GenAI for Customer Service and Experience CX AI & Analytics

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.

Based on my conversations with customers, at least 20% to 30% of the calls (and often much higher) received in call centers are information-seeking calls, where customers ask questions that already have answers. However, they can be difficult to find, and customers often don’t have the time or patience to search for them. Unsurprisingly, most customers end up being routed to a human agent, even for relatively simple queries; it’s often too complex to program traditional chat or voice bots to provide the right answer or think of all potential questions someone might ask. With the arrival of generative AI, though, we can see a new and powerful path to contact center modernization that is powered by AI and based in the cloud. Despite having 8 million customer-agent conversations full of insights, the telco’s agents could only capture part of the information in customer relationship management (CRM) systems. What’s more, they did not have time to fully read automatic transcriptions from previous calls.

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.

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.

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.

The quality of service a customer receives typically depends on the knowledge and accessibility of the agent they’re talking to, whose attention may be divided among multiple screens. A generative AI “co-pilot” can support the agent by suggesting the most probable answers to quickly address customer needs. It can even detect emotion in real time and offer recommendations based on a caller’s mood. The quality of coaching continuously improves by leveraging human feedback to reinforce models. And since the learning takes place during calls, not after, quality assurance levels increase as early as on the next call.

This can help accelerate the time it takes to resolve service and support calls, and everything can be handled by a virtual agent from start to finish. When it comes to making communication easier during complex calls, generative AI truly shines. Thanks to multi-modal foundation models, your virtual agents or chatbots can have conversations that include Chat GPT voice, text, images and transactions. With the call companion feature in Dialogflow CX (in preview), you can offer an interactive visual interface on a user’s phone during a voicebot call. Users can see options on their phone while an agent is talking and share input via text and images, such as names, addresses, email addresses, and more.

Work and productivity implications

Whether it’s personalized marketing messages, product suggestions or support responses, the Generative AI customer experience enables businesses to deliver a more personalized and engaging experience, increasing customer satisfaction and loyalty. IBM Consulting™ can help you harness the power of generative AI for customer service with a suite of AI solutions from IBM. For example, businesses can automate customer service answers with watsonx Assistant, a conversational AI platform designed to help companies overcome the friction of traditional support in order to deliver exceptional customer service. Combined with watsonx Orchestrate™, which automates and streamlines workflows, watsonx Assistant helps manage and solve customer questions while integrating call center tech to create seamless help experiences. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. AI can deliver benefits that save time and money, enhance customer experience, and improve efficiency.

Throughout this guide you’ll find statistics, predictions and perspectives to spur thinking on how to pragmatically apply this technology to innovate. However, while most companies have actively explored gen AI’s potential through proofs of concept and early-stage experimentation this past year, Cognizant research shows that many leaders (30%) believe meaningful impact is still years away. Siloed, disconnected systems become an even bigger issue when companies begin investing in AI and generative AI, which is why many companies are reevaluating their technology stack. According to

Accenture’s 2024 Technology Vision report, 95 percent of

executives believe generative AI will compel their organization to modernize their technology architecture.​ Many are turning to trusted platforms. Labor economists have often noted that the deployment of automation technologies tends to have the most impact on workers with the lowest skill levels, as measured by educational attainment, or what is called skill biased.

We find that generative AI has the opposite pattern—it is likely to have the most incremental impact through automating some of the activities of more-educated workers (Exhibit 12). Over the years, machines have given human workers various “superpowers”; for instance, industrial-age machines enabled workers to accomplish physical tasks beyond the capabilities of their own bodies. More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. Pharma companies that have used this approach have reported high success rates in clinical trials for the top five indications recommended by a foundation model for a tested drug.

But the challenge for organizations is how to adopt Generative AI successfully and deliver competitive advantages without exposing themselves to significant risks. Because generative AI can make critical errors, companies must ensure that they are in control of the entire process, from the business challenges they address to the governance that controls the model once it is deployed. A key advantage of a conversational AI platform is its ability to collect and analyze customer data, providing insights into customer behavior and preferences. By analyzing interactions with chatbots, businesses can identify trends, patterns, and areas for improvement, allowing them to make data-driven decisions and optimize their customer service strategies. Building and maintaining customer trust has never been more crucial, especially with AI and the uncertainties that surround it. Customer feedback should guide AI implementation, ensuring solutions are value-driven and truly solve real customer problems.

This blog explores the benefits, navigates the challenges and reveals key tips to leverage the power of Generative AI in transforming customer interactions. Together with Google Cloud’s partners, we’ve created several value packs to help you get started wherever you are in your AI journeys. No matter your entry point, you can benefit from the latest innovations across the Vertex AI portfolio. Also, visit our website to stay updated on the latest conversational AI technologies from Google Cloud.

Member Exclusive: Generative AI Marketing Tools – The New Competitive Advantage

They identify areas for improvement and offer targeted coaching to contact center employees. Maoz reminds us that the combination of AI technologies, automation at scale and real-time data analytics, visualization and reporting are key to improving the customer experience. Maintaining consistent quality in customer interactions is a significant challenge with Generative AI. AI-powered systems sometimes produce inaccurate or irrelevant responses, leading to poor customer experience and potential brand damage. By analyzing and interpreting large volumes of customer data, AI algorithms identify patterns, trends and correlations to provide actionable insights and recommendations. This enables businesses to make informed decisions, optimize their customer experience strategies and allocate resources more effectively, leading to improved performance, competitiveness and success.

This floral subscription company used Generative AI to elevate their Mother’s Day campaign. Master of Code Global, in partnership with Infobip, developed an eCommerce chatbot for this purpose. The bot led customers through a playful quiz, rewarding those who answered correctly with a free bouquet. Winners could then use the intelligent feature to create customized messages for their mothers. This innovative tactic deepened buyer connections with the brand and skyrocketed engagement metrics. The initiative resulted in a 60% quiz completion rate, a 78% prize claim ratio, and 38% of clients opting for generated greetings.

• 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.
• 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.
• Image generators like OpenAI’s DALL-E or the popular Midjourney both return multiple images to any single prompt.
• Unveil the potential of Generative AI to revolutionize the future of customer experience and enhance client satisfaction.

For lower-wage occupations, making a case for work automation is more difficult because the potential benefits of automation compete against a lower cost of human labor. Additionally, some of the tasks performed in lower-wage occupations are technically difficult to automate—for example, manipulating fabric or picking delicate fruits. Some labor economists have observed a “hollowing out of the middle,” and our previous models have suggested that work automation would likely have the biggest midterm impact on lower-middle-income quintiles.

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 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.

According to NewVoiceMedia’s report, it translates to a loss exceeding $75 billion annually. Moreover, 67% of clients are “serial switchers,” readily abandoning brands after a negative incident. Generative AI models predict future behaviors by analyzing current trends, enabling businesses to craft anticipatory marketing strategies.

Ask how they plan to improve SLAs, decrease total cost of ownership, operate faster and otherwise drive more business value for you and other customers. Whether a service provider, a manufacture or raw goods provider, a logistics service or any other company that plays a role in your operations, there is an advantage to engaging early in a dialogue about gen AI. Process automation has long been a popular use-case in our digital world and AI is going to open entire new opportunity spaces here. The debate around automation will continue to be more focused on how regulators will impose limitations on the technology instead of how much potential the technology affords us.

Improved customer experience and more time for human agents to handle complex calls. Instead, you can describe in natural language how to execute specific tasks and create a playbook agent that can automatically generate and follow a workflow for you. Convenient tools like playbook mean that building and deploying conversational AI chat or voice bots can be done in days and hours — not weeks and months. Connecting to these enterprise systems is now as easy as pointing to your applications with Vertex AI Extensions and connectors. Because of the speed at which teams are asked to release software, they need to embed quality earlier in the process.

It can also reveal patterns and insights from large data volumes and inform smart business decisions. Whether a company faces the challenge of a fast-arising sales opportunity or needs to resolve a disappointing customer engagement, generative AI lets them navigate turbulent seas and build lasting, lucrative relationships. CX reaches out to humans with astounding intuition that is personalized, memorable, and influential.

This is really taking their expertise and being able to tune it so that they are more impactful, and then give this kind of insight and outcome-focused work and interfacing with data to more people. I think that’s where we’re seeing those gains in conversational AI being able to be even more flexible and adaptable to create that new content that is endlessly adaptable to the situation at hand. “We know that consumers and employees today want to have more tools to get the answers that they need, get things done more effectively, more efficiently on their own terms,” says Elizabeth Tobey, head of marketing, digital & AI at NICE. Of the organizations that have kick-started their AI experimental journey, most haven’t considered the implications these regulations will have on their final creations.

Whether placing an order, requesting a product exchange or asking about a billing concern, today’s customer demands an exceptional experience that includes quick, thorough answers to their inquiries. Large Language Models can also accelerate responses to public inquiries about historical government department orders. By automating information extraction and interpretation from scanned PDF documents, response times are minimized, errors are reduced, and resource allocation is optimized. This enhances governmental transparency and efficiency in public communication and fosters greater engagement and trust.

They can also respond to visual elements, such as clickable menu options, during the conversation. Instead of hard-coding information, you only need to point the agent at the relevant information source. You can start with a domain name, a storage location, or upload documents — and we take care of the rest. Behind the scenes, we parse this information and create a gen AI agent capable of having a natural conversation about that content with customers. It’s more than “just” a large language model; it’s a robust search stack that is factual and continually refreshed, so you don’t need to worry about issues, such as hallucination or freshness, that might occur in pure LLM bots.

His research focuses on customer strategies and technologies, with an emphasis on the CRM customer service disciplines, collaborative customer strategies, AI and Mobile strategies, and cloud-based CRM applications and analytics. Automating customer service with AI-powered chatbots and virtual assistants yields benefits as discussed earlier, handling customer inquiries smoothly and quickly, improving response times, and reducing the workload on customer service teams. Generative AI customer experience excels in content creation, producing high-quality and relevant content at scale.

Nearly all (94%) of these professionals believe their companies will use generative AI in their future work. Test the unified power of Sprinklr AI, Google Cloud’s Vertex AI, and OpenAI’s GPT models in one dashboard. As you implement generative AI, stay updated on the evolving standards and regulations related to AI ethics and data privacy to ensure compliance. Understand that “Responsible AI” is the intersection of trust, partnership, and integrity between brands, vendors, and consumers.

With increasing dependence on software, the pressure on businesses remains intense, and these problems and disruptions continue. As all companies are learning, work with suppliers to understand their own findings, partnerships and interest areas. By building and deploying AI https://chat.openai.com/ in accordance with best practices where we robustly test before deployment then monitor and improve operations regularly, we can reduce the risk of harm or unintended outcomes. Even at this early stage, the opportunities for generative Al across the enterprise are countless.

Learn how AI is revolutionizing the customer experience in the telecommunications industry. Rather than defining processes for every specific task, you can build these generative AI bots once and deploy them across multiple channels, such as mobile apps and websites. This means that customers can get the answers they need, regardless of how they interact with your organization. Programming a virtual agent or chatbot used to take a rocket scientist or two, but now, it’s as simple as writing instructions in natural language describing what you want with generative AI. With the new playbook feature in Vertex AI Conversation and Dialogflow CX, you don’t need AI experts to automate a task. No matter where you are in your journey of customer service transformation, IBM Consulting is uniquely positioned to help you harness generative AI’s potential in an open and targeted way built for business.

The AI’s iterative learning process allows it to adapt to evolving customer preferences and market trends, ensuring sustained relevance and effectiveness. Coupled with robust security measures and compliance with industry regulations, Startek provides a secure and reliable solution for businesses aiming to enhance their customer service operations with Generative AI. AI-powered chatbots, virtual assistants, and automation tools handle a high volume of customer inquiries and tasks simultaneously, reducing the need for human intervention and speeding up response times.

Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence. For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise.

According to the survey, 17% of banks worldwide have incorporated GenAI into their core business processes, while 11% of insurance companies have integrated GenAI into their core business processes. However, this discrepancy gap is expected to be narrowed significantly with the rapid evolution of GenAI, which will reshape how businesses operate in the coming years. By 2027, Gartner projects that over 50% of the Generative AI models utilized by enterprises will be tailored specifically to an industry or a particular business function. This represents a dramatic increase from the mere 1% of such specialized models in 2023. We need standardized, integrated solutions such as unified coding practices and consistent testing frameworks that prioritize both efficiency and high-caliber code. Standardization helps maintain consistency and reduces errors across different teams and projects.

Being “born into” the gen AI era is far less important than exploration and adoption. Those organizations who pioneer AI—and set the rules early to gain competitive market share from it—will establish what it means to be an AI native. Enterprise organizations, with their robust proprietary data to build upon, have the advantage. Generative video and AR/VR renaissance

With significant advancement in AR/VR technology spearheaded by Meta, Apple and Microsoft, compelling new applications backed by gen AI will launch. With conversational user interfaces (i.e., chat, voice), new visual worlds will be seen.

This enables businesses to streamline their customer service operations, optimize resource allocation and improve overall efficiency, leading to cost savings and increased productivity. The launch of ChatGPT will be remembered in business history as a milestone in which artificial intelligence moved from many narrow applications to a more universal tool that can be applied in very different ways. While the technology still has many shortcomings (e.g., hallucinations, biases, and non-transparency), it’s improving rapidly and is showing great promise. It’s therefore a good time to start thinking about the competitive implications that will inevitably arise from this new technology. Many executives are wrestling with the question of how to take advantage of this new technology and reimagine the digital customer experience?

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.

Among the major technology trends driving business in 2024 and beyond, generative AI is a powerful game-changer. With its ability to streamline, propel, and optimize the Customer Experience (CX), generative AI for customer experience shapes commerce—all the way from hopeful new Etsy retailers to global technology enterprises. Explore the role of generative AI in banking and finance to deliver personalized experiences, and revolutionize customer insights, engagement, and offerings. Data security is a significant concern when implementing Generative AI for customer experience, as AI systems require access to and processing of sensitive customer data, which might be vulnerable to security breaches and cyberattacks. Businesses must address these ethical considerations by implementing transparent AI algorithms, providing clear explanations of AI-generated decisions and recommendations, and adhering to data privacy regulations and guidelines. Additionally, conducting regular ethical reviews and audits of AI systems helps ensure responsible and ethical AI practices in customer experience initiatives.

Instead of looking at Gen AI as a silver bullet that will solve all support issues, use it as part of a broader automation system. Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets. As executives begin to consider the commercial implications for Generative AI technology, many are prioritizing the opportunity for it to elevate customer experience and drive growth.

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.

Google DeepMind’s new AI systems can now solve complex math problems

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 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.

Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics. But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies.

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.

This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. You can foun additiona information about ai customer service and artificial intelligence and NLP. We estimate that generative AI could increase the productivity of the marketing function with a value between 5 and 15 percent of total marketing spending. We estimate that applying generative AI to customer care functions could increase productivity at a value ranging from 30 to 45 percent of current function costs. In this section, we highlight the value potential of generative AI across business functions.

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 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.

Using voice interaction, it suggests personalized actions it can do on your behalf like prepare your shopping in advance, reserve a convenient short-term parking spot, or arrange fast-track service that allows you to speed through airport check-in. Built on a strong generative-AI foundation that provides security, privacy protection, and scale, Capgemini’s robust architecture approach can bring CX use cases to life for any business domain. In “Why consumers love generative AI”, we explore the potential of generative AI as well as its reception by consumers, and their hopes around it.

The avatars are capable of replicating human gestures, micro-expressions and speech patterns, aimed at offering an empathetic and immersive experience. Through the use of advanced AI algorithms, they can react in real time to speech or text, analyse real-time data and understand customer requirements. According to Geoff Lloyd, director of retail at NTT Data UK and Ireland, generative ai customer experience this technology can augment and improve every stage of a customer’s journey, whether via digital receptionists, sales personnel or customer care agents. With a simple text prompt, generative AI empowers experts to do more faster while helping less experienced users accelerate their learning curves to ideate, create, learn, and understand, often in ways we never imagined.

As organizations come to understand the strengths and potential use-cases of gen AI, they also begin to realize the fundamental requirements within their organization for fully leveraging this technology. A much larger context window

Increasing context windows are critical for many enterprise use-cases and will allow for larger, more comprehensive prompts to be passed to models. A much larger context window\r\n Increasing context windows are critical for many enterprise use-cases and will allow for larger, more comprehensive prompts to be passed to models. With the internet and accelerated business digitization, data availability and IT funding expand to drive practical AI applications. When that innovation seems to materialize fully formed and becomes widespread seemingly overnight, both responses can be amplified. The arrival of generative AI in the fall of 2022 was the most recent example of this phenomenon, due to its unexpectedly rapid adoption as well as the ensuing scramble among companies and consumers to deploy, integrate, and play with it.

Early adopters are establishing and quantifying basic use cases—gaining earned media as a result—and most would-be digital leaders are watching with curiosity. Preparing the business for gen AI means getting serious about near-term, safe-guarded adoption with well-integrated monitors and control of usage. Navigate current state

Engage with AI to discuss enterprise structure, performance, code base, etc. Navigate current state\r\nEngage with AI to discuss enterprise structure, performance, code base, etc.

AI algorithms analyze customer data and behavior to generate personalized content, recommendations and interactions that resonate with individual customers. Generative AI enables businesses to deliver tailored and contextually relevant experiences that enhance customer engagement and satisfaction through personalized marketing messages, product suggestions and support responses. As indicated by a report from Adobe, 72% of consumers worldwide express confidence in generative AI’s ability to enhance their customer experience. Generative AI for customer experience is revolutionizing how companies approach customer engagement by automating and optimizing multiple aspects of the customer journey. By analyzing data and understanding customer preferences and behaviors, Generative AI creates customized marketing materials, product recommendations and support responses that resonate with individual customers. This improves the quality of customer interactions and enables businesses to scale their customer experience efforts more efficiently.

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.

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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.