then take action on data. That’s I think one of the huge aha moments we are seeing with CX AI right now, that has been previously not available. In the ever-evolving landscape of customer experiences, AI has become a beacon guiding businesses toward seamless interactions. While AI has been transforming businesses long before the latest wave of viral chatbots, the emergence of generative AI and large language models represents a paradigm shift in how enterprises engage with customers and manage internal workflows. With Generative AI for CX, we help organizations develop tuned foundation models and help them navigate the complexities smoothly.

It’s no surprise that two-thirds of millennials expect real-time customer service and three-quarters of all customers expect smooth cross-channel customer service. As cost pressures build, simply adding trained employees to handle high volumes of customer service is inefficient. Explore the benefits of AI call center software for improved efficiency, and personalization. Unveil the potential of Generative AI to revolutionize the future of customer experience and enhance client satisfaction. Using the Dialogflow Messaging Client, you can then easily integrate the agent into your website, business or messaging apps, and contact center stack. You can foun additiona information about ai customer service and artificial intelligence and NLP. This provides a quick and easy way to divert a large number of support calls to self-service, with relatively low investment and high customer satisfaction.

In the life sciences industry, generative AI is poised to make significant contributions to drug discovery and development. While we have estimated the potential direct impacts of generative AI on the R&D function, we did not attempt to estimate the technology’s potential to create entirely novel product categories. These are the types of innovations that can produce step changes not only in the performance of individual companies but in economic growth overall. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions.

The Software Industry Is Facing an AI-Fueled Crisis. Here’s How We Stop the Collapse.

This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks. The speed at which generative AI technology is developing isn’t making this task any easier. Support agents can prompt a Gen AI solution to convert factual responses to customer queries in a specific tone. They remember the context of previous messages and regenerate responses based on new input.

Second, such tools can automatically generate, prioritize, run, and review different code tests, accelerating testing and increasing coverage and effectiveness. Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools can review code to identify defects and inefficiencies in computing. While other generative design techniques have already unlocked some of the potential to apply AI in R&D, their cost and data requirements, such as the use of “traditional” machine learning, can limit their application. Pretrained foundation models that underpin generative AI, or models that have been enhanced with fine-tuning, have much broader areas of application than models optimized for a single task.

Generative AI systems can be used to industrialize data collection from a range of sources, including curated market research, real-time customer and competitive behavior, internet scraping and primary user research. Whether structured or unstructured, this data empowers systems to drive a range of automated analysis, summarization and recommendations. Every customer interaction ― whether it’s resolving a banking dispute, tracking a missing package, or filing an insurance claim ― requires coordination across systems and departments. Being required to have multiple interactions before a full resolution is achieved is a top frustration for 41 percent of customers. Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work.

It can take on administrative tasks and liberate staff for higher-value and more fulfilling tasks. This technology uses AI algorithms to analyze customer preferences and behavior to generate personalized visual content, such as product recommendations, personalized advertisements and interactive visual experiences. Visual customization enhances the visual appeal and relevance of content, leading to increased engagement, higher conversion rates and improved customer satisfaction. Generative AI for Customer Experience provides real-time insights into customer interactions and behaviors.

They are also exploring ways to analyze sentiment, tone, and emotion in contact center conversations to provide real-time agent guidance. Learn more about Adobe’s differentiated approach to generative AI – including next-generation customer experiences enhanced by Adobe Sensei GenAI, and our creative co-pilot Adobe Firefly. In each case, generative AI will be critical to reimagining and streamlining content supply chains, enabling brands worldwide to meet customer content demands that have continued multiplying by 2X, 5X, and 10X factors. The Adobe-founded Content Authenticity Initiative (CAI) is one example of an industry-led guardrail. With more than 1,500 members, CAI advocates for open global standards and technologies, including Content Credentials, which provides a digital “nutrition label” for content, empowering consumers to see exactly how generative AI content was made.

“This approach highlights our dedication to technological advancement and enhances our ability to streamline activities and tasks within our stores. We’re committed to further exploring transformative AI applications across our entire organization.” As you engage with your suppliers, consider internal solution opportunities and how supplier data might improve model training and solution delivery. As covered in our section on LLMOps, generative AI development implies systemic changes to the way that software is delivered and supported within organizations.

Generative AI is a powerful tool, catalyzing increased productivity and automating repetitive tasks in development and testing. It also poses potential threats to the foundation of software development, however, and is contributing to the generation of subpar code and heightened vulnerability to security threats. As the innovation potential of generative AI becomes clear to more organizations, the opportunity to create wholly new experiences, services and processes by partnering with suppliers on a joint journey will become compelling for many businesses. Mature LLMOps processes are iterative in nature with observability and automation at their heart. As a continuous cycle, LLMOps allows data intake and learning to regularly impact the solution while automating as much as possible and keeping humans in the loop.

The system saves users time and allows them to quickly determine if an item aligns with their needs. As a co-creative effort, Zalando invites users to provide feedback, actively upgrading the virtual agent. This collaborative approach guarantees the solution continues to iterate alongside client preferences.

It can perform any straightforward mathematical routine faster and more accurately than a human and work at all times. A developer can use this super-fast and precise ability and write applications such as calculating routes, or creating schedules, or measuring and predicting engine performance. While classical computers work with a limited set of inputs, quantum computers are a dimension different. When data are input into the “qubits,” these interact with other qubits, which enables dizzying numbers of calculations to take place simultaneously. Quantum computers save time by narrowing down the range of possible answers to extremely complex problems. It’s possible now for advanced algorithms and machine learning to compose complex musical pieces and model chart-topping hits.

The need for sophisticated governance mechanisms, both from a technological and legal perspective is urgent. Get valuable insights and practical strategies to optimize your contact center operations during open enrollment.

As Generative AI tools advance at an unprecedented pace, it’s no longer a matter of if AI will shape your marketing strategies, but how you can strategically employ it to gain a competitive advantage and enhance the customer journey. FORWARD LOOKING STATEMENTS – THE ODP CORPORATION|This communication may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements are subject to various risks and uncertainties, many of which are outside of the Company’s control. There can be no assurances that the Company will realize these expectations or that these beliefs will prove correct, and therefore investors and stakeholders should not place undue reliance on such statements. As you seek to leverage gen AI to unlock new efficiency, differentiate experiences, maximize quality, find cost-savings and evolve the business model, don’t discount the role your suppliers will play in these improvements.

We have supported multiple organizations on establishing their own innovation lab environments where governance, collaboration and technology enablement are high. These environments become particularly powerful when formed in collaboration with hyperscalers who might provide innovative organizations with access to advanced models, education and specialized tooling. Clear processes and incentives for engagement create a culture where every individual is empowered to protect people, minimize risk and discover spaces of humane value. Whether they’re just browsing or already a loyal customer, the way that people engage with brands throughout the shopping and post-purchase experience is set to dramatically evolve with gen AI. With answers becoming more seamless and appetite for content noise decreasing, customers will expect personal, intuitive, adaptive touch-points that understand and serve their needs. Turning data into human-readable, actionable and contextualized guidance is a major strength of gen AI.

This personalized approach enhances customer satisfaction and loyalty, setting businesses apart in today’s competitive landscape. Generative AI customer experience is a cutting-edge approach that leverages the capabilities of Generative AI to enhance customer interactions and engagement. Unlike traditional customer experience strategies that rely on predefined rules and responses, generative AI customer experience harnesses artificial intelligence’s power to generate real-time personalized and contextually relevant content. This enables businesses to provide more tailored and dynamic customer experiences, increasing satisfaction and loyalty.

As the hype around Gen AI simmers down, it’s vital for businesses to evaluate the real value Gen AI brings to them. Either connect use cases to measurable KPIs or recognize net new revenue created by GenAI in CX. Additionally, leverage these five tips to risk-proof your AI investment and make Generative AI work for you. Generative AI can help them identify micro-segments of users with similar spending habits and socio-economics to introduce features catering to each group.

Ensure your data architecture can support generative AI by being robust and flexible. Generative AI delves into data with pattern recognition capabilities, detecting subtle customer segment behaviors for hyper-accurate audience targeting. They even used ChatGPT 4 to sift through thousands of customer notes, including requests and feedback, allowing them to grasp each customer’s unique style. This analysis enabled them to create more tailored and accurate styling options for their clients. Businesses were limited by static data collection methods, missing the deeper, evolving narratives of customer behavior. There are many surefire use cases of Generative AI in CX with palpable challenges and solutions.

Tied together and you have Generative AI to create art (think about the Cosmopolitan magazine cover last year), articles, video, and an entire conversation that AI can have with a human. There is a new burst of products and companies to perform these feats of AI magic, such as OpenAI’s Dall-E 2 and ChatGPT, Google’s Imagen Video, Stable Diffusion, and many more. These images and text are sufficiently advanced to convince a human that people and not computers create them.

We understand the intricacies of user needs and possess the technical expertise to translate them into successful apps. Let’s work together to elevate your CX and forge enduring relationships with buyers. Integrated services like music streaming, eCommerce, and even payments streamline daily tasks. The company expands the boundaries of AI-driven customer interactions with this unique approach. The solution creates custom routes based on destination, dates, and traveler preferences. The brand’s vast database of reviews and opinions ensures reliable, community-driven recommendations.

It transforms the buying journey from a search-focused task to a personalized, conversational experience. Merchat AI streamlines the process while uncovering items customers might never have found on their own. Overall, such an integration makes secondhand shopping more accessible and appealing. One more example of Generative AI adoption in hospitality is “Jen AI” from a famous cruise line. This playful campaign features a virtual Jennifer Lopez powered by artificial intelligence. The solution allows travelers to create custom invitations, promising a memorable way to gather friends and family.

The potential of technological capabilities in a lab does not necessarily mean they can be immediately integrated into a solution that automates a specific work activity—developing such solutions takes time. Even when such a solution is developed, it might not be economically feasible to use if its costs exceed those of human labor. Additionally, even if economic incentives for deployment exist, it takes time for adoption to spread across the global economy. Hence, our adoption scenarios, which consider these factors together with the technical automation potential, provide a sense of the pace and scale at which workers’ activities could shift over time. A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support.

Here are the types of generative AI in customer experience you can use to level up your business. In every industry, marketers look at the dimensions that are most valued by the customer. In the airline industry, for example, these are often listed as the cost of the flight, the emotional value of the brand to the customer, the availability of flights that interest the customer, and the experience a traveler has in flight.

These solutions will be specifically crafted to tackle the distinctive challenges and opportunities within individual industries and business sectors. As these customized models become more prevalent, they are anticipated to enhance operational efficiency, accuracy, and ingenuity and drive innovation, enabling businesses to harness AI more precisely and effectively. For Instance, especially in taxation, a language model trained on GST laws and regulations can automate the creation of show-cause notices for tax violations. Product design

As multimodal models (capable of intaking and outputting images, text, audio, etc.) mature and see enterprise adoption, “clickable prototype” design will become less a job for designers and instead be handled by gen AI tools.

In another instance, Lloyds Banking Group was struggling to meet customer needs with their existing web and mobile application. The LLM solution that was implemented has resulted in an 80% reduction in manual effort and an 85% increase in accuracy of classifying misclassified conversations. I’m calling on the industry to thoughtfully navigate the balance required to create quality code with human developers working alongside AI-powered tools. By understanding AI’s limitations, developers can capitalize on its strengths while mitigating its risks.

Quality services, smart value, and customer satisfaction are the foundation of loyalty—borne out by the boom in brand membership programs. There’s no shortage of ingenious ways that generative AI can support customer service. Here are examples across key industries that deploy generative AI in their customer service functions.

Foundation models and generative AI can enable organizations to complete this step in a matter of weeks. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products. For example, generative AI can improve the process of choosing and ordering ingredients for a meal or preparing food—imagine a chatbot that could pull up the most popular tips from the comments attached to a recipe. There is also a big opportunity to enhance customer value management by delivering personalized marketing campaigns through a chatbot. Such applications can have human-like conversations about products in ways that can increase customer satisfaction, traffic, and brand loyalty.

This estimate would roughly double if we include the impact of embedding generative AI into software that is currently used for other tasks beyond those use cases. From personalized customer experiences to efficient supply chain management, generative AI is… Rather than relying entirely on big-gen AI models to handle customer support automation tasks, use them as part of a broader automation solution.

Tools like AI-powered virtual assistants are paving the way for a new era of customer and agent experiences. Generative AI-powered capabilities like case summarization save agents time while

improving the quality of case reports for the most critical hand-offs. Post-call summarization helps encapsulate call transcripts right as a call ends, so agents can wrap up inquiries fast and

have more time to manage interactions. However, folding generative AI into the customer service process is proving easier said than done. While a large percentage of leaders have deployed AI, a

third of business leaders cite critical roadblocks that hinder future GenAI adoption, including concerns about user acceptance, privacy and security risks, skill shortages, and cost constraints.

If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. Reetu Kainulainen is the CEO and Co-Founder of

Ultimate, the world’s leading virtual agent platform custom-built for support. Started in 2016, with a global client base far exceeding its Berlin and Helsinki-based roots, the company is transforming how customer service works for brands and customers alike. Reetu is passionate about using AI to scale customer service and – as importantly – to make agents’ careers more rewarding. The capacity for AI tools to understand sentiment and create personalized answers is where most automated chatbots today fail. Its recent progression holds the potential to deliver human-readable and context-aware responses that surpass traditional chatbots, says Tobey.

The IP established through smartly leveraging Generative AI in this space will reshape industries and establish new leaders. It’s built to respond to our prompts—no matter their complexity—and often provides answers that, in a sense, acknowledge this fact. Image generators like OpenAI’s DALL-E or the popular Midjourney both return multiple images to any single prompt. Whether its brand values, ethical considerations, generative ai customer experience situational knowledge, historical learning, consumer needs or anything else, human workers are expected to understand the context of their work—and this can impact the output of their efforts. With generative AI, contextual understanding is often difficult to achieve “out of the box,” especially with consumer tools like ChatGPT. The fundamental strengths of generative AI perfectly mirror its unavoidable weaknesses.

This information is then conveyed to customers automatically without any further training. Business leaders resisted implementing automation solutions in the past because customers found bot-to-human interactions frustrating. Generative AI is a branch of artificial intelligence that can process vast amounts of data to create an entirely new output. Depending on the training data you use (and what you want the AI ​​model to do), this output can be text, images, videos, and even audio content. However, implementing Gen AI in customer service comes with its own set of challenges.