GenAI for customer support: Explore the Elastic Support Assistant

Generative AI Will Enhance Not Erase Customer Service Jobs

In other cases, generative AI can drive value by working in partnership with workers, augmenting their work in ways that accelerate their productivity. Its ability to rapidly digest mountains of data and draw conclusions from it enables the technology to offer insights and options that can dramatically enhance knowledge work. This can significantly speed up the process of developing a product and allow employees to devote more time to higher-impact tasks.

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. Safely connect any data to build AI-powered apps with low-code and deliver entirely new CRM experiences. Resolve cases faster and scale 24/7 support across channels with AI-powered chatbots. Guide agents with AI-generated suggested offers and actions crafted from your trusted data.

So, this particular segment won’t make exceptions to being attended to AI-powered experiences as long as they work well and have a human in the loop to right the ship if anything goes wrong. This creates situations where it hallucinates nonexistent facts that are based structured to look convincing, just like in the aforementioned case. LoDuca and Schwartz got off with a $5,000 fine, but on a large enough scale, generative AI models can make blatantly misleading claims about your brands, products, and services, especially if there’s no human in the loop. You always need to vet answers, except for basic queries that require linear, straightforward replies. These digital assistants enable end-users and provide customer self-support that provides a better overall customer experience, reduces time-to-resolution, and deflects support tickets. Unlike traditional chatbots that need every detail specified with “if/then” logic, generative AI chatbots and digital assistants can handle basic queries by interpreting them and referencing the data requested against the database it’s trained on.

Additionally, many cloud providers cannot offer the storage space these models need to run smoothly. Gen AI models’ impressive fluency comes from the extensive data they’re trained on. But using such a broad and unconstrained dataset can lead to accuracy issues, as is sometimes the case with ChatGPT. Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets. To track the success of your pilot program, you need to specify customer experience metrics and KPIs to track, such as NPS, CSAT, customer effort score, time-to-resolution (TTR), average handle time, and churn. Some other customers might have reservations, either due to ideological reasons (“AI is taking jobs away!”), wanting to speak to an actual human, or even wanting to play around to get it confused.

What are the challenges of using GenAI in customer service?

Nevertheless, an estimated 75 percent of customers use multiple channels in their ongoing experience.2“The state of customer care in 2022,” McKinsey, July 8, 2022. Neople is the perfect solution for eCommerce brands in their native stage who would like to add customer support services but don’t have the budget to hire agents for the same. The team at Neople understands the need for 24/7 service, which is always active and helps companies offer faster responses. That’s because it trains on company information and integrates seamlessly with the whole tool stack. This approach makes it smarter every time during an interaction and improves customer experience. Some of the key benefits of AI for customer service and support are service team productivity, improved response times, cost reduction through automation, personalized customer experiences, and more accurate insights and analysis.

Fed with design principles, systems and reference designs, these prototype design tools will produce unbiased prototypes best fitting the market data available. The job of designers will be to identify the most promising solutions and refine them. Product design\r\nAs 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. War for talent shifts to war for innovation

As 30% of work hours4 are expected to be directly impacted by AI and resulting automation capabilities, productivity gains will be felt by all.

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. To ready themselves for the road ahead, it is imperative that organizations go beyond provisioning access to public tooling and begin developing their own inside use-cases to drive a business case, spark thinking and lay a foundation for future development. 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. As organizations seek to develop effective generative AI- enabled solutions for internal and external users, defining and enforcing their own LLMOps approach is imperative.

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. As gen AI permeates markets, it’s critical that adaptability be built into the technology and cultural fabric of organizations. New, disruptive intra-industry and extra-industry use-cases will arise frequently in the coming years creating continuous change to navigate.

As noted in our gen AI timeline, there has been an explosion of AI-centric startups born over the past two years—these might be defined as AI natives. These companies focus on AI and, presumably, they have AI built into their operations and culture as well as their product. 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.

How leaders fulfill AI’s customer engagement promise

With generative AI tapping into customer resolution data to analyze conversation sentiment and patterns, service organizations will be able to drive continuous improvement, identify trends, and accelerate bot training and updates. Our analysis captures only the direct impact generative AI might have on the productivity of customer operations. Generative AI improves planning, production efficiency and effectiveness throughout the marketing and sales journey. As the technology gains adoption, asset production cycles will see a marked acceleration with a range of potential new asset types and channel strategies becoming available.

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. Generative AI offers retailers and CPG companies many opportunities to cross-sell and upsell, collect insights to improve product offerings, and increase their customer base, revenue opportunities, and overall marketing ROI. Layering generative AI on top of Einstein capabilities will automate the creation of smarter, more personalized chatbot responses that can deeply understand, anticipate, and respond to customer issues. This will power better informed answers to nuanced customer queries, helping to increase first-time resolution rates.

It enhances efficiency, enables self-service options, and empowers support agents with valuable insights for better customer satisfaction. You can foun additiona information about ai customer service and artificial intelligence and NLP. Improve agent productivity and elevate customer experiences by integrating AI directly into the flow of work. Our AI solutions, protected by the Einstein https://chat.openai.com/ Trust Layer, offer conversational, predictive, and generative capabilities to provide relevant answers and create seamless interactions. With Einstein Copilot — your AI assistant for CRM, you can empower service agents to deliver personalized service and reach resolutions faster than ever.

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”). 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. Instead of manually updating conversation flows or checking your knowledge base, generative AI software can instantly provide that information to customers.

This will allow you to customize and build a solution that is tailored to your specific needs and can be more closely integrated with your internal tools. Just like in the aforementioned legal case, generative AI models can make your support team hopelessly dependent on technology—initially, your experimenting with AI starts innocently enough with tight oversight. But, as your employees get more comfortable with its functionality, it’s easier to share confidential data and not vet AI-generated output. As your business scales internationally, an increasing number of your customer tickets will come in outside normal working hours. Most businesses try to surmount this by hiring a distributed team of customer support managers so that there’s always a live support agent(s) to respond to tickets, but the costs can be prohibitive as you scale.

By creating a messaging flow with an AI chatbot that guides customers through the entire process, you can elevate their experience with onboarding on their favorite channel while easing the workload for customer support agents. Holistically transforming customer service into engagement through re-imagined, AI-led capabilities can improve customer experience, reduce costs, and increase sales, helping businesses maximize value over the customer lifetime. Generative AI translators can help support teams communicate with international customers and localize help resources in their audience’s preferred languages without growing headcount significantly. Here are some of the benefits you can expect when you start integrating generative AI into your support operations. Language models can be trained on (or granted live access to) your product’s database, customer conversations, brand guidelines, customer support scripts, and canned responses to ‘understand’ customers’ needs and resolve their queries. If you’ve had the chance to chat with Bard or another conversation AI tool in the last year, you probably, like me, walked away with a distinct impression that services like these are the future of enterprise technology.

Pedro Andrade is vice president of AI at Talkdesk, where he oversees a suite of AI-driven products aimed at optimizing contact center operations and enhancing customer experience. Pedro is passionate about the influence of AI and digital technologies in the market and particularly keen on exploring the potential of generative AI as a source of innovative solutions to disrupt the contact center industry. The future of generative AI in customer support, while brimming with potential, also has some challenges, especially around privacy and ethics. Personalization is great, but there’s a thin line between being helpful and being intrusive. With a well-trained AI chatbot, you can avoid any inconvenience and frustration because the intelligent chatbot can understand the intent behind a message and offer a conversational response to improve overall customer support experiences. At any time, when it’s most convenient for them, customers can access support, and get answers to their questions through a chatbot.

Hence, customer service offers one of the few opportunities available to transform financial-services interactions into memorable and long-lasting engagements. 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). These examples illustrate how technology can augment work through the automation of individual activities that workers would have otherwise had to do themselves. 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.

If you grant it access to your customer database, an LLM can use customer data, such as purchase history and demographics, to customize help experiences, offers, and follow-ups better than a human agent can. With a sufficiently large trough of data, generative AI-powered support engines can suggest complementary purchases, seasonal gifts, discounts, etc., customized to individual customers. This improves the efficiency of support-related processes and activities, accelerates resolution, and enables SMB to enterprise support teams to manage support ticket queues more effectively. 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. Benioff suggested that the pricing model for Agentforce’s agents could be based on consumption, such as by charging companies based on the number of conversations.

Leaders in AI-enabled customer engagement have committed to an ongoing journey of investment, learning, and improvement, through five levels of maturity. We will also see benefits in field service with generative AI for both frontline service teams and customers. AI-generated guides will help new employees and contractors to onboard quickly and brush up on their skills with ongoing learning resources.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. Since these algorithms are trained on mass amounts of data, it is critical to ensure none of the data contains sensitive information. You then run a risk of the AI revealing this information in responses or making it easier for hackers to gain access to private data. Brands that need a chatbot to handle FAQ use cases on a large scale and offer human-like responses. Account creation or profile registration can be done with an AI chatbot over any messaging channel of your choice. Imagine a lead is interacting with your chatbot, asking some FAQs and is ready to create an account with you.

By using location services and training your AI chatbot accordingly, you can offer customers support on finding local stores, bank branches, pharmacies, etc. Your chatbot can summarize a list of local locations, working hours, time to travel, and other important information all in one conversation. Customers are looking for fast, human-like responses from chatbots, and generative AI can help brands elevate their customer support, if trained and integrated in the right way. Learn how generative AI can improve customer service and elevate both customer and agent experiences to drive better results. We hope this research has contributed to a better understanding of generative AI’s capacity to add value to company operations and fuel economic growth and prosperity as well as its potential to dramatically transform how we work and our purpose in society.

Operating effectively in the era of generative AI requires a reconstruction of the now decades-old digital maturity narrative. We’re entering a post-digital era where every enterprise is digital and what defines leaders is their adaptability—which extends to their definition of maturity, how they operate and what they sell. 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.

The war for technology talent will be reshaped as a war for technology innovation as organizations differentiate with data. War for talent shifts to war for innovation\r\nAs 30% of work hours4 are expected to be directly impacted by AI and resulting automation capabilities, productivity gains will be felt by all. As an integral part of the knowledge base solution, Eddy helps customers find relevant articles in the repository with an assistive search option. What’s more, it specializes in summarizing the information that helps customers find a solution and decide faster.

AI adoption creates new categories of risk that require focused assurance at the enterprise level. Organizations that engage in this transformative technology with this in mind will gain the most from the AI era. It isn’t sentient but it sure does behave in human ways – and that’s what’s so inspiring about this technology.

Zendesk is planning on charging for its AI agents based on their performance, aligning costs with results, the company announced Wednesday. Deploy Einstein Bots to every part of your business, from marketing to sales to HR. Qualify and convert leads, streamline employee processes, and build great conversational experiences with custom bots.

The Dartmouth Workshop (1956) stands as a cornerstone, formally birthing the discipline of Artificial Intelligence. This pivotal gathering catalyzed the exploration of “thinking machines,” an effort that laid the groundwork for machine learning studies and the subsequent emergence of generative models. The Support Assistant can find the needed steps to guide you through the upgrade process, highlighting potential breaking changes and offering recommendations for a smoother experience. Performance tuningYou can query the Support Assistant for best practices on optimizing the performance of your Elasticsearch clusters. Whether you’re dealing with slow queries or need advice on resource allocation, the Assistant can suggest configuration changes, shard management strategies, and other performance-enhancing techniques based on your deployment’s specifics.

Automating repetitive tasks allows human agents to devote more time to handling complicated customer problems and obtaining contextual information. Generative AI can substantially increase labor productivity across the economy, but that will require investments to support workers as they shift work activities or change jobs. Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on the rate of technology adoption and redeployment of worker time into other activities.

With all that investment, support teams have some of the highest attrition rates that can peak at 87.6%, according to this Cresta Insights report. Outsourcing isn’t a better idea either, since you’ll be spending $2,600 to $3,400 per agent per month on contractors. 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.

For example, the life sciences and chemical industries have begun using generative AI foundation models in their R&D for what is known as generative design. Foundation models can generate candidate molecules, accelerating the process of developing new drugs and materials. Entos, a biotech pharmaceutical company, has paired generative AI with automated synthetic development tools to design small-molecule therapeutics.

That was the approach a fast-growing bank in Asia took when it found itself facing increasing complaints, slow resolution times, rising cost-to-serve, and low uptake of self-service channels. Service agents face record case volumes, and customers are frustrated by growing wait times. Often, to manage the case load, agents will simultaneously work on multiple customers’ issues at once while waiting for data from legacy systems to load.

Einstein Copilot uses advanced language models and the Einstein Trust Layer to provide accurate and understandable responses based on your CRM and external data. 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.

Top 10 GenAI tools for Customer Service You Must Explore

Refine those recommendations and manage suggestions in categories like repair, discount, or add-on service. In fact, many companies are already taking concrete steps to reduce the burden on their employees. According to our Customer Service Trends Report 2023, 71% of support leaders plan to invest more in automation to increase the efficiency of their support team. Support reps can build on past interactions with customers to create articles that better respond to their needs. Reps can also use artificial intelligence to expand on a topic, identify gaps in tutorials, and make the information as complete as possible. Now that you know what generative AI is, it’s time to see how the technology can make your customers’ lives easier and your agents’ work more efficient.

Maximize efficiency by making the most out of data and learnings from your resolved cases. Use Einstein to analyze cases from previous months and automate the data entry for new cases, classify them appropriately, and route them to the right agent or queue. Reduce agents’ handle time with AI-assigned fields and help them resolve cases quickly, accurately, and consistently.

Protect the privacy and security of your data with the Einstein Trust Layer – built on the Einstein 1 Platform. Mask personally identifiable information and define clear parameters for Agentforce Service Agent to follow. If an inquiry is off-topic, Agentforce Service Agent will seamlessly transfer the conversation to a human agent. The Backpropagation Algorithm (1986) emerged as a transformative breakthrough, resuscitating neural networks as multi-layered entities with efficient training mechanisms. This ingenious approach entailed networks learning from their own errors and self-correcting – a paradigm shift that significantly enhanced network capabilities.

With so much opportunity and so many questions, it can be hard to know where to start. As you’ll find in our discussion of gen AI readiness later in this guide, what’s key is that organizations begin exploring this technology early to identify their own opportunity spaces, safeguard against disruption and begin building skills. What’s certain is that readying the organization to navigate this AI-enabled world is critical for future business performance—exploring these questions is a key part of that readiness.

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. 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. This success has allowed these drugs to progress smoothly into Phase 3 trials, significantly accelerating the drug development process. Notably, the potential value of using generative AI for several functions that were prominent in our previous sizing of AI use cases, including manufacturing and supply chain functions, is now much lower.5Pitchbook.

Institutions are finding that making the most of AI tools to transform customer service is not simply a case of deploying the latest technology. Customer service leaders face challenges ranging from selecting the most important use cases for AI to integrating technology with legacy systems and finding the right talent and organizational governance structures. Generative AI could still be described as skill-biased technological change, but with a different, perhaps more granular, description of skills that are more likely to be replaced than complemented by the activities that machines can do.

A few years back, the world was bursting with promises about AI transforming contact centers, yet the reality was a long way from meeting the hype. Solutions required significant resources and expensive data scientists to train and generative ai customer support update and oftentimes didn’t work as well as promised. That’s when we started to work on redefining AI in the contact center space—creating an AI-powered contact center platform that wasn’t just buzz, but a tangible game-changer.

While traditional AI approaches provide customers with quick service, they have their limitations. Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. As the 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.

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Our customers are already reaping the benefits, seeing unprecedented improvements in customer experience, along with significant cost reductions and boosts in operational efficiency. This is a new era of automation and intelligence meticulously designed for the contact center. Generative AI for customer service is a new narrative of contact center AI—one where promises meet real-world requirements and innovation defines the future. AI chatbots are an ideal way to enable faster customer support, while keeping that human-touch to the conversation. With generative AI, you can widen the breadth of use cases and FAQ questions that the chatbot can handle, making customer support faster and more convenient than before.

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. Agent Assist is easy to deploy, requires almost no customization work, and operates in a Duet mode with a human agent in the middle — so it’s completely safe. It delivers measurable value across KPIs like agent handling time, CSAT (customer satisfaction score), and NPS (net promoter score).

Here are a few examples they found useful, which might offer ideas on how you can make use of it. Once you’re up and running with your monitoring and alerting, the Observability AI Assistant can help to answer any questions you have about the data you collect. This will involve staying up-to-date with the latest developments in workplace trends and AI technology, as well as adopting a habit of continuous learning and upskilling. We broke down barriers with Industry Experience Clouds—an innovation that pre-designed and integrated AI specifically tailored for various verticals. A key word driven chatbot with defined rules to guide customers through a series of menu options.

That’s why it’s such an attractive first step for gen AI and contact center transformation. As you engage with your suppliers, consider internal solution opportunities and how supplier data might improve model training and solution delivery. In our opening section of this document covering the future of gen AI, we touched on a shift from a war for talent (commonly discussed in the 2010s and pandemic era) towards a war for innovation as all businesses use gen AI to gain efficiency. As covered in our section on LLMOps, generative AI development implies systemic changes to the way that software is delivered and supported within organizations.

Like many companies, at the start of the COVID-19 pandemic, John Hancock contact centers saw a spike in calls, meaning the company needed new ways to help customers access the answers they needed. So they turned to Microsoft to help set up chatbot assistants that could handle general inquiries – thus reducing the total number of message center and phone inquiries and freeing up contact center employees. Whatfix offers a guided adoption solution for support teams and organizations making generative AI a part of their support workflow.

Generative AI can also help streamline business processes to make customer support agents more efficient at their job. For example, a customer has been interacting with a chatbot but must be transferred to an agent for further support. AI can help summarize the customer’s conversation with the chatbot so the agent can quickly get contextualized information and avoid asking the customer repetitive questions. This makes their job easier and improves customer satisfaction with your support service. To achieve the promise of AI-enabled customer service, companies can match the reimagined vision for engagement across all customer touchpoints to the appropriate AI-powered tools, core technology, and data.

• In Samsung’s case, an employee pasted code from a faulty semiconductor database into ChatGPT to ask it for a fix; likewise, another worker shared confidential code with the LLM to help them find a fix for a defective device.
• As all companies are learning, work with suppliers to understand their own findings, partnerships and interest areas.
• Chat with G2’s AI-powered chatbot Monty and explore software solutions like never before.
• Being “born into” the gen AI era is far less important than exploration and adoption.

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, 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 key is to fully disclose when a customer interaction is AI-generated and offer alternatives customers can use if they feel they’re not getting the help they need quickly enough. By comparison, an analysis by SemiAnalysis shows that OpenAI’s ChatGPT costs just $0.36 per answer—and it’ll only get cheaper as newer models that use computing power more efficiently are released. But when customers can’t identify which bracket theirs falls into, they just add it to the general firehose. Categorizing tickets manually can be tedious, especially when coupled with the responsibility of resolving customer issues. To help clients succeed with their generative AI implementation, IBM Consulting recently launched its Center of Excellence (CoE) for generative AI. Vertex AI data connectors help your applications maintain freshness and extend knowledge discovery with read-only access to enterprise data sources and third-party applications like Salesforce, JRA or Confluence.

With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors. But they give an indication of the degree to which the activities that workers do each day may shift (Exhibit 8). Based on these assessments of the technical automation potential of each detailed work activity at each point in time, we modeled potential scenarios for the adoption of work automation around the world. First, we estimated a range of time to implement a solution that could automate each specific detailed work activity, once all the capability requirements were met by the state of technology development. Second, we estimated a range of potential costs for this technology when it is first introduced, and then declining over time, based on historical precedents.

The GPT in ChatGPT stands for Generative Pre-trained Transformer architecture, which is a language model capable of understanding natural language and performing related tasks. These tasks include creating text based on a prompt Chat GPT and engaging in a conversation with users. This need culminated in the emergence of Restricted Boltzmann Machines (Late 1990s), a genre of generative models founded on probabilistic modeling and unsupervised learning.

But the same principles can be applied to the design of many other products, including larger-scale physical products and electrical circuits, among others. Generative AI’s potential in R&D is perhaps less well recognized than its potential in other business functions. Still, our research indicates the technology could deliver productivity with a value ranging from 10 to 15 percent of overall R&D costs.

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. Generative AI streamlines and accelerates the provisioning of expert advice to benefit end-users and businesses alike.