23 May AI for Customer Service & Support

3 Ways Generative AI Will Reshape Customer Service

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. Nearly seven years ago, Salesforce launched Einstein for Service to give agents AI-powered capabilities. These have included recommended next-best actions and responses to customer inquiries, as well as automating case summarization.

Given the speed of generative AI’s deployment so far, the need to accelerate digital transformation and reskill labor forces is great. 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.

Expanding Service Operations

As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. 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 a result of these reassessments of technology capabilities due to generative AI, the total percentage of hours that could theoretically be automated by integrating technologies that exist today has increased from about 50 percent to 60–70 percent. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities. The modeled scenarios create a time range for the potential pace of automating current work activities.

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. 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. To grasp what lies ahead requires an understanding of the breakthroughs that have enabled the rise of generative AI, which were decades in the making.

This would increase the impact of all artificial intelligence by 15 to 40 percent. 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. The speed at which generative AI technology is developing isn’t making this task any easier.

Automating Post-Call Processing

Such knowledge sources likely include web links, the knowledge base, CRM, and various other customer databases – which may also allow for personalization. Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various Chat GPT knowledge sources looking for the answer. Our innovation strategy sparked the development of a holistic suite of CX AI products, seamlessly integrated and native to our cloud contact center platform. Our goal was to empower our customers to achieve the outcomes that truly mattered to them.

Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets. When you ask your Gen AI solution for a response, it’ll search your help articles to find the right answer. Instead of directing customers to the article, the bot consolidates the required information. It sends precise instructions directly to the customer on how to edit their address – solving their query immediately without any back and forth. Generative AI built into a broader automation or CX strategy can help you deliver faster and better support. Organizations looking to partner with incumbent vendors for generative AI solutions should evaluate the vendor’s roadmap, capabilities, and pricing to ensure that it aligns with their requirements and budget.

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.

Here are some of the benefits you can expect when you start integrating generative AI into your support operations. Generative AI refers to artificial intelligence that creates human-like content from scratch—images, videos, music, and text. The most common applications of generative AI are large language models (LLMs), which use deep learning algorithms to analyze vast amounts of text to learn how human language is structured and generate unique content ‘inspired’ by its training corpus. 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. Eddy also offers detailed analytics data for users to explore customers’ successful and unsuccessful searches.

It leverages strategy documents, brand guidelines, and other assets to build customer questionnaires for review in seconds. The Customers’ Choice conversational AI vendor – as per a 2023 Gartner report – defines an “assertion” as the conditions a bot must meet to pass a test. With this insight, brands can deep dive into how their agents evoke all sorts of emotions and uncover new best practices to coach across the agent population. When a contact escalates, the customer must often repeat their problem and the information they shared with the first agent – which is a common source of customer frustration. Knowing this, they can stay focused on what the customer is saying, not trying to remember what they said previously, which should improve their call handling. Learn even more about how Talkdesk can increase the quality of your Customer Experiences.

Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. 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 have used two complementary lenses to determine where generative AI, with its current capabilities, could deliver the biggest value and how big that value could be (Exhibit 1). Foundation models have enabled new capabilities and vastly improved existing ones across a broad range of modalities, including images, video, audio, and computer code.

The technology could also monitor industries and clients and send alerts on semantic queries from public sources. The model combines search and content creation so wealth managers can find and tailor information for any client at any moment. In addition to improving support operations, generative ai for customer support artificial intelligence in customer service can also benefit marketing, sales, and product strategy. By gaining deeper insights into customers’ needs and behaviors, businesses can anticipate their future needs and make data-driven decisions to improve their overall customer experience.

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. ChatGPT has introduced generative AI to knowledge workers and has started conversations about using generative AI models to automate manual work. This provides endless use cases for customer support challenges, where interactions and requests tend to be repetitive, but with nuance that can be easy to miss. 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.

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. And finally, the entire transformation is implemented and sustained via an integrated operating model, bringing together service, business, and product leaders, together with a capability-building academy. Even before customers get in touch, an AI-supported system can anticipate their likely needs and generate prompts for the agent. For example, the system might flag that the customer’s credit-card bill is higher than usual, while also highlighting minimum-balance requirements and suggesting payment-plan options to offer. If the customer calls, the agent can not only address an immediate question, but also offer support that deepens the relationship and potentially avoids an additional call from the customer later on. While a few leading institutions are now transforming their customer service through apps, and new interfaces like social and easy payment systems, many across the industry are still playing catch-up.

If generative AI could take on such tasks, increasing the efficiency and effectiveness of the workers doing them, the benefits would be huge. In addition to the potential value generative AI can deliver in function-specific use cases, the technology could drive value across an entire organization by revolutionizing internal knowledge management systems. Generative AI’s impressive command of natural-language processing can help employees retrieve stored internal knowledge by formulating queries in the same way they might ask a human a question and engage in continuing dialogue. This could empower teams to quickly access relevant information, enabling them to rapidly make better-informed decisions and develop effective strategies.

But unfortunately, there is a risk of the algorithm generating false responses and presenting them as facts aka AI hallucinations. This can be countered by limiting the scope of the AI model and giving it a specific role so to avoid it generating false responses. The way you train your AI model will impact how accurate the information it generates is, so ensure you invest the needed time and effort to make sure it is as accurate as possible. Generative AI can make communicating with customers around the world easier than ever.

Augmenting Search Functions

IBM Consulting used foundation models to accomplish automatic call summarization and topic extraction and update the CRM with actionable insights quickly. This innovation has resulted in a 30% reduction in pre- and post-call operations and is projected to save over USD 5 million in yearly operational improvements. Integrate data, including Knowledge, from third-party systems to help Agentforce Service Agent generate accurate responses personalized to your customers’ specific needs and preferences. Drive efficiency and boost agent productivity with AI-generated summaries for any work, order, or interaction. Save time by using Einstein to predict or create a summary of any issue and resolution at the end of a conversation.

Support customers and save agents time by making useful information easily accessible. Build a knowledge base with articles on topics ranging from product details to frequently asked customer questions. Increase customer satisfaction and boost service team productivity with AI-generated replies, summaries, answers, and knowledge articles powered by your trusted CRM data natively integrated within the Einstein 1 Platform.

AI has permeated our lives incrementally, through everything from the tech powering our smartphones to 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. The release and timing of any features or functionality described in this post remain at Elastic’s sole discretion. Any features or functionality not currently available may not be delivered on time or at all. Here are a few examples they found useful, which might offer ideas on how you can make use of it.

Gather and analyze relevant customer support data

When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system. That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies. As such, GenAI has made capabilities such as case summarization, sentiment tracking, and customer intent modeling much more accessible and cost-effective. New tools that establish generative AI guardrails, deepen our commitment to help our customers adopt AI in a way that’s simple, safe, and effective. This strategy is not just about mitigating risks; it’s about accelerating the value delivered to our customers.

It enhances efficiency, enables self-service options, and empowers support agents with valuable insights for better customer satisfaction. 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.

Before you launch your generative AI pilot project, you need to specify your goals, the parameters you’ll track to measure success, and a timeframe for your experimentation. Your goals might be to reply to support requests faster, reduce wait times by at least X%, increase customer satisfaction, and enable more customers to resolve issues independently with self-help content. Even when it’s necessary, they treat it like a colonoscopy—the shorter it takes, the better. 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.

Choose the right generative AI tools and platforms

The software accesses the most up-to-date by sifting through your help center, FAQ pages, knowledge base, and other company pages. This information is then conveyed to customers automatically without https://chat.openai.com/ any further training. In this way, generative AI can support the work that human agents do and free them up to focus on more complex customer interactions where they can add the most value.

As a result, many leaders are turning to

AI and generative AI, recognizing its potential to speed resolution times and reduce friction. A recent EY survey asked 1,200 CEOs if they will invest in GenAI and almost 100 percent said

yes. With generative AI layered onto Einstein for Service and Einstein 1, we’ll have the ability to automatically generate personalized responses for agents to quickly email or message to customers. The enhanced relevance and quality of knowledge across the company will make self-service portals and chatbots more valuable, freeing human agents to spend more time deeply engaging on complex issues and building long-term customer relationships. Recently, there has been a lot of buzz around ChatGPT, a generative artificial intelligence (AI) model developed by OpenAI. GPT and other generative AI models like Anthropic and Bard are built on pre-trained, large language models that help users create unique text, images, and other content from text-based prompts.

• This can cause latency issues, where the model takes longer to process information and delays response times.
• 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.
• It allows you to offer 24/7 assistance to your customers, as well as more consistent responses, no matter how high the volume of inquiries becomes.
• Refine those recommendations and manage suggestions in categories like repair, discount, or add-on service.
• Agentforce Service Agent chats with customers using natural language and sophisticated reasoning across self-service portals and messaging channels like WhatsApp, Apple Messages for Business, Facebook Messenger, and SMS.

Features like Call Companion help to supplement voice interactions and make it easier and faster for customers to get answers. 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. 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.

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. But combining Gen AI capabilities with customer support automation is possible if you address and mitigate the following risks and challenges. There’s a ton of potential here but it’s early days and it will get better with time. That said, the one thing I would add is that many Support Orgs I know struggle with significant technical debt when it comes to their own tools.

More recently, computers have enabled knowledge workers to perform calculations that would have taken years to do manually. Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries. Its precise impact will depend on a variety of factors, such as the mix and importance of different functions, as well as the scale of an industry’s revenue (Exhibit 4). 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.

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”). Our second lens complements the first by analyzing generative AI’s potential impact on the work activities required in some 850 occupations. 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 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.

By offloading routine inquiries to AI, support agents can focus on the more engaging and intellectually stimulating aspects of their work. 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. One of the great strengths of generative AI for customer support is its ability to identify which questions can or cannot be answered by the AI itself, filtering out the most complex ones and sending them directly to humans.

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. Guide agents with AI-generated suggested offers and actions crafted from your trusted data. Refine those recommendations and manage suggestions in categories like repair, discount, or add-on 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).

Last, the tools can review code to identify defects and inefficiencies in computing. 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.

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. 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.

Agentforce Service Agent doesn’t require thousands of lengthy structured dialogues. Simply use out-of-the-box templates, existing Salesforce components, and your LLM of choice to get started quickly. Interestingly, the range of times between the early and late scenarios has compressed compared with the expert assessments in 2017, reflecting a greater confidence that higher levels of technological capabilities will arrive by certain time periods (Exhibit 7). An important phase of drug discovery involves the identification and prioritization of new indications—that is, diseases, symptoms, or circumstances that justify the use of a specific medication or other treatment, such as a test, procedure, or surgery. Possible indications for a given drug are based on a patient group’s clinical history and medical records, and they are then prioritized based on their similarities to established and evidence-backed indications.