Economic potential of generative AI

Combining generative AI with all other technologies, work automation could add 0.5 to 3.4 percentage points annually to productivity growth. However, workers will need support in learning new skills, and some will change occupations. If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. They can also handle a large volume of queries efficiently and provide more personalized responses over time.

• You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use.
• On top of all that, Fin becomes smarter over time, enabling it to keep up with the forever changing support needs of your customers.
• With conversational user interfaces (i.e., chat, voice), new visual worlds will be seen.
• 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.
• Chat-bots, candidate screening tools, summarizers and picture-makers might inspire us today, but soon AI will shape the core of modern business.

Significant breakthroughs in neural network and generative AI model development, accomplishing previously impossible tasks, alongside surge in big-tech investment. As of Q1 2024, the Crunchbase AI startup list has grown to nearly 10,000 companies2. 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. Executives estimate that 40 percent of their employees
will need new skills in the next three years due to GenAI implementation. Critical to GenAI implementation is upskilling and reskilling agents for the inevitable changes in their roles.

Providing updates for insurance claims, delivery and order statuses can elevate your customer service and ensure your customers aren’t waiting for answers to their queries. Ensuring your refund and return process is smooth is critical to customers repurchasing with you in the future, even if they didn’t keep the product the first time. With an AI chatbot, you can guide customers through the return process, offer updates, and ensure they are satisfied with your services overall.

Sometimes customers need fast support during purchase, and if they can’t get it, you run the risk of them abandoning their order. By utilizing an AI chatbot for customer service you can provide 24/7 instant support for any purchase related needs and questions. Two-thirds of millennials expect real-time customer service, for example, and three-quarters of all customers expect consistent cross-channel service experience.

As they navigate use-cases, seek to answer questions about risks and control and otherwise dive into gen AI, join them. 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. Even at this early stage, the opportunities for generative Al across the enterprise are countless. With the right foundations, the only limitation of gen AI solution-building may be a company’s imagination. Consider the early plugins available for ChatGPT, or bots on the Poe app, and it’s clear that the use -cases of generative AI are about as vast and varied as software itself—and those are just chat interfaces.

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. This analysis may not fully account for additional generative ai customer support revenue that generative AI could bring to sales functions. For instance, generative AI’s ability to identify leads and follow-up capabilities could uncover new leads and facilitate more effective outreach that would bring in additional revenue.

You can train your AI chatbot to understand the intent behind a question, so they can better address and answer the query. An AI assistant is powered by generative AI, and can create various types of content like text, images, audio etc. It allows for a greater volume of FAQ responses and more human-like interactions with users. Appointment booking and management is one of the more popular ways businesses use chatbots for support. Customers can choose their appointment times, cancel, and reschedule as needed without having to wait for an agent. Underpinning the vision is an API-driven tech stack, which in the future may also include edge technologies like next-best-action solutions and behavioral analytics.

Ways to leverage the Support Assistant for your deployments

The current wave of generative models are very powerful, but in a small number of cases, they can generate biased and even harmful outputs, as well as made-up facts (called “hallucinations”). This is why keeping a human reviewer in the loop, whether it’s a service agent or knowledge expert, will be important for the foreseeable future. Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data.

Generative AI built into a broader automation or CX strategy can help you deliver faster and better support. 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. Check out our Next ’23 sessions for Vertex AI Conversation and Contact Center AI to catch more details about all the innovation we’re bringing to you or talk to your Google Cloud sales team to learn more about how you can get value from generative AI today. Also, visit our website to stay updated on the latest conversational AI technologies from Google Cloud.

These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. 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. Rather than relying entirely on big-gen AI models to handle customer support automation tasks, use them as part of a broader automation solution.

Textbook publisher Wiley implemented Agentforce in time for the back-to-school season, when customer service volumes reach their peak. The company reported a double digit percentage increase in customer satisfaction and deflection rates compared to older technology, alongside a 50% increase in case resolution, due to the help of AI agents, according to Benioff. Conversica is a conversational AI that intercepts any stage of the sales funnel and provides support that encourages people to make purchase decisions faster. This revenue digital assistant never leaves your leads behind, allowing you to explore untapped potential sales opportunities hassle-free.

The analyses in this paper incorporate the potential impact of generative AI on today’s work activities. They could also have an impact on knowledge workers whose activities were not expected to shift as a result of these technologies until later in the future (see sidebar “About the research”). The McKinsey Global Institute began analyzing the impact of technological automation of work activities and modeling scenarios of adoption in 2017. At that time, we estimated that workers spent half of their time on activities that had the potential to be automated by adapting technology that existed at that time, or what we call technical automation potential. 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. 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.

I don’t believe that we will immediately see mass human redundancy across customer support roles. You can foun additiona information about ai customer service and artificial intelligence and NLP. After all, people will always be required to cope with unexpected and unique challenges that always occur. I do, however, believe that professionals in the field who prepare themselves for the AI revolution will increase their chances of remaining useful and valued. Generative AI can also be used to draft automated but personalized responses to email inquiries, making sure that messages carry a consistent tone while providing customers with advice relevant to their specific issues. When applied across industries, generative AI’s focus and capabilities facilitate outcomes that seemed futuristic until recently.

How to Intelligently Use Generative AI in Customer Service

Receive AI-generated replies crafted from data from the conversation or from your company’s trusted knowledge base. Enable agents to share these replies with customers with one click, or edit them before sending. Improve search efficiency for agents and customers with AI-powered Search Answers.

Exhibit 1 captures the new model for customer service—from communicating with customers before they even reach out with a specific need, through to providing AI-supported solutions and evaluating performance after the fact. Monty-like Gen AI support and service tools significantly reduce response time and improve response quality, translating to a better customer experience. They’re adept at handling recurring customer queries simultaneously, freeing human support agents to focus on more strategic and complex issues. In fact, ChatGPT is so good that UK energy supplier Octopus Energy has built conversational AI into its customer service channels and says that it is now responsible for handling inquiries. The bot reportedly does the work of 250 people and receives higher customer satisfaction ratings than human customer service agents.

Complete your Customer Service AI solution with products from across the Customer 360.

The challenge is finding the balance of when the right moment is for this transfer to ensure accuracy and maintain customer satisfaction. Generative AI can make communicating with customers around the world easier than ever. It can be trained on multilingual data to provide fast translations for customer queries and responses. That means that brands can provide 24/7 multilingual support to customers anywhere in the world, in an instant.

As new generative AI capabilities continue to become more readily accessible, you might now be wondering where you can apply them within your own organization. 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. By ensuring that model behavior, application performance, data protection and system changes are controlled through a technology-driven workflow, organizations can operate more effectively.

Morgan Chase, Bank of America, and Goldman Sachs have banned internal ChatGPT usage due to the risk of data leaks. On November 30, 2022, OpenAI released ChatGPT, its generative AI large language model powered by GPT-3, into public availability. With CCAI Platform, all the gen AI capabilities mentioned above are available to you from Day 1. 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.

Customers will be able to troubleshoot common issues on their own with knowledge base articles. These tools have the potential to create enormous value for the global economy at a time when it is pondering the huge costs of adapting and mitigating climate change. At the same time, they also have the potential to be more destabilizing than previous generations of artificial intelligence. Generative AI’s ability to understand and use natural language for a variety of activities and tasks largely explains why automation potential has risen so steeply. Some 40 percent of the activities that workers perform in the economy require at least a median level of human understanding of natural language. The growth of e-commerce also elevates the importance of effective consumer interactions.

Leaders must begin now to do the hard work of reinventing jobs and creating the most effective mix of human, automated, augmented, and emergent tasks in the context of the company’s specific business. If you’re going with a pre-integrated generative AI assistant (from Zendesk, Intercom, HubSpot, etc.), you may be able to skip this step since your customer conversations and help library live on the same platform, which your AI assistant has easy access to. While you specify the metrics and KPIs your support team will track, you need to equally set performance benchmarks by studying historical data from previous customer support interactions. It’ll simply reference a support article or a delivery tracking database and offer a straightforward answer. Despite the large corpus of facts and answers it can generate from its training data, LLMs like GPT-4 can’t empathize with customers.

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). Our analysis of 16 business functions identified just four—customer operations, marketing and sales, software engineering, and Chat GPT research and development—that could account for approximately 75 percent of the total annual value from generative AI use cases. The company has partnered with Microsoft to implement conversational AI tools, including Azure Bot Service, to provide support for common customer queries and issues.

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. 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.

Unlike previous deep learning models, they can process extremely large and varied sets of unstructured data and perform more than one task. After training, you’ll need to validate your generative AI assistant in a controlled environment, possibly by opening it up to your internal support agents or a smaller segment of customers. Your goal here is to track the performance metrics (AHT, CSAT, NPS, TTR, churn, etc.), collect live user feedback, and gradually eliminate performance issues. If you’re on a tight timeline, you can block your model from entertaining certain requests completely, editing or refining tone, etc., to make your generative AI assistant more engaging and professional for rollout.

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. The potential for generative AI like ChatGPT to disrupt how humans interact with computers, change how information is retrieved, and transform jobs across industries has left a lot of company leaders scratching their heads. As with other breakthroughs in AI, ChatGPT and similar large language models (LLMs) raise big questions about their impact on jobs and how companies can apply them productively and responsibly. As your generative AI model goes into general availability, you’ll uncover more bugs, errors, and exceptions in the wild. But, you can think of the post-deployment stage as more of an iterative learning process where you observe, refine, and update your generative AI capabilities to fit your agents’ workflows and answer customer queries more accurately. Even when it’s necessary, they treat it like a colonoscopy—the shorter it takes, the better.

Any features or functionality not currently available may not be delivered on time or at all. Give the Support Assistant a try and let us know your thoughts — your feedback will shape its future improvements. Monitoring and alertingThe Support Assistant can help with providing steps for setting up monitoring for your deployment. Whether you need to configure Kibana dashboards or set up alerting for specific events, the Assistant can walk you through the necessary steps, ensuring your deployment remains healthy and issues are flagged promptly. This can be particularly helpful when you aren’t sure where to find a specific error. Instead of searching the Kibana docs for an error that is actually for Elasticsearch, the Assistant can save time by figuring out the appropriate context for you.

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. Affirmative consent and a human-centered, privacy-first approach ensures sensitive data is never used unethically. Unlike the software solutions of the pre-generative AI world, generative solutions cannot be built, tested, and released into an ecosystem without continuous oversight. With the following seven example use-cases of generative AI, we’ll highlight just how varied the opportunity can be. Every part of the value chain across every industry stands to be disrupted in unique, differentiating ways as organizations bring their unique data, processes and POV to the discussion.

This is a prime example of how contact centers will increasingly incorporate generative AI chat and voice tools to deal with straightforward, easily repeatable tasks. And, of course, these tools give customers 24/7 access to support, 365 days a year, via multiple channels (such as phone, online chat, and social media messaging). Botsify is another customer service AI tool that helps you build a seamless customer conversation experience.

Work and productivity implications

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

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. With generative AI, you can empower human agents with in-the-moment assistance to be more productive and provide better service. Neurond Generative AI consulting services support drafting an AI implementation roadmap for your business needs. Based on experiences identifying the potential of scaling your businesses, we analyze the low-hanging fruit use cases to maximize implementation efficiency. Generative AI implementation has been a strategic approach to streamlining the operation system, with the market size worldwide intending to gain $45 billion in 2023, according to Statista.

How can you use AI in customer service?

Based on developments in generative AI, technology performance is now expected to match median human performance and reach top-quartile human performance earlier than previously estimated across a wide range of capabilities (Exhibit 6). For example, MGI previously identified 2027 as the earliest year when median human performance for natural-language understanding might be achieved in technology, but in this new analysis, the corresponding point is 2023. We also surveyed experts in the automation of each of these capabilities to estimate automation technologies’ current performance level against each of these capabilities, as well as how the technology’s performance might advance over time.

In fact, this automation feature of generative AI for customer support can reduce manual tasks. According to Intercom’s State of AI 2023 report, 28% of the respondents say that artificial intelligence https://chat.openai.com/ helped them recap conversations, for example. Fast-forward to 2011, and the Proposal of Generative Adversarial Networks (GANs) by Ian Goodfellow and his collaborators took center stage.

• Gen AI presents a fundamental change in our understanding of what practical, immediately-accessible AI can do.
• From medical professionals to technical support, your AI chatbot can instantly detect the intent of the user and direct them to a professional if they cannot assist with the query.
• Although not intrinsically linked to Generative AI, this notion profoundly shaped the perception of AI’s potential in emulating human-like proficiencies.
• Moreover, this solution easily integrates with multiple communication channels, therefore helping you create an omnichannel solution for the business.
• Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets.

More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. One European bank has leveraged generative AI to develop an environmental, social, and governance (ESG) virtual expert by synthesizing and extracting from long documents with unstructured information. The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables. Software engineering is a significant function in most companies, and it continues to grow as all large companies, not just tech titans, embed software in a wide array of products and services. For example, much of the value of new vehicles comes from digital features such as adaptive cruise control, parking assistance, and IoT connectivity. 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.

They can handle complex customer queries, including nuanced intent, sentiment, and context, and deliver relevant responses. Generative AI can also leverage customer data to provide personalized answers and recommendations and offer tailored suggestions and solutions to enhance the customer experience. To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management.