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  • Companies should begin with off-the-shelf systems for high-value use cases, such as boosting the accuracy of chat channels. Then they should move on to use cases that can offer new products and services to high-value customers, helping them throughout their customer journeys.
  • But be aware that generative AI has been shown to produce the occasional inaccuracy and could unintentionally reveal proprietary information and customer data, so human oversight will likely continue to be required in advanced use cases.
  • Sooner rather than later, generative AI will transform the customer care function itself—and even, possibly, the company business model. Be ready.

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Generative AI

/ article, how generative ai is already transforming customer service.

By  Simon Bamberger ,  Nicholas Clark ,  Sukand Ramachandran , and  Veronika Sokolova

Key Takeaways

Open AI launched ChatGPT less than a year ago, and already companies in every industry are exploring how generative AI can augment the capabilities of their customer care centers.

The large language models (LLMs) upon which ChatGPT and other text-based generative AI applications are built give these apps the power to respond to prompts with human-like text and voice, answering complex questions with seeming ease. The general public has quickly begun testing generative AI’s capabilities, and the technology is rapidly gaining acceptance, lauded for the variety and nature of the responses it provides.

This makes it a natural for customer service operations; indeed, we estimate that the technology, once implemented at scale, could increase productivity by 30% to 50%—or more. And according to a 2022 BCG survey of global customer service leaders, 95% expect their customers to be served by an AI bot at some point in their customer service interactions within the next three years.

Yet questions and challenges remain. LLM-based chatbots are trained on data that may contain intrinsic biases and can generate inaccuracies, a real problem in corporate contexts when an occasional error can result in significant costs to a company’s bottom line and reputation. This explains, in part, why all the current full-scale deployments of generative AI in a customer service setting have some level of human oversight or provide noncritical services, such as offering vacation ideas on travel websites.

But that will change, and soon. Companies looking to incorporate this powerful new technology into their customer care functions need to determine which use cases will deliver the most value and the steps to take to set themselves up for success while avoiding pitfalls. Here, we analyze both the promise and the challenges of generative AI and offer a path forward for companies eager to advance their customer service functions.

The Promise

The contact center—the hub of most customer service operations—has come a long way in the past couple of decades. Tools such as interactive voice response (IVR), agent assist, robotic process automation, and chatbots have already made customer service agents more productive. These technologies have not triggered a significant decline in the number of agents at most contact centers, largely because, as their old tasks were automated, contact center workers took on new roles and responsibilities, such as educating customers on how to use digital services.

But LLMs have the power to significantly expand what can be automated, performing critical customer service tasks that are far beyond the capacities of earlier technologies. These models are trained on vast amounts of data and can recognize, classify, and create sophisticated text and speech with speed and precision.

Driven by a host of enablers—new AI algorithms, increases in computing power, and cheaper cloud-computing infrastructure—emerging AI-powered customer care applications will be able to provide answers and solutions to customers faster and in a much more human-like manner. And when the data on which they are trained is adjusted, these algorithms can be fine-tuned to better suit specific industry and company needs.

Companies are already putting LLMs to work in their customer care centers. For example, this year Octopus Energy, a global specialist in sustainable energy, added generative AI capabilities to its customer service platform to help teams draft rich and thorough email responses more quickly than was previously possible. According to the company, emails drafted by the AI application achieved 18% higher customer happiness scores compared with email responses generated by humans alone. The application already responds to a third of all customer inquiry emails, creating capacity for agents to support more complex, high-growth products like electric vehicles and home electricity generation.

The Path to Maturity

Over time—and likely quite quickly—generative AI will become more and more embedded in the customer service function. (See Exhibit 1.)

generative ai customer service case study

It will soon reach stage 3 of the journey we outline, driving predominantly reactive use cases that will continue to include humans in the loop. It will be able to resolve increasingly complex customer queries. This capability, along with the ability to interact with customers just like a human agent in both tone of voice and responsiveness, will continue to improve the customer experience.

At stage 4, AI will be able to assist customers with most of their queries. Businesses will transition from reacting to queries to proactively solving problems, thereby improving the customer experience even more. AI-enabled assistants will reach out directly to customers, offering preventive solutions to common problems rather than responding to queries after the problem occurs. Traditional AI and predictive analytics will decide on the prompts and the messages to deliver to the customer while generative AI will deliver those prompts and messages in a nonintrusive, human-like, and personalized manner. As confidence in AI-enabled customer contact grows and trained actions become more accurate and bias-free, it will require far less human oversight.

Eventually, at stage 5, AI-enabled support will be available for virtually every user journey. Generative AI could support service bots customized to the specific needs of individual customers, acting as a personal assistant that fully understands customers’ relationship with the company, anticipating their needs and concerns, and interacting with other systems elsewhere in the company to develop a full picture of the customer life cycle.

Companies have yet to proceed through all of these stages, but many are already imagining how a fully AI-enabled customer care center might work.

Overcoming the Challenges

Despite its promise, generative AI has several well-known limitations.

Among the most significant are the occasional factual inaccuracies they might provide. This is a particular danger in the context of customer care, where answers to customers’ questions, offered with a high degree of confidence, might be wrong. Moreover, LLM-based applications can incorporate biases inherent in the data on which the foundational model was trained and in the fine-tuning of models based on specific contexts; such biases could lead to unfair treatment of certain customers. A further risk: the technology could reveal proprietary information and intellectual property or inappropriate customer data.

Currently, the most effective strategy for minimizing these risks is to keep human agents in the loop, checking the content produced by AI before it reaches the customer. Whether to do so will likely depend on the type of customer interaction. Some interactions could be carried out by LLMs independently; other, high-value, premium services will likely require direct human oversight. Some companies are trying to reduce the risk of error by building hybrid tools that use a mix of LLMs and more traditional AI and automation technology to combine the precision of traditional tools with the human-like intimacy of LLMs.

As customer-service applications based on generative AI become more mature, companies will gain confidence in their performance, reducing the need for human oversight and allowing customers to interact with them directly. This will depend on the relevance, quality, and security of the training data; the ability to eliminate inaccuracies and bias; and the specific use case to which the AI is applied. Only when companies have fully understood and managed the risks and limitations of each use case should they move to the next level. They must also work carefully to ensure that as the technology develops, it maintains the “human touch” and capacity for empathy needed to be effective in the customer care context, regardless of the maturity level attained. Finally, they will soon have to understand the implications of their customers’ own increasing AI maturity . (See the sidebar, “Rise of the [Negotiation] Machines.”)

Rise of the (Negotiation) Machines

Decisions, decisions.

In light of generative AI’s benefits, companies are moving to incorporate the technology into customer care at unprecedented speed, despite its risks and challenges.

But how to proceed? That is the critical question. Should companies buy an industry-specific ready-to-use solution or a system from one of the major tech companies offering platforms that incorporate LLM capabilities? Or should they invest time and resources in fine-tuning their own model?

To find the answers to these questions, companies should start now with some relatively simple but high-value use cases that will allow them to test the technology and learn what works and what doesn’t from technical, functional, and business perspectives. Exhibit 2 lays out the variety of use cases across the typical customer service journey—from initial customer contact to final response and resolution—that will likely be augmented by generative AI.

generative ai customer service case study

That’s the approach JetBlue has taken. The US-based airline has partnered with ASAPP, a technology vendor, to implement a packaged generative AI–enabled solution to drive the automation and augmentation of its chat channel, helping its contact center provide customer service. As a result, the contact center has been able to save an average of 280 seconds per chat—which yields a total of 73,000 hours of agent time saved in a single quarter and means that agents have more time to serve customers with complex problems.

Meanwhile, a North American technology company is progressively deploying a generative AI “sidekick” that helps customers and support engineers complete technical support requests, gain access to product information, and automate routine tasks. The initial version of the tool will provide support on the relatively simple requests that make up about 30% of total support tickets, such as how-to guides and basic product configuration information. As the technology matures, the company hopes to expand the range of use cases to cover more complex requests such as fault finding and fixing.

The decision to buy either an off-the-shelf customer-service application based on generative AI and use it without fine-tuning or a foundational LLM and fine-tune it based on an individual organization’s data depends on the complexity of the use case and the industry context.

For general-purpose, nonspecific use cases, fine-tuning is unnecessary; it’s a complex task that doesn’t always ensure greater accuracy and usually requires a major investment in time, money, and technical expertise.

At present, the most suitable approach includes building a customer-facing application based on a combination of traditional AI, such as machine-learning systems, LLMs, and prompt engineering. This final element involves developing and optimizing the information and constraints provided to LLMs to improve the accuracy of the answers—defining company-specific keywords within the prompt itself, for example. This process allows companies to achieve better levels of control, moderation, and personalization.

But in highly regulated industries and in those with heightened data security requirements such as financial services and defense —and generally for more complex and personalized use cases—fine-tuning of existing models will become a more popular option to ensure faster response times to more complex customer requests with greater control of the output.

The recent rapid advances in generative AI are already transforming the ways in which companies manage their critical customer service functions. Now, companies must anticipate how the technology’s considerable capabilities could even more profoundly disrupt their business models.

We predict that today’s large customer service functions—which now typically interact with customers separately from the rest of the business—will become nimble, data-driven organizations that work closely with the rest of the business to create truly differentiating customer experiences. As generative AI systems learn more about a company’s products, operations, and customers, they will likely be able to predict customer behavior and reach out to customers in anticipation of their needs and desires.

As generative AI advances, it may also learn to use such information to reach deeper into other aspects of the business, such as production and resource planning and even working directly with suppliers.

Whatever happens, it will happen fast. Are you ready?

The authors thank Juan Martin Maglione, Marcus Wittig, and Stuart McCann for their contributions to this article.


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How CMOs Are Succeeding with Generative AI

GenAI technology is already enhancing marketing efficiency and productivity. Managed responsibly, it can positively reshape the role and influence of the CMO.

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. They also expect service to be delivered 24/7 across multiple channels.

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.

Generative AI has the potential to significantly disrupt customer service, leveraging large language models (LLMs) and deep learning techniques designed to understand complex inquiries and offer to generate more natural conversational responses. Enterprise organizations (many of whom have already embarked on their AI journeys) are eager to harness the power of generative AI for customer service. Generative AI models analyze conversations for context, generate coherent and contextually appropriate responses, and handle customer inquiries and scenarios more effectively. 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.

How generative AI can disrupt customer service

Generative AI represents a powerful opportunity for businesses to increase productivity, improve personalized support and encourage growth. Here are five exciting use cases where generative AI can change the game in customer service:

  • Conversational search : Customers can find the answers they’re looking for quickly, with natural responses that are generated from finely tuned language models based on company knowledge bases. What’s different is that generative AI can provide relevant information for the search query in the users’ language of choice, minimizing effort for translation services.
  • Agent assistance – search and summarization: Customer support agents can use generative AI to help improve productivity, empowering them to answer customer questions with automatically generated responses in the users’ channel of choice based on the conversation. Generative AI auto-summarization creates summaries that employees can easily refer to and use in their conversations to provide product, service or recommendations (and it can also categorize and track trends).
  • Build assistance: Employees who create chatbots and other customer service tools can use generative AI for content creation and build assistance to support service requests, getting generated responses and suggestions based on existing company and customer data.
  • Call center operational and data optimization: Generative AI can perform the repetitive tasks needed to gather the information needed to enhance the feedback loop within a call center. It can summarize and analyze complaints, customer journeys and more, allowing agents to dedicate more time to customers. The insights produced make evaluating performance improvements for enhanced services much easier, so call centers can contribute to revenue generation.
  • Personalized recommendations: Generative AI considers the history of a customer’s interaction with the brand across platforms and support services to provide them with information that is specific to them (and delivered in their preferred tone and format).

Transforming the contact center with AI

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.

With the roll out of watsonx, IBM’s next-generation AI and data platform, AI is being taken to the next level with three powerful components: watsonx.ai, watsonx.data and the upcoming watsonx.governance. Watsonx.ai is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.data allows scaling of AI workloads using customer data. Watsonx.governance is designed to provide an end-to-end solution to enable responsible, transparent and explainable AI workflows.

To deliver generative AI solutions tailored for contact centers, IBM Consulting works closely with ecosystem partners including Salesforce, Amazon, Genesys, Five9 and NICE to help clients benefit from open source and other technologies.

Generative AI for customer service in action

As part of a multi-phase engagement, Bouygues Telecom has been working with IBM Consulting to transform its call center operations with enterprise-ready generative AI capabilities. Prior to this phase, the European telco engaged with IBM to scale its first four cloud-native AI apps across Amazon Web Services (AWS) cloud , an IBM ecosystem partner.

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. 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 $5 million in yearly operational improvements.

In another instance, Lloyds Banking Group was struggling to meet customer needs with their existing web and mobile application. Within weeks, the IBM team of data scientists, UX consultants and strategy consultants built a proof of concept (POC) to prove that LLMs could improve the virtual assistant  experience by reducing unsuccessful searches, improving virtual assistant performance and personalizing search performance for its customers. 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.

Navigating the challenges of generative AI

In a 2023 study conducted by the IBM Institute of Business Value , 75% of CEOs surveyed believe the organization with the most advanced generative AI will have a competitive advantage. However, these executives are also concerned about navigating risks such as bias, ethics and security.¹

To help clients succeed with their generative AI implementation, IBM Consulting recently launched its Center of Excellence (CoE) for generative AI. It stands alongside IBM Consulting’s existing global AI and automation practice, which includes 21,000 skilled data and AI consultants who have completed over 40,000 enterprise engagements and specialize in helping organizations across every industry adopt and scale AI to detect and mitigate risks, and provide education and guidance.

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.

1. CEO decision-making in the age of AI , IBM Institute for Business Value, July 2023.

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How generative AI is transforming the customer service experience

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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. 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. While there are a ton of use cases where gen AI can unlock value, customer service — specifically the contact center — is an excellent place to start, with low risk, and clear, measurable ROI via increased NPS/CSAT, increased agent productivity, and significant operational savings.

Almost all companies have contact centers or similar customer service channels. The leaders responsible for these operations are constantly on the hunt for ways to improve customer satisfaction, reduce cost, improve query handling times, and identify opportunities for upselling and cross-selling — often amidst tremendous challenges due to legacy infrastructure, staff turnover, and shrinking budgets. 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.

Earlier this year at Next ’23, we shared momentum for our conversational AI offerings and how customers like Gen Digital, Wells Fargo , ING , and Six Flags are adopting Google Cloud’sVertex AI Conversation and Contact Center AI offerings.

Real-time Agent Assist: The natural first step

Many CIOs and CX execs ask us where to start with this transformation. Google Cloud’s Agent Assist is the fastest and safest first step. 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). That’s why it’s such an attractive first step for gen AI and contact center transformation.

Powered by generative AI, Agent Assist makes your human agents faster, more accurate, and overall more productive throughout the agent journey:

  • Live transcription , including PII reduction, allows the agent to focus on the call without worrying about transcribing or note taking.
  • An internal bot in the agent’s console that listens to the conversation and presents to the agent real-time answers and suggestions, helping them learn and respond faster to questions and requests.
  • Summarization provides high quality, structured, and comprehensive summaries, whenever the customer is handed off between human and virtual agents, as well as the end of the call. This saves significant agent handling time, increases CSAT in future calls, and provides data to contact center execs on compliance and business intelligence.
  • Coaching delivers recommendations to live agents after they complete a call about general behavior and opportunities for upsell, cross-sell, and compliance.

We’ll be adding real-time live translation soon, so an agent and a customer can talk or chat in two different languages, through simultaneous, seamless AI-powered translation. We’ll also be offering personalized continuous monitoring and coaching for ALL agents with real time score cards and personalized coaching and training in real time and post-call.


With generative AI, you can empower human agents with in-the-moment assistance to be more productive and provide better service.

Make information seeking a breeze

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. These answers may be on the company's website, in a manual, or in FAQs. 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 Vertex AI Conversation and Dialogflow CX , we’ve simplified this process for you and built an out-of-the-box, yet customizable and secure, generative AI agent that can answer information-seeking questions for you.

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.

Using the Dialogflow Messaging Client, you can then easily integrate the agent into your website, business or messaging apps, and contact center stack. 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.

Connecting to backends and completing actions

Answering questions is extremely useful and provides high ROI. However, in order to truly automate your customer experience, you want your bots to be able to complete actions on behalf of your users, such as checking the status of an order, accessing a bill, updating payment details, scheduling an appointment, and many more. Connecting to these enterprise systems is now as easy as pointing to your applications with Vertex AI Extensions and connectors.

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. These connectors index your application data so you’re always surfacing the latest information to your users.

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.

Extensions and connectors are an open standard, so developers can also upload their own extensions to reuse.

Transform task handling in days

To save you time and costs, Google Cloud’s Contact Center AI already comes with a broad set of pre-built common tasks: authenticate, explain bill, check order status, make payment, and more. However, all companies also have tasks and business logic that are unique to them. We’ve made defining and adding these much easier and faster.

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.

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.

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.

The result? Improved customer experience and more time for human agents to handle complex calls.

Vertex AI Conversation’s playbook feature (also available in Dialogflow CX) lets you use natural language to define what responses and actions you want to enable your voice and chatbots to perform, similar to how you would instruct a human agent on how to handle tasks.

Building robust virtual agents with gen AI: Putting it all together

Building robust virtual agents is now an easy to follow three steps process.


You can learn more about using gen AI virtual agents in contact centers in this free deep-dive webinar video .

Improve complex call handling across virtual and human agents

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 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. They can also respond to visual elements, such as clickable menu options, during the conversation.

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. Watch this demo from our Next ’23 session to see this useful feature in action.

Generating Day 1 value for contact center teams

One of the biggest challenges we hear from customer service leaders is around limitations imposed by their current infrastructure. Last year, we launched the Contact Center AI Platform , an end-to-end cloud-native Contact Center as a Service solution. CCAI Platform is secure, scalable, and built on a foundation of the latest AI technologies, user-first design, and a focus on time to value.

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.


The newly announced “IVA-only” CCAI Platform connects Dialogflow, Insights, and Summarization to your existing contact center infrastructure.

The latest developments in generative AI are pointing to a future where implementation timelines are shrinking for technology adoption, and my team and I are focused on helping customers realize Day 1 value.

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.

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Harnessing the power of Generative AI in Customer Service: Top 9 possible business use cases 

If you asked any customer service professional to sum up their experience of the last couple of years, they would likely respond that it was intense! The budgets have been fluctuating, customer expectations are skyrocketing, and service teams are locked in a perpetual quest: How to achieve more with limited resources? Implementing Artificial Intelligence in customer service  is the right thing to do. With the buzz around   generative AI models built on pre-trained, large language models that generate human-like, unique content based on prompts, it is no wonder that this technology will be game-changing in customer service. 

Meeting the soaring customer expectations  

The landscape of customer service has gone through a seismic shift since the onset of the pandemic. Customers’ expectations have soared to unprecedented heights;  72% of customers  choose businesses offering swift customer service. However, customer service agents are constantly swamped with work. Around 78% of customer service agents grapple with balancing speed and quality, which is higher than 63% in 2020. Unsurprisingly, these pressures have contributed to a notable 19% turnover rate within service organizations. 

While predictive AI has played a role in customer service, the spotlight now shines on generative AI. This cutting-edge technology holds immense potential, arousing curiosity among service professionals and customers. The burning question is: How will generative AI-powered customer service reshape their experiences? Let us understand why adding a layer of generative AI to the existing automation, Artificial Intelligence(AI) , and data analytics in customer service can be transformational for the enterprise, irrespective of the industry. 

Why harness Generative AI in customer service?  

Many customers find bot-to-human interactions disappointing. Business leaders were reluctant to implement automation solutions primarily because of this. Having rigid, rule-bound first-generation bots to serve customers was apprehensive. However, technological strides have transformed the landscape. 

Present-day Gen AI chatbots’ seamless human-like conversations make integrating them into customer-facing operations an intuitive choice. From enhancing the conversational experience to supporting agents with suggested responses, Gen AI ensures swifter and superior support. 

Let us take the instance of Amazon, a popular e-commerce platform that has integrated Gen AI into its customer support operations.  Amazon used Gen AI  to create a chatbot system that understands customer queries and provides instant, precise answers. Training on a vast dataset of historical customer interactions assures the platform a comprehensive knowledge of common inquiries and proper responses. The chatbot system ensured 24/7 availability and empowered customers with swift, relevant, and helpful responses. This led to soaring customer satisfaction and reduced wait times. The implementation also freed human agents for more critical tasks while optimizing operations. 

Best possible Use Cases of Gen AI in Customer Service  

As discussed above, Gen AI-powered chatbots accelerate the process of customer service and elevate the experience. Let us have a comprehensive view of the possible use cases of Gen AI  in customer support. 

Chatbots and Virtual Assistants

Gen AI-powered chatbots and virtual agents can process routine inquiries and tasks intelligently as they deliver round-the-clock customer support while alleviating the workload of human agents.  As per research by  Deloitte , 54% of Gen Z and 56% of millennials in a survey dropped a company because of poor customer service. Notoriously known for ‘cancel culture,’ young consumers do not think twice about walking out on a company if they have a slow resolution time. We can see how effective faster turn-around time and swift resolution, coupled with 24/7 customer service, can be in reducing customer churn. 

Empowering customer self-service

Gen AI can enable customers to address common queries independently through a conversational interface, allowing support teams to focus on complicated concerns. Research shows millennials and  Gen Zs  prefer self-service options rather than talking to an agent and resolving issues. It boosts the customer experience and increases the retention of young consumers.

Sentiment analysis

The technology can be equipped to grasp the customers’ sentiments, moods, and emotions while interacting with them and via feedback. It can then analyze them to recognize potential problems to forestall escalations, enhance satisfaction, and reduce churn rates. 

Predictive assistance

By analyzing the customer data, Gen AI can predict potential forthcoming problems and allow support teams to reach out to the customers, addressing the concerns before they spiral into major issues. 

Tailored support

Based on customer data analysis, Gen AI can also generate tailored content spanning from product suggestions and marketing communications to support replies, aligning with each customer’s unique preferences and needs. Again, Gen Z and millennials love the personalized touch and will be more inclined to engage with the business. 

Real-time language translation

Gen AI-powered chatbots can instantly translate customer queries and support requests. As a result, enterprises can leverage this technology to offer multilingual support to consumers worldwide. With such a seamless support experience, eradicating language barriers can give an enterprise a competitive advantage. 

Knowledge optimization

Gen AI can help craft personalized answers to queries, construct knowledge repositories, and consistently enhance the accuracy and reliability of the information. The machine learning algorithm allows the technology to keep learning and evolving from the substantial data present/generated in customer service operations. 

Enterprises can also leverage this technology to gain insights into best practices and common issues by analyzing historical customer interactions and other customer data. These insights allow the business to enhance the overall quality of customer support. 

Automated ticket classifying and routing

The technology can be trained to evaluate incoming customer requests, recognize the purpose of the request, and route it to the relevant department or customer agent for swift resolution. This can result in enhanced response times and a positive customer experience. 

Creating interactive tutorials

As mentioned earlier, Gen Zs love self-servicing. So, having interactive tutorials and walkthroughs ready for the customers to help them thoroughly comprehend processes can win brownie points for the company. It can be tutorials on how to use the services or products, how to reduce the number of support tickets, how to increase the usage and adoption of the product or service, and much more. 

Possible challenges   

The seamless natural language processing capabilities of Gen AI and the advantage of providing nuanced answers are a giant leap for customer service. However, Gen AI can occasionally generate inaccurate or irrelevant responses. It can err when it comes to ambiguous or complex queries. Yet, continuous learning and feedback can reasonably improve its efficiency. Balancing automation with human involvement is crucial. A human-in-the-loop can complement generative AI by processing complex and sensitive queries while maintaining accuracy and customer satisfaction. 

Take Away  

Generative AI stands as a dynamic force, revolutionizing interactions and elevating experiences in the customer service landscape. It can steer businesses towards a new personalized, efficient customer engagement path. With its advanced capabilities and smooth integration into customer service workflows, Generative AI has rightfully earned all the buzz. While accuracy challenges persist, its undeniable benefits to customer service are clear and indisputable.  

Explore the possibilities of Generative AI for your customer service journey with Saxon’s cutting-edge Gen AI services and engaging workshops, ensuring you stay ahead in this transformative era of  innovation . You can also explore our ‘ Gen AI consulting workshop for Enterprises ‘  to get in-depth knowledge and hands-on training to steer ahead with the power of Generative AI. 

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Joel Jolly

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How to Intelligently Use Generative AI in Customer Service

May 5, 2023

by Reetu Kainulainen

generative AI in customer service

In this post

Why use generative ai in customer service.

  • How to use generative AI in customer service
  • The challenges of using generative AI in customer service

Generative AI, the advanced technology behind ChatGPT, Google's Bard, DALL-E, MidJourney, and an ever-growing list of AI-powered tools, has taken the world by storm. And quite literally.

With its ability to replicate human-like responses, gen AI is the next big thing for companies looking to improve the customer experience . Gen AI-based customer service tools can quickly respond to customer inquiries, provide personalized recommendations, and even generate content for social media. 

A great example of this pioneering tech is G2’s recently released chatbot assistant, Monty , built on OpenAI and G2's first-party dataset. It’s the first-ever AI-powered business software recommender guiding users to research the ideal software solutions for their unique business needs.

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.

However, implementing gen AI in customer service comes with its own set of challenges. One of the biggest challenges is training the AI ​​models on different datasets to avoid bias or inaccuracy. The AI must also adhere to ethical standards and not compromise privacy and security. 

This article discusses how gen AI has tremendous potential in customer service and how businesses can benefit from its ethical implementation.

What is generative AI?

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.

Thanks to accelerating interest and investment in AI generation companies, the market valuation of this sector is expected to reach  $42.6 billion globally in 2023.

Business leaders resisted implementing automation solutions in the past because customers found bot-to-human interactions frustrating. This was a legitimate concern with clunky, rules-based first-generation bots. But tech has come a long way since then.

Gen AI chatbots' advanced ability to converse with humans simply and naturally makes using this tech in a customer-facing environment a no-brainer. From improving the conversational experience to assisting agents with suggested responses, generative AI provides faster, better support.

The focus has now shifted to empowering customer support teams by liberating them from the mundane so they can focus on what truly matters—solving complex issues and providing exceptional service. According to a recent study, customer service teams outlined the benefits of using AI as follows: 36% mentioned continuous 24/7 availability, 31% highlighted time savings and task automation, 30% emphasized faster response to support requests, 28% discussed the balancing act of AI collaborating with human efforts, and 25% underscored AI's role as a strategic ally in effective issue resolution.

How to use generative AI in customer service 

Generative AI built into a broader automation or CX strategy can help you deliver faster and better support. Here's how.

Create more natural conversations

Adding a gen AI layer to automated chat conversations lets your support bot send more natural replies. This saves you from building dialogue flows for greetings, goodbyes, and other conversations.

Pull updated info from your web pages

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

Suppose a customer wants to update the shipping address listed on their account. 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.

Structure support tickets

Gen AI works best when structuring, summarizing, and auto-filling tickets. Not only does this help your support team resolve customer queries faster , but lets them focus on more critical and strategic work. 

Gen AI models can even analyze message sentiment and categorize tickets. Categorized support tickets are easy to work with, allowing you to send tailored responses and prioritize tickets.

Use suggested replies

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.

Generate training data

Gen AI accelerates analytical and creative tasks around training and maintaining AI-powered bots. This helps automation managers, conversation designers, and bot creators work more efficiently, enabling organizations to get more value from automation faster.

Don't have the time to work out every single way a customer might ask for a return? Instead of manually creating this training data for intent-based models, you can ask your gen AI solution to generate it.

Provide sample conversation flows

Even the best writers sometimes hit a wall. In such a case, Gen AI can help break writer's block and encourage creativity by creating response templates for your writers. Writers can use the example flows as inspiration for brainstorming dialog flows.

Read more: What is Generative AI: Synthetic Media, LLMs, and More →

The challenges of using generative AI in customer service 

Generative AI is relatively new. And as with every new development, it has a few quirks to iron out. But combining Gen AI capabilities with customer support automation is possible if you address and mitigate the following risks and challenges.

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 .

Depending on the prompt you provide, generative AI models draw on their training data to offer their best estimate of what you want to hear. Unfortunately, these estimates might not take facts into account. 

Customers who reach out to your support team want accurate responses to resolve their specific issues as quickly as possible. That’s why plugging generative AI straight into your tech stack and letting it loose isn’t a good idea. So how can you ensure generative AI-enabled conversations aren’t derailed?

You don’t want your AI model to make up facts when the data it’s trained on doesn’t contain information about the specific question asked or holds conflicting or irrelevant information. The solution? Creating a system to reshape the AI model. 

Here’s how to keep AI-powered support conversations on track:

  • Optimize the training dataset. When training data, consider quality over quantity. The gen AI model will be connected to your knowledge base in a customer support setting. To get the most value from implementing it, review your knowledge base, remove old or duplicate articles, and feed current and relevant data to the bot.  
  • Ground the model with a search engine. You can steer how your model navigates the knowledge base it’s trained on with a custom internal search engine. This model accesses information relevant to the questions asked and streamlines customer interactions.
  • Introduce fact-checking processes. If you're concerned about AI accuracy, introducing an extra layer of fact-checking into your automation solution will help produce relevant and useful answers. After using the model to generate a conversational reply, you can use another AI model to verify the response before sending it to the customer.

Setting up these guardrails will prevent the bot from sending rogue responses or coming up with an unrelated topic.

Resource use

Gen AI bots require large datasets to train. This makes maintaining them resource intensive and technically challenging.

You can host your own model, but the running costs can quickly add up. Additionally, many cloud providers cannot offer the storage space these models need to run smoothly.

This can cause latency issues, where the model takes longer to process information and delays response times. With 90% of customers stating instant responses as essential, the response speed can make or break the customer experience.

Using a reasonably sized language model is key to reducing resource usage. 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.

“To see the best results with generative AI, we need to think of AI in customer support as not just one neural network, but a whole brain, where different parts of the brain handle different tasks.”   Jaakko Pasanen Chief Science Officer and AI expert at Ultimate

Rather than relying entirely on big-gen AI models to handle customer support automation tasks, use them as part of a broader automation solution.

Be smart and cautious when implementing gen AI in your business 

Generative AI is undoubtedly powerful. 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. 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.

Despite the challenges, gen AI has many benefits for customer service. And as it matures, you'll find new and more advanced use cases and a better way to implement it in your tech stack.

Software buying is now simple, smart, and friendly! Chat with G2's AI-powered chatbot Monty and explore software solutions like never before.

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Reetu Kainulainen photo

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.

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Realize the business value of generative AI in your organization

Top generative ai use cases, improve customer experiences.

Chatbots and virtual assistants Streamline customer self-service processes and reduce operational costs by automating responses for customer service queries through generative AI-powered chatbots, voice bots, and virtual assistants.

Conversational analytics Analyze unstructured customer feedback from surveys, website comments, and call transcripts to identify key topics, detect sentiment, and surface emerging trends.

Personalization Deliver better personalized experiences and increase customer engagement with individually curated offerings and communications.

Boost employee productivity

Employee assistant Improve employee productivity by quickly and easily finding accurate information, get accurate answers, summarize and create and summarizing content through a conversational interface. Learn more

Code generation Accelerate application development with code suggestions based on the developer’s comments and code. Learn more

Automated report generation Generative AI can be used to automatically generate financial reports, summaries, and projections, saving time and reducing errors. Learn more

Enhance creativity & content creation

Marketing Create engaging marketing content, such as blog posts, social media updates, or email newsletters, saving time and resources.

Sales Generate personalized emails, messages based on a prospect's profile and behavior, improving response rates. Generate sales scripts or talking points based on the customer's segment, industry and the product or service.

Product development AI can generate multiple design prototypes based on certain inputs and constraints, speeding up the ideation phase, or optimize existing designs based on user feedback and specified constraints.

Accelerate process optimization

Document processing Improve business operations by automatically extracting and summarizing data from documents and insights through generative AI-powered question and answering. Learn more

Data augmentation Generate synthetic data to train ML models, when the original dataset is small, imbalanced or sensitive.

Supply chain optimization Improve logistics and reduce costs by evaluating and optimizing different supply chain scenarios. Learn more

Explore generative AI use cases for your industry

Ambient digital scribe.

Automatically create transcripts, extract key details, and create summaries from clinician-patient interactions.

Interpret medical images and documentation

Enhance, reconstruct, or even generate medical images like X-rays, MRIs, or CT scans, which can aid in better diagnosis. 

Personalized medicine

Based on a patient's genetics, lifestyle and symptoms, generative AI can create personalized treatment plans.

Intelligent Health Assist

Enable payor agent assist, call report summarization, agent performance assessment for health care insurance providers. 

Automate medical coding

Automate medical coding of medical claims to reduce timeframe for billing, errors, administrative tasks and to meet regulatory and compliance requirements.

Life Sciences

Clinical development.

Analyze large data sets to identify potential adverse drug reactions for both clinical and in-market drugs.

Drug discovery

Use generative AI tools for protein folding, protein sequence design, docking and molecule design to accelerate drug discovery and the design process while reducing costs.

Enhance clinical trials

Augment and accelerate clinical trials by rapidly synthesizing vast amounts of combinatorial trial data, simulating patient populations, and optimizing protocol design.

Automated research reporting

Generate documents or narratives based on drug discovery research dataset, such as proprietary scientific reports. For example, collate phase 1 clinical trials for a candidate therapeutic.

Optimized trial enrollment

Use generative AI to match patients to clinical trials based on inclusion and exclusion criteria. For example, determine if the patient is eligible based on co-morbidities.

Financial Services

Ai-managed portfolios.

Deploy generative AI to create highly tailored investment strategies and portfolios aligned to specific financial goals.

Increase the business value of unstructured content

Create on-demand structured data products (e.g., competitor maps, supply chain relationships, product & service catalogs) from large unstructured data sources such as emails, document repositories, and filings.

Drive product innovation and automate business processes

Use generative AI to develop new tools for end-users, e.g. stock screening using natural language search. Examples include wealth management and brokerage clients and advisors, and institutional investment analysis.

Intelligent advisory

With chatbots and call center assist, firms can automatically translate complex questions from internal users and external customers into their semantic meaning, analyze for context, and then generate highly accurate and conversational responses.

Transform financial documentation

Quickly draft investment research, loan documentation, insurance policies, regulatory communications, RFI, business correspondence, and more. 


Product design optimization.

Generative AI can quickly generate and assess countless design options, helping manufactures find the most optimized, efficient, and cost-effective solutions.

Operational efficiency

Generative AI can simulate production to identify improvements, find hidden insights, validate models with synthetic data, and boost predictive accuracy - all without disrupting operations.

Real-time equipment diagnostics

By ingesting historical data, generative AI can diagnose equipment failures in real time and recommend maintenance actions like input adjustments, repairs, or likely spare parts.

Supply chain traceability

Gain end-to-end traceability of component parts through multi-tier supply chains and identify anomalies or gaps in the supply chain data.

AI-powered maintenance assistants

Generative conversational agents can be trained on product manuals, troubleshooting guides, and maintenance notes to deliver swift technical support to workers, reducing downtimes.

Planogram optimization

Dynamically update planograms based on new products, changing inventory levels, sales trends and competitor data.

Virtual try-ons

Generative AI can synthesize realistic images of people wearing different clothing items, enabling immersive virtual try-on experiences.

Optimize pricing

Continuously run simulations to set optimal pricing for goods based on expiration dates, competition, location, and others.

Product descriptions

Generative AI can automatically generate unique, high-quality product descriptions and listings based on product data.

Personalized recommendations

Analyze customer data to create customized promotions and personalized product recommendations for customers.

Media & Entertainment

Produce high-quality content at scale.

Generate characters, animations, and visual effects tailored to specific themes, genres, or formats.

Optimize subscriber experiences

Create effective, personalized content that adapts in real-time based on user engagement and preference.

Enrich broadcast content

Enhance live broadcast content through automated graphics, speech, and video generation tailored to each program.

Automated highlight generation

For sports, generative AI can detect highlights and automatically generate polished packages and promos.

Automatic content tagging

Use generative AI to auto-tag and index massive media libraries for easier search and recommendation.

Enhance, reconstruct, or even generate medical images like X-rays, MRIs, or CT scans, which can aid in better diagnosis.

Continuously run simulations to set optimal pricing for goods based on expiration dates, competition, location etc.

What leaders need to know about generative AI

Explore how to build generative ai responsibly, an introduction to generative ai with swami sivasubramanian, executive insights: perspectives from leaders at the intersection of business and technology, use cases in action, allen institute uses aws to map the whole human brain, bbva innovates in the financial services industry, omnicom builds the next generation of tools, accenture boosts productivity for their developers, generative ai in your business, light the way for business transformation, aws partner community to accelerate innovation, introduction to generative ai with aws leaders, tips to prepare your organization for generative ai, applying generative ai to product development.

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Three recommendations for where — and how — to deploy this new technology.

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? For value creation to happen, we have to think about large language models as a solution to an unmet need, which requires a precise understanding about the pain points in customer experiences. From finance to healthcare and from education to travel, industry observers expect an explosion of service innovations and new digital user experiences on the horizon.

Since its launch in November 2022, ChatGPT, the chatbot developed by OpenAI, has taken the business world by storm . Following this success, Microsoft has increased its investment in OpenAI and has launched a new version of its search engine Bing that provides users with generated answers in response to searches, as opposed to providing them with thousands of links to choose from. Not surprisingly, Google, as the incumbent in the search engine market, quickly reacted and is launching Bard , its own attempt to create an AI chatbot leveraging the power of large language models and integrate it into the search process. (“Large language models” are deep-learning algorithms for natural language processing that can summarize, translate and generate new text.)

generative ai customer service case study

  • Nicolaj Siggelkow is a professor of management and strategy at Wharton and a codirector of the Mack Institute for Innovation Management. He is a co-author (with Christian Terwiesch) of Connected Strategy (Harvard Business Review Press, 2019).
  • Christian Terwiesch is a professor of operations and innovation at Wharton and a codirector of the Mack Institute for Innovation Management. He is a co-author (with Nicolaj Siggelkow) of Connected Strategy (Harvard Business Review Press, 2019).

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Generative AI: 60 Business-Ready Use Cases

Looking across six key industries, a new report examines potential applications to help enterprises navigate the transformative impact of generative ai .

generative ai customer service case study

With a remarkable capacity to consume and generate information in different modalities, generative AI has unleashed new ways of working and transforming enterprises across virtually every sector. Amid the current frenzy of advancement and adoption, a new report details 60 of the most compelling use cases for businesses today, serving as a road map for executives looking to deploy high-impact, generative AI solutions at scale. 

“At the intersection of innovation and creativity, generative AI has proven to be a catalyst for enterprise transformation and growth across industries,” says Lynne Sterrett, a principal and U.S. Generative AI Market Activation Leader with Deloitte Consulting LLP. 

The  Deloitte AI Institute ’s “Generative AI Dossier” highlights business-ready applications across six industries: financial services; technology, media, and telecommunications; energy, resources, and industrials; consumer; government and public services; and life sciences and health care. Leaders can consider various advantages of generative AI in driving efficiency, creativity, speed, scale, and capacity, and highlight modalities and considerations for risk and trust. 

  • Enabling transformation with speed and confidence . Generative AI can enable banks to increase digitization at a faster pace through code assistants. 
  • Customizing marketing for the individual. The technology can enable hyperpersonalized, regulatory-compliant marketing material generation across different geographies. 
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Technology, media, and telecommunications (TMT). Much of the data-rich TMT industry sees the greatest potential value from generative AI in accelerating efficiencies through digitization and shifting organizations from product-focused to customer-centered. TMT companies face a transformative opportunity to streamline processes, free up human capital for creative, value-driven tasks, and ultimately help companies grow and innovate. For instance: 

  • Providing conversational chat for customer service . With a generative AI-enabled voice assistant, customer concerns can be remedied faster and in line with company policies and standards while maintaining or even enhancing customer satisfaction. 
  • Translating specs for sales. Generative AI can help sales staff quickly find and translate technical specifications for customers as well as document and summarize insights from customer interactions. 

Energy, resources, and industrials (ER&I ). Faced with substantial challenges related to energy security, affordability, and profitability, ER&I companies can use generative AI to glean valuable insights, adapt to industry nuances, and evolve to take a leading market position. Looking to the future, generative AI will likely play a central role in developing real-time, bespoke training materials to support the workforce through transition and adoption of sustainable practices. For example: 

  • Keeping the equipment healthy. Generative AI in asset maintenance planning can improve equipment uptime, reduce maintenance costs, and enhance operational efficiency. 
  • Promoting employee safety. The technology can be used to develop personalized and immersive occupational health and safety training materials that allow trainees to be safely exposed to realistic scenarios and thereby reduce or better respond to real incidents. 

Consumer. For businesses in the consumer industry, generative AI holds vast potential. It could support organizations in enhancing interactions, creating compelling content on demand, and analyzing large-scale enterprise data at faster speeds. From helping customers find the answers and products they need to enabling a level of market analysis with granularity and speed that was previously unachievable, generative AI may sit at the core of consumer business. Among the potential applications: 

  • Enabling data access for all. Generative AI can help guide business users to key insights in consumer behaviors by enabling them to combine data from various sources through natural language queries and translating issues to action without needing the help of dedicated analysts. 
  • Assisting in product design. The technology can help to accelerate the product prototyping life cycle by creating new concepts and high-fidelity virtual prototypes. 

Government and public services (GPS). GPS organizations are increasingly exploring how generative AI can be used to help automate administrative tasks, analyze policy documents, parse case notes, and inform customized citizen services. In fulfilling their duty to serve their constituents, public servants can use generative AI natural language processing to revolutionize the way governments interact with citizens while promoting the responsible use of this technology. For instance: 

  • Digitizing policymaking. Generative AI can be used to search large volumes of policy documents and output natural language responses to user queries in complex policy environments. 
  • Simulating urban planning scenarios. The technology can be used to help urban planners in the ideation and design of novel urban concepts, such as by generating 3-D city models or simulating natural disasters to assess infrastructure vulnerabilities.  

Life sciences and health care (LSHC). Generative AI can help transform the LSHC industry in three ways: enhancing operational performance through improved employee productivity; providing hyperpersonalized experiences to patients, customers, and employees; and developing enterprise digital and data solutions. Together, these capabilities have the power to improve efficiency, experience, and agility, and enhance quality of care and health outcomes. Some examples: 

  • Unlocking the cures. Generative AI can be used to model the structure and function of proteins and biomolecules, accelerating the identification and validation of molecules and the creation of new drug candidates. 
  • Simplifying claims submission. The technology can also be used to create code for a claims department to categorize incoming claims and billing for medical services and procedures, thereby potentially improving accuracy, efficiency, and speed in the claims submission process. 

The advent of accelerated computing is driving massive advancements in AI technology, which is leading enterprises across industries to radically reimagine their products and business models. With these examples in mind, businesses can position themselves to take full advantage of the emerging opportunities from generative AI. 

Read the full “ Generative AI Dossier ” report. 

This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor.

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Introducing UltimateGPT

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5 Generative AI Use Cases to Supercharge Your Customer Support

  • AI Automation


What if there was a way to scale your CX and provide a more joyful support experience for your customers? This article covers 5 generative AI use cases that are sure to enhance the quality of your support.

Picture this: those shoes you’ve had your eye on have finally gone on sale, and you want to try a couple sizes to make sure you get the right fit. Before you hit that order button with 3 sizes of the same pair in your cart, you’d need to know the return policy for your region because you’re located in Canada and the company is based in the U.S. You spend ages scrolling through Help Center articles trying to find the answer, but to no avail.  It’s now 11:56 and that sale ends at midnight.

No one wants this customer experience! Luckily, with the help of automation your customers can self-serve 24/7, and the potential of chat bots to deliver a conversational experience has dramatically increased in recent times. In fact, what if we told you there was another way to scale your CX while also offering a more joyful experience for your customers, your agents, and more? The answer lies in bringing the tech behind ChatGPT into your customer support with the help of generative AI.

What is generative AI? 

In order to fully appreciate the top generative AI use cases for supercharging your customer support, it’s best to start by understanding the basics of how this tech works. When it comes to text-based automation, generative AI draws on large language models (LLMs) to produce more natural bot-based conversations than ever before. It’s the tech that powers ChatGPT , which took the world by storm last year. The capacities of generative AI  for mimicking human-like conversational style are so convincing that it has even passed the Turing test . 

In terms of how generative AI can revolutionize your CX , this new generation of automation promises increased speed, efficiency, and sophistication. For example, instead of manually building out bot flows and spending months training your bot, you can simply connect to your knowledge base and immediately begin having more human-like conversations with customers. We saw the capabilities of gen AI and decided to build it into our product, launched a bot called UltimateGPT that can do all this in minutes. And that’s only the tip of the iceberg. Below are the top 5 generative AI examples for how you can use this tech to enhance your customer support .   

Want to know more about the latest AI trends in 2023? Click here to review the must-haves for customer support automation.

Top 5 generative AI use cases

Provide more natural conversational experiences for your customers.

Prior to generative AI, it was not uncommon for chatbots to miss the mark on creating a genuinely humanized experience with the help of conversational AI . On a good day they could correctly match their replies to the customer query at hand or liaise between your customers and your support agents. But could they really produce humanized automated experiences ? With generative AI the answer is a resounding yes. Thanks to the LLM-based technology, it can immediately start producing more natural conversational experiences without training or manually building bot flows. Simply connect it to your knowledge base, and you are ready to get started. 

And this brings us to our second generative AI use case.

2. Instantly pull info from your knowledge base 

One of the best parts about not needing to train your bot in order to make sure it’s supplying customers with the most up-to-date information is that when you use your knowledge base as your data source, it will automatically pull information from your FAQs, catalog, or whichever other inputs you want it to. This is revolutionary because the bot’s set-up time is drastically reduced. You can take the existing info in your data source to build a bot that works instantly.

Learn more about generative AI for customer support

How to prep your customer service knowledge base for generative ai, understanding the tech behind llms and generative ai, 3. keep all conversations aligned with your brand tone of voice.

One of the coolest aspects of using an LLM-based bot that’s connected to your knowledge base is that it won’t just deliver facts in a generically conversational way; it can seamlessly match your brand tone of voice as well. Whatever data source it is connected to, it will mimic stylistically rather than merely regurgitate facts. For example, you can select how your bot will interact with your customers by selecting between informal and formal tone of voice. Your bot can even supply customers with the info they are looking for in the language of their choice. This capability even extends to aligning with specific regional language variation. 

And with UltimateGPT you can make sure small talk and other chatter is on-brand as well with our new Bot Persona feature .

4. Structure tickets automatically, so agents don’t have to 

Yet that’s not all, folks. Not only is the generative AI bot factual and conversational to the benefit of your customers, it is also extremely helpful to your support agents. That’s because the LLM-based bot can do more than chat with customers. With the help of generative AI, your bot can automatically structure tickets to prime your human agents better when conversations get escalated to them. This capability even extends to analyzing customer sentiment. 

Your bot can also structure, summarize, and auto-populate tickets – all those tedious tasks your human agents kinda hate doing. And this way, they can hit the ground running when it’s time to reply to more complex queries. 

5. Quickly craft example replies

Your LLM-based bot’s capacity to assist your agents with the more tedious elements of their jobs doesn’t stop with structuring tickets. Bots powered by generative AI can also save agents tons of time manually writing out replies. And this brings us to our final generative AI use case. An LLM based bot can then produce personalized sample replies (support macros) tailored to a customer’s specific query because these bots can also take conversational context into account.

Ready to get started? Here's how to easily build a gen AI chatbot using ChatGPT.

All these potential generative AI use cases may sound cool, but to actually implement LLM technology into your CX you’ll still need the right tools. UltimateGPT harnesses the power of LLM-based automation – enabling brands to integrate the best of conversational and generative AI into their support offering.     

Are you ready to enhance your customer support with generative AI?

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generative ai customer service case study

  • AI & Solutions

The Future of Contact Centers: How Generative AI is Transforming Customer Service Experiences?

generative ai customer service case study

The contact center industry has witnessed significant changes over the years, driven by technological improvements and shifting customer expectations. One of the most revolutionary developments in recent times has been the integration of generative Artificial Intelligence (AI) into customer service operations.

Generative AI, powered by cutting-edge language models like GPT, Bard, etc., has the potential to redefine how contact centers handle customer interactions. 97% of business owners believe ChatGPT will help their business, and 64% believe AI will improve customer relationships.

This blog will discuss the impact of generative AI on contact centers and delve into its role in shaping the future of customer service experiences .

Key Takeaways:

→ The global contact center Artificial Intelligence (AI) market is anticipated to reach $7.5 billion by 2030.

→ Integrating generative AI into contact centers has brought about significant transformations through personalized responses, omnichannel support, virtual assistants, real-time language translation, proactive customer support, and data-driven insights.

→ However, generative AI solutions also have their share of challenges related to ethics, human collaboration, data security, scalability, and workforce training. To overcome them, businesses must prioritize transparency, empathy, privacy, continuous improvement, and employee skilling.

→ The future of CX lies in AI-powered contact centers, as they are setting new standards for customer engagement and satisfaction. Contact centers must thus embrace AI to remain competitive and combine its potential with human expertise for stronger customer relationships.

The Rise of Generative AI for Contact Centers

Generative AI, a subfield of Artificial Intelligence, enables machines to produce human-like responses, generate creative content, and simulate human thinking processes. Generative AI for contact centers offers many benefits that revolutionize customer interactions. 

Let’s discuss the prominent ones:

generative ai customer service case study

Enhanced Customer Service with Personalized Interactions

Traditional contact centers have relied on pre-scripted responses and decision-tree-based systems, often leading to a one-size-fits-all approach. This doesn’t align with customer expectations. 71% of buyers prefer personalized interactions, and 66% are frustrated when brands don’t deliver on this expectation. This is why the CX personalization & optimization software market is predicted to be worth $11.6 billion by 2026. 

Generative AI is powered by natural language processing capabilities , which can help agents understand customer queries. By analyzing previous interactions and customer data, AI-powered contact centers can tailor responses to individual preferences, past behaviors, and demographics, leading to highly personalized customer interactions. 

Moreover, Generative AI can adapt its tone and language to match the customer’s, creating a more human-like and empathetic conversation. This level of personalization fosters stronger connections with customers.

Seamless Omnichannel Support

Customers today expect seamless support across multiple channels, including phone calls, emails, social media, and messaging platforms. Meeting this customer demand leads to significant benefits for businesses. For instance, companies with omnichannel customer engagement strategies can retain 89% of their customers.

The Generative AI language model can be integrated into various communication channels, ensuring that customers receive the same level of service, irrespective of the platform they choose to interact with the company.

This omnichannel support improves the customer experience and helps contact centers optimize their operations by centralizing data and interactions. Agents can access relevant information from a unified system, resolving issues quicker.

AI-Powered Virtual Assistants

Generative AI for contact centers has facilitated the rise of AI-powered virtual assistants that can independently handle routine inquiries and tasks. Virtual assistants can handle simple queries, provide basic information, and perform tasks like order tracking, appointment scheduling, and payment processing. By taking on these routine tasks, virtual assistants free up human agents to focus on more strategic customer interactions. This enhances the overall service experience and increases employee retention rates by eliminating repetitive and boring tasks.

Real-Time Language Translation

Contact centers, especially those working for global brands, often deal with customers from diverse linguistic backgrounds. Even customers value the multilingual capability of a business, and it influences their level of trust. A survey revealed that 40% of consumers would refuse to buy from a company that does not speak their native language.

Generative AI can bridge these language barriers by providing real-time language translation services. This feature enables agents to communicate with customers in their native languages, enhancing inclusivity and customer satisfaction. Moreover, language translation AI ensures that nothing gets lost in translation during interactions, leading to more accurate and effective problem-solving. 

Proactive Customer Support

Generative AI enables contact centers to adopt a proactive approach to customer service. By analyzing customer behavior, AI systems can identify and address potential issues before they escalate. For instance, if a customer is repeatedly searching for refund policies, the AI can proactively provide relevant information, reducing the need for the customer to reach out for assistance. This makes customers feel empowered.

Additionally, AI-driven sentiment analysis can assess customer emotions during interactions. Contact center staff can then intervene promptly and positively influence customer experiences. This proactive approach further cements the relationship between customers and the brand.

Data-Driven Insights and Analytics

AI-powered contact centers generate vast amounts of data through customer interactions. Analyzing this data provides valuable insights into customer preferences, pain points, and trends. Contact centers can use these insights to identify areas for improvement, develop targeted marketing strategies, and optimize customer service processes continually.

By leveraging AI analytics, contact centers can predict customer behavior, anticipate demand fluctuations, and allocate resources more effectively. Such data-driven marketing results in cost savings and operational efficiencies.

Challenges Related to Generative AI for Contact Centers

Generative AI has the potential to transform customer service experiences in contact centers, but its successful implementation requires addressing the following challenges:

generative ai customer service case study

Ethical Considerations

As generative AI becomes more sophisticated, there is a need to address ethical considerations surrounding its usage in customer interactions. Contact centers must ensure transparency about AI involvement and communicate when customers are interacting with AI-powered systems. Ethical guidelines should be in place to prevent the misuse of AI and maintain trust with customers.

Human-AI Collaboration

While generative AI can handle routine tasks effectively, human agents still play a critical role in emotionally sensitive interactions. Finding the correct mix of automation and human support is essential to deliver exceptional customer service. Human agents can complement AI by providing empathy, emotional intelligence, and creative problem-solving.

Data Security and Privacy

Generative AI models rely on huge volumes of data to learn and generate responses. Contact centers must prioritize data security and privacy, especially when dealing with sensitive customer information. Implementing robust data protection measures and adhering to relevant regulations is vital to maintaining customer trust.

Training and Fine-Tuning

Generative AI models require continuous training and fine-tuning to ensure accuracy and relevance in customer interactions. Contact centers should invest in regular model updates and improvements to adapt to evolving customer preferences.

Scalability and Performance

As contact centers handle large volumes of customer inquiries, AI systems must be scalable and perform efficiently under high demand. Ensuring that AI-powered solutions can handle peak loads without compromising response times is crucial for maintaining service quality.

Impact on Workforce

Adopting Generative AI solutions for the contact center may lead to concerns about job displacement and the future role of human agents. Contact center management should address these concerns proactively by reskilling and upskilling agents for more complex tasks that require human expertise and empathy.

Final Words

Introducing AI into customer interactions might be met with varying levels of acceptance from customers. Building trust for AI-powered support is thus crucial. This can be achieved by providing clear information about AI’s role, ensuring transparency, and consistently delivering accurate and helpful responses.

Contact centers’ future is undoubtedly shaped by Generative AI and its transformative impact on customer service experiences . From personalized interactions to omnichannel support and proactive assistance, AI-powered contact centers are setting new standards for customer engagement. 

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generative ai customer service case study

Generative AI in operations: Capturing the value

In this episode of McKinsey Talks Operations , host Christian Johnson sits down with senior partner Nicolai Müller and partner Marie El Hoyek from McKinsey’s Operations Practice. Together, they discuss the game-changing potential of generative AI. From automating complex processes to unprecedented opportunities across industries, discover insights on productivity boosts, system considerations, and the vital capabilities organizations need for successful integration.

Their conversation has been edited for clarity.

Christian Johnson: Your company’s future demands agile, flexible, and resilient operations. I’m your host, Christian Johnson, and you’re listening to McKinsey Talks Operations , a podcast where the world’s C-suite leaders and McKinsey experts cut through the noise and uncover how to create a new operational reality. As we’re recording this episode in late 2023, it’s clear that generative AI, or gen AI, has become the topic in conversations about digital, analytics, and operations. This new deep learning technology is already making ripples with applications across the value chain.

For today’s episode, I’m delighted to be joined by Marie El Hoyek, an associate partner based in London, and Nicolai Müller, a senior partner based in Cologne. Together, we’ll be exploring what generative AI in operations is, how it’s different from digital twins and other AI technologies, its potential, and its risks. We’ll also look at what it takes to get started with these tools. Nicolai, great to have you here today. Welcome.

Nicolai Müller: Thank you. It’s a pleasure to be here, Christian.

Christian Johnson: Marie, so pleased you’re able to share your thoughts with us today. Thanks for joining.

Marie El Hoyek: Pleasure being here, Christian.

Christian Johnson: Great. So, Nicolai, can you tell us a bit about why you believe generative AI is worthy of discussion for operations leaders, especially now?

Nicolai Müller: In the past decades, there was this mantra of being faster, being more efficient, and pushing productivity. Tools we all know, such as Lean, offshoring, reviewing make-or-buy decisions, and also through technology—but we see nowadays that this productivity improvement gets more complex.

In this scenario, we now have a new technology coming in: generative AI. It promises to automate processes that, in the past, were hard to automate—areas that are more in management collaboration, which currently humans are operating, and also in complex data that you have to manage. So, in this context, there’s the question: How much will generative AI help in the search for productivity?

The McKinsey Global Institute has looked into this, and we discovered that, particularly in the areas of collaboration and management, around 50 percent of typical activities can now be automated by generative AI. Also, when it comes to handling complex data and synthesizing the essence of that, we believe there’s a huge jump in automation. This may lead to value creation across industries and functions—from pharmaceuticals, to automotive, to machinery and functions from engineering, procurement, and supply chain, to customer operations—that can unleash tremendous value. We talk about $3.5 [trillion] to $4 trillion, which is approximately the GDP of the UK.

Christian Johnson: Nicolai, what are some of the more specific opportunities that your clients are focusing on, and that you’re focusing on right now?

Nicolai Müller: Where I see our clients acting fast is in product development. And if you look deeper into product development, especially in software coding, we see up to a 50 percent productivity increase by having a machine produce code from the simple instruction, “Please give me the code for a program doing XY,” and by using tools like ChatGPT and others, a solution is generated. This is one application area where we see generative AI becoming a copilot for humans, aiding in tasks ranging from program management to procurement, and assisting supply chain managers in performing their roles more effectively.

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Christian Johnson: Thanks, Nicolai. That has given us a great idea of the why and some of the opportunities. Now, let’s go into a little bit more detail about what generative AI is. Marie, what can you describe here for us?

Marie El Hoyek: Generative AI is a fascinating field, and just like the name suggests, it exists at the intersection of artificial intelligence and natural-language processing. Essentially, it involves a machine that can analyze something, and this something can now be unstructured, like language or pictures. Similar to a person, generative AI is all about teaching machines to understand and generate text or content.

Now, to add a bit more flavor, let’s discuss the different generations of large language models—LLMs. These models are the driving force behind what we refer to as generative AI. One of the first ones we commonly heard about is GPT-3, which stands for generative pretrained transformer 3. When it was introduced, it had 175 billion parameters. Think of parameters as the amount of information it had learned, allowing it to generate text ranging from writing letters to answering questions, primarily text-based. Soon after, GPT-4 was released, and we saw a leap from 175 billion to 170 trillion parameters. Consider how much more it had learned, making it more fluent and accurate, and now it could also be used for images and video.

This is the transformative possibility with generative AI. You can now generate new content in many different types of spaces. Now, that being said, generative AI comes with its own set of risks and challenges. If you imagine that it’s based on logic or probabilities, very similar to the human brain, the answers come from what you’ve learned and your sources. Because of this fact, any generative AI can give you a convincingly wrong answer—and this is what we call hallucination .

Christian Johnson: I love that term. But what do you do about it? How do you mitigate?

Marie El Hoyek: If you had a person answering you based on wrong information, you would tell them, “I want your answer from this specific book.” Similarly, you can prompt generative AI better by telling it, “I want you to answer me from this data set or to tell me where you’re guessing.”

Another risk is model bias. Imagine that the model or the person has learned from the internet as its source, which is not the most respectful or kindest place. So, whenever you use a model, you need to be able to counter these biases and instruct it not to use inappropriate or flawed sources, or things you don’t trust. Another risk that is top of mind is IP [intellectual property] risk. Now, if you imagine generative AI generating code for you, who owns the code? Is it the gen AI that generated it or the requester who wanted it? These details are something we will need to iron out soon.

Christian Johnson: What I’m appreciating here is the discussion of the very limits of the data sources. That’s really critical, right?

Marie El Hoyek: It’s critical. Additionally, the fact that you need to guide your own data means you have to take care of your data and ensure its safety. Otherwise, that is also an added risk. That being said, all of these risks can be mitigated. However, we need to be aware of them, plan for them, or approach them in a way that limits them so we can control them. By the way, we’re witnessing regulations and offerings that are starting to adapt to these risks, and I expect we’re going to see quite a few changes in the near future.

Christian Johnson: Just the evolution here—the rapid expansion from 100 billion with a “B” to 170 trillion with a “T” is really dramatic. I think one thing we would now like to turn to is how this is being used and where we are seeing use cases come to life in businesses today. What are some really good examples of that?

Nicolai Müller: I think it’s a question that clients have to ask themselves: What impact do I want to achieve? In the end, we have to solve one big question and challenge: how to increase productivity, which involves efficiency and effectiveness.

When we look into use cases, we try to explore different angles. One is the question of automation. Things that currently take hours can be done in seconds. But it’s also about augmentation, where a human may only be able to work with a certain set of data. Imagine being able to access all the data in the world that exist. This was one of the big revolutions; the internet gave us access to all data. Now, with machines, we can use and synthesize that data. So we talk about augmentation. And then we see innovation.

Innovation is the capacity to come up with completely new solutions. Not just making an existing product cheaper or achieving faster product development, but now generating completely new ideas for features and services. So what have we seen? Automation. I talked about how I’m fascinated by what we can now do in software coding and the whole field of engineering. You also heard, for example, the CEO of Nvidia saying, “Hey, the era of software is over. I think we told all our kids to learn software; now you figure out software can be done by a machine.” It’s a huge evolution that we see, but not only in software.

Parts and hardware development. Synthesizing a huge amount of requirements that your customer gives to you, asking generative AI to understand what the requirements are and how the requirements differ from the last product. How do the requirements vary between products? Are they similar or different? It will help to come to a better synthesis, better understanding of the requirement, and develop faster and better products.

In augmentation in pharma and research, I think we’ll see a humongous increase in effectiveness, output, and research. We have cases in pharma where you can imagine understanding each little molecule, what kind of effect it has, and how it reacts with other molecules. It’s something that is instrumental. So we see vaccines or other pharma products being developed faster than traditionally was expected by using generative AI. This augmentation leads to a better kind of solution.

As for innovation, you may have also seen one famous German OEM in the US that has integrated ChatGPT into their products. So you can interact and speak with your car. This is innovation. But, Marie, you have also worked with me in this space. What have you seen?

Marie El Hoyek: My background is in industrials, very much deep in operations. Personally, I love all the copilot applications, especially in procurement. The idea that you can ask a friend who knows all your contracts and can answer any question by heart and in plain English is just mind-blowing to me. So, instead of analyzing old contracts, price history, and external trends, I can simply ask the questions. I’m sure there are many more cool applications in terms of content generation, etcetera, but this one, in particular, blew my mind.

Nicolai Müller: And Marie, what I observed are these humongous opportunities out there and the numerous use cases. I mean, we have been in workshops where we were sitting with our clients, and easily after an hour or two, we didn’t end up with just five or six potential use cases across a whole different function, but rather 150 or more. I see here a huge opportunity, but the challenge that we’re facing is, where do you start? What I call “happy generative AI,” where a copilot can help you in your daily job, may become a commodity that everybody can do. Where is the truly transformative generative AI? Is it leading to a differentiating factor for your business? Is it really adding value and creating value for your customers?

I think this is the challenge we face. It’s like what we say in Germany, you don’t see the woods because of the amount of trees in front of you. So where do you start and where do you end?

Christian Johnson: Can’t see the forest for the trees. That’s exactly it. When I hear all of this excitement, I also think of the classic chart that we’ve seen for technologies in general, where you have this initial sharp upward curve as everybody gets very excited about it. Then it sounds like where you’re moving is, we need to anticipate when organizations either find, as you’ve put it, that it’s commoditized or that it’s hard. And that gets us down then to value. How do companies think about long-term value and not just a set of very exciting use cases that may not build forward very much

Nicolai Müller: This is a challenging question. If you look into the Google search index, which gives you a bit of a feeling of where we are on the curve, you’ll find out that it’s now Googled more than any traditional operational questions you have. You have seen all the digital manufacturing terms out there. We have cloud computing and the Internet of Things that we’ve now seen over the years, and it’s a constant discussion.

Generative AI in operations has just started to pick up, I would say, in the first quarter of this year. And it has, in terms of the amount of searches people are doing, overtaken everything you can imagine. This may give you an indication that there is a huge hype out there. But has this hype and all the dreams come true yet? Indeed, people are now starting to recognize that things are easy, like the low-hanging fruits, but actually, the real core is still challenging to implement and also to make your company adaptive to changes. So we are still on the verge of answering one important question when it comes to generative AI. Is it now just another tool kit in your operations, like lean or digital or any other artificial intelligence—that is, predictive maintenance—and enables levers you can pull? Or is it a disruption on its own? Is it changing the way you operate? I think these are two scenarios I can imagine.

I tend to believe that in the next two to three years, we’ll see these two questions answered. And it may differ completely by player or by industry what the outcome is. Let’s talk about disruption. Imagine that coding is now easy. Often, you have, for example, an automotive OEM defining requirements, and then you have a supplier more or less programming the code. If now that code can be programmed by machine, do you need a supplier anymore? It can be disruptive and threatening to say that the raison d’etre, or the reason for the supplier to exist, is actually gone. So this is an extreme of a disruption.

For example, for a very research-heavy company, suddenly, if you tap into completely new sources of data, you come to a completely new set of products. And finding the language model that suits you by adopting generative AI in ways that are differentiating may help you to move faster and with better products. I think this is the most pressing question that clients have to answer.

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Christian Johnson: I think one of the things we’re struggling with and organizations seem to always struggle with when it comes to a new technology or a new methodology is how do you scale? We talked years ago about pilot purgatory—this idea that you try a bunch of ideas, but then they’re never really cohering in a way that creates lasting value. So how can organizations think about this in a way that they can minimize or even avoid that kind of stagnation with this idea?

Marie El Hoyek: This is a good question, Christian. Generative AI might be relatively new, but we have years of experience in scaling digital transformations. To your point, one of the biggest challenges is the pilot trap. Building a pilot or innovating with the technology is great, but transforming an organization is a whole different playing field.

Nicolai talked about the business-led mindset to prioritize applications that are useful with real business ROI. Beyond that, getting a real impact out of any digital change, and for generative AI in particular, will always be both a human and systems question. The way I’d summarize it is, without people, the best technology has no impact. We need to take our people on a real change journey to build the capabilities to use this technology, develop this technology, but also just to know what you can ask of this technology. And by the way, in terms of developing it, there are new skills that are needed here.

Christian Johnson: So what sort of capabilities do organizations really need now?

Marie El Hoyek: I’m thinking about prompt engineering, for example, which is the ability to ask a question really, really well. Now, number two is in terms of systems. There are fundamental questions that businesses should consider early to ensure that whatever they decide leads to capable, consistent, and safe technology usage. You don’t want to end up with ten different decisions on the technology because pilots are going left and right.

So you’re going to be wondering, do we build our own language models? Do we work with partners? Do we get off-the-shelf solutions? Where do we put our data? How do we process it? These questions are better learned early, and you need to make a conscious decision about them, to ensure that later on, as you use generative AI more and more, your solution is safe, scalable, and consistent. So, yes, for me, it’s both the people element and the systems element that will enable us to go through to the finish line.

Christian Johnson: Excellent. Thank you very much. We’re now nearing the end of our discussion. But before you go, I’d like to ask one final question, which is, what should our audience be doing now to bring generative AI to their organizations? There’s so much noise out there. We’ve got a strong idea of that with the Google searches. So how do you start to cut through and make a solid start?

Nicolai Müller: I would recommend two things. First is to start with a pilot, and I would even use the term “play” with generative AI. The cost of doing nothing is just too high because everybody has this at the top of their agenda. I think it’s the one topic that every management board has looked into, that every CEO has explored across all regions and industries. So it’s important that you start and see what generative AI can do.

In parallel, you need to really think about your strategy. When I talk about strategy, it includes a couple of elements. It’s a question of how will this impact my business? Where will it lead to improvements? Where will it not lead to improvement? Should I go fast ? Should I not go fast? Do I have solutions out there? Do I need partners? Can I rely on existing LLMs out there, or should I build my own? I think this is the whole question of truly understanding what generative AI in 3 to 5 years means for us.

Then there’s a layer in the strategy, which is about getting the data technology right. It’s understanding how you want to put governance and organization in place, which can build solutions. And there’s the question, where do the competencies in my company actually come from? Can I build them? Do I need to acquire them? So you need to be thoughtful about the whole question of competencies needed.

And then there’s the question of actually making the change. We often hear that this is the most important thing. You need to make people work with generative AI. You need to capture the early wins, but also things that are more challenging.

Christian Johnson: Excellent. And Marie, anything you’d like to add?

Marie El Hoyek: Yes, Christian. Nicolai, last time we spoke, you talked about this fresh breath of innovation in our companies, and I love to repeat this. You can see it in our discussion even. This gives us the ability to dream again, to come up with new things, and to hope for more impact. And I think, to some extent, we just need to learn, and start doing it, and start capturing it.

Christian Johnson: That’s a lovely ending, Marie. Thank you both, Nicolai and Marie, for sharing your expertise and experiences of generative AI with us today. It’s a topic that we don’t see going away anytime soon. So, your advice on diving in, but with both eyes open to risk mitigation and value creation, is a great note to end on.

Marie El Hoyek is an associate partner in McKinsey’s London office, and Nicolai Müller is a senior partner in the Cologne office. Christian Johnson is an executive editor and is based in McKinsey’s Washington, DC, office.

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Generative AI has been gaining awareness and popularity since the launch of ChatGPT in November 2022. According to McKinsey , one-third of organizations have already embraced the technology for at least one business function.

The research further found that 60% of organizations that have adopted AI are using generative AI for content creation in various business areas, including product and service development, marketing and sales, and service operations.

It’s clear that embracing AI and generative AI is no longer a choice for organizations—it's a must. Implementing generative AI for better self-service for both customers and employees can help increase deflection rates, which translates to huge cost savings.

Let's explore three ways to use generative AI in any business.  

1. Employee empowerment

AI is an ally. By assisting with and augmenting repetitive, mundane tasks, it frees people to focus on higher-value job duties and activities that require human intellect.

Generative AI possesses case and chat summarization capabilities that streamline and enhance workflows. For IT and HR agents, the automated ability to condense intricate details into digestible formats boosts productivity and helps keep employees well informed.

In addition, generative AI plays a vital role in helping employees easily find the information they need and quickly get their work done. Find out how to put this game-changing technology to use in our Empowering your workforce with generative AI webinar.  

2. Customer connections

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The technology also equips customer service agents with case summaries to help them quickly get up to speed on customers’ concerns so they can provide rapid resolutions. Putting people first explores the transformative impact of generative AI on customer experience.  

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Find out how in Generative AI to boost your low-code developer experience .

© 2024 ServiceNow, Inc. All rights reserved. ServiceNow, the ServiceNow logo, Now, and other ServiceNow marks are trademarks and/or registered trademarks of ServiceNow, Inc. in the United States and/or other countries. Other company names, product names, and logos may be trademarks of the respective companies with which they are associated.

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Five “no regret” actions for TMT companies to unlock generative AI’s potential

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Tmt companies already have a lead in genai adoption. we’re exploring how they can maintain that lead and unlock value, now and in the future..

  • With over half (52%) of TMT companies already using GenAI – well ahead of other industries – the sector is uniquely well-placed to drive GenAI adoption. 1
  • As early adopters, TMT companies will also be among the first to tackle GenAI’s uncertainties and risks. 
  • To maximize GenAI’s value, TMT companies must pursue innovation and integrity through select actions. 

A s the world of generative AI (GenAI) takes shape, technology, media and entertainment and telecommunications (TMT) companies will play a crucial role by injecting GenAI into their service portfolios and deploying it in their internal digitalization roadmaps. GenAI’s transformative potential and rapid acceleration also pose challenges, from organizational constraints to regulatory uncertainties. Mindful of these complex forces, we’ve identified five ”no regret” actions that TMT companies should pursue to convert GenAI’s promise into long-term value. TMT companies will be at the forefront of the GenAI revolution. Many technology providers will enable GenAI solutions for their customers, with telcos enabling the key infrastructure. For media and entertainment companies, GenAI will trigger both innovation and disruption of business models.

Organizations across all three sectors can also harness GenAI as they transform internal systems and processes, building on the benefits of “traditional” AI. Here, TMT companies find themselves in a promising position relative to other industries. This is evident in higher levels of GenAI adoption, led by technology companies. It’s only in media and entertainment companies where employee usage falls below the cross-sector average, a possible sign of greater employee resistance. But taken together, TMT companies are well-placed to both deploy GenAI internally and build it into their products and services.

Open Image description#Close Image description

Graph of current usage of GenAI across TMT and all industry markets drawn from the EY Work Reimagined Study 2023. Technology hardware, Technology Software, Media, and Telecommunications employers and employees are all more likely to be using GenAI compared to the average of all industry markets combined.

Encouraging usage levels are supported by ongoing investment: 45% of TMT companies are investing in AI-driven innovation – with AI product and service changes already integrated into capital allocation processes – and a further 46% plan significant capital investments in the next 12 months. 2 This puts TMT companies in prime position to leverage GenAI to benefit the wider economy. And while GenAI is potentially disruptive, TMT companies have previously adapted to changing technology cycles and the pressures they bring. Looking ahead, TMT companies should focus on how best to harness GenAI as part of the AI capabilities they already possess.

Shaping new opportunities for customers, platforms and collaboration

As TMT companies explore how they can benefit from GenAI – both in the near- and long term – they should consider use cases that deliver both growth and efficiency. Use case clusters that stand out include:

  • Driving customer experience excellence: In the near term, GenAI can revolutionize customer interactions by creating smarter chatbots, and by blending human and digital assistance in new ways. This is particularly relevant to telcos with extensive customer support operations. GenAI can also help personalize services, ensuring that investments – whether in content recommendation engines or fiber network upgrades – better fit specific customer needs.
  • Unlocking platform business models: In the longer-term, Gen AI can unlock the power of new business models, specifically service platforms made available across ecosystem partners and intermediaries. GenAI’s potential to help enhance product development and distribution, curate B2B2X customer journeys and improve “ecosystem satisfaction” means it can play a pervasive role in business model innovation.
  • Becoming an AI-augmented organization: Knowledge management, productivity and automation all stand to gain in a GenAI-centric organization with more empowered employees. By improving information accessibility and sharing, TMT companies can finally overcome enduring silos – between IT and product development functions, for example – automate low-value tasks, and free up time to focus on value creation.

Use case needs will vary by sector. Technology companies will be keen to enhance their existing platform offerings with GenAI, looking for the most promising adjacency with other emerging technologies in their product portfolio. Telcos will focus on how GenAI can either augment or substitute existing customer care channels, systems and processes. And media providers will assess GenAI’s specific role at every stage of the content lifecycle, from creation through to curation and distribution.

Figure 2: Indicative use cases by TMT sector

TMT Growth and efficiency table

Source: EY Knowledge analysis

This is a chart showing use cases across tech, media and entertainment, and telecom, listed on a scale from growth to efficiency. Growth areas include platform business models, content creation and editing, and B2B2X services and ecosystems. Efficiency areas include code development and maintenance, dynamic content delivery and IT and network infrastructure planning.

Case study: A major telecommunications company reshapes its customer service channels with GenAI

EY teams are working with a leading global telecommunications operator to reshape its customer service channels for both B2B and B2C customers. This involves developing specific GenAI use cases and associated automation for various customer-facing environments, including contact centers and retail stores. Key elements of the project include establishing a robust governance model, integrating GenAI with traditional AI capabilities and leveraging Microsoft Azure technologies as part of the customer operations improvement.

GenAI sets new challenges for TMT

The fact that TMT companies are further along the GenAI adoption path than other industries means they will be the first to experience its challenges. Indeed, 68% of TMT companies (pdf)  believe they’re not doing enough to manage the unintended consequences of AI, underlining the responsibilities that come with ”first mover” advantages. We see the following as major issues that require management attention:

  • Uncertain regulatory and policy environment: Regulators recognize the ethical challenges posed by AI, alongside the need for new forms of data governance and protection. While new rules should bring more certainty, they will take time to embed and may differ by geographic region, adding to complexity and potentially limiting innovation. Leading TMT companies have already voiced such fears regarding the prospective EU AI Act 3 .
  • Ecosystem limitations: While many TMT companies have supplier and value chain ecosystems in place, it may be difficult to absorb a new wave of GenAI-based partnerships within those existing ecosystem structures. New and existing partners’ rationales and priorities may differ when it comes to GenAI implementation, or they may face contrasting regulatory pressures.
  • Budget and investment constraints: Challenging macroeconomic environments could constrain TMT companies’ ability to invest in AI. This, in turn, may force greater reliance on partners to bring in GenAI technology capabilities and expertise. Likewise, budget constraints may also limit enterprise customers’ ability to invest in both AI technology and skills.
  • Employee and customer resistance: GenAI’s potential to accelerate automation and reduce human involvement is already unnerving employees, notably in the US media and entertainment industry. 4 At the same time, customer concerns about data privacy and quality may create confusion, or hinder acceptance of AI-based interactions. EY research shows that 48% of consumers are worried about algorithms used by apps and websites, and how they impact what they see online. 5
  • Inadequate data and intellectual property (IP) governance and protection: TMT companies are data-rich organizations, but this creates complexities. Clean, curated datasets are crucial to train GenAI algorithms. Organizations where data still remains fragmented and hard to manage, will need to adapt their data governance faster and further. Meanwhile, new risks can emerge from combining proprietary and public data, and from increased data sharing between TMT companies, partners and customers. What’s more, the risk of IP infringement will likely grow in a GenAI world.

Organizations also need to consider sector-specific challenges. Telcos, for example, should consider how GenAI adoption will impact future network loads and associated investment commitments. Media companies may experience business model disruption ahead of other industry sectors – as with previous technology cycles – while technology companies may be the most exposed to regulatory uncertainties as they look to globalize GenAI platform solutions. These nuances aside, TMT companies realize they need to collaborate further with other industry stakeholders to address ethical risks: 74% of CEOs believe that the business community needs to focus much more on the ethical implications of AI and how its use could impact key areas of our lives.6 This sense of shared commitment has important implications for ecosystem strategies.

Five key actions for TMT companies

1. establish an ai control tower to centralize innovation, knowledge and skills..

Thirty percent of TMT companies already have a group dedicated to AI adoption and use. 7 GenAI should act as the trigger for all companies to go further and establish an AI control tower. This moves beyond use-case experimentation to help reimagine business models, improve governance and centralize skills. The control tower group can comprise a mix of business-unit heads and other relevant executive roles – chief digital and data officers, for example – to identify and prioritize GenAI opportunities, while assessing disruptive risks, talent requirements and data governance needs.

Where to act now

Designate someone from the C-suite as an AI leader, to plan and coordinate the control tower’s activities with other parts of the business, including pre-existing digital business units or centers of excellence. Ensure that the activities of the control tower are aligned to the organization’s overall business and technology strategies.

Identify relevant skills required and immediate skills gaps, taking care to consider new roles that report to AI leadership. Meanwhile, train teams in the business and technical aspects of GenAI, leveraging core principles that can inform longer-term reskilling needs.

Develop a portfolio of targeted GenAI opportunities. As part of this, revisit your existing catalog of AI use cases and identify opportunities to incorporate GenAI into them where feasible. Prioritize GenAI use cases based on metrics such as impact, complexity, scalability and time to market. Pick a healthy mix of “quick wins” and more complex use cases.

Meanwhile, technology companies providing GenAI-based offerings to enterprises should consider commercial principles upfront. These include whether to offer GenAI as a standalone product or incorporate it into existing service bundles, and the best pricing model to begin with – from free trials and tiered pricing, through to value-based pricing.

What to decide later

  • Design a comprehensive roadmap for scaling GenAI solutions across the business. As organizations learn more about use cases’ outcomes and feasibility, teams can decide whether to allocate more resources to GenAI projects.
  • Explore more transformational business models and service portfolios that take advantage of GenAI – these include platform or B2B2X services that involve joint go-to-market with partners. Define the commercial terms that underpin “sell with” approaches, paying particular attention to revenue-share models.
  • Create a long-term plan to acquire new talent with GenAI capabilities in areas relevant to priority use cases. Regularly review and prioritize specific GenAI roles, focusing on those that will differentiate your business over time, as opposed to those likely to become commoditized.

2. Reimagine business functions and ways of working

Realizing GenAI’s potential to increase productivity and overhaul business models hinges on new ways of working. GenAI will enable more seamless interaction between business functions, with roles and responsibilities evolving over time. Organizational structures and processes should reflect these new ways of working. New information flows between previously siloed teams become possible, with GenAI empowering employees rather than displacing jobs. Initial signs from TMT employees are positive: 51% expect a net positive impact on how work is done. 8

To mitigate employee resistance, launch small-scale pilot projects using proprietary enterprise data to test GenAI solutions and gather feedback. Use the results to show how GenAI can enhance existing processes, improve employee efficiency and augment capabilities.

Ensure that your AI control tower works closely with other parts of the business to create the right internal feedback loops. Make sure that employees at all levels feel that they’re part of the journey by explaining how GenAI is being deployed and the underlying data sets it uses. This will help to build trust and confidence in AI-based outputs.

Make sure that leadership clearly communicates how workflows are changing, and that business unit leaders meet regularly to share progress and future plans, Highlighting AI’s role as a collaborative tool transforms it from a source of possible resistance into an opportunity for growth.

  • Re-evaluate the operating model in light of AI-driven improvements in data management, paying attention to new points of intersection between legacy business functions, and between those functions and the AI control tower.
  • Invest in function-specific GenAI training, as well as organization-wide upskilling and reskilling. Develop a talent development plan in sync with your technology roadmap and business function transformation. Consider creating an internal talent marketplace to enable employees to shift to new, emerging roles and build multi-function AI competencies.
  • Introduce continuous monitoring mechanisms and develop key performance indicators (KPIs) to demonstrate GenAI initiatives’ long-term value.

3. Put GenAI at the center of your ecosystem strategy

TMT companies, from technology giants and hyperscalers to network equipment vendors and telcos, are established ecosystem orchestrators. That experience could put them at an advantage, but it also means they need to consider how to adapt existing ecosystem structures. Begin by assessing capability gaps, ensuring that the ecosystem strategy caters to evolving AI opportunities, and look for new ways to tap into cutting-edge research and knowledge.

Prioritize AI discussions with the existing partner ecosystem, highlighting areas of mutual interest and potential cooperation in GenAI. Continual monitoring of the AI partner landscape for new opportunities is critical. Identify new partners – whether start-ups, immediate industry peers or academic institutions – that can enhance your GenAI initiatives. Meanwhile, technology companies can partner with companies in different industries to create customized, domain-specific large language models (LLMs) and proprietary knowledge graphs. These can be integrated with public models or offered as a service.

Assess your GenAI readiness across different layers, such as infrastructure (compute facilities, cloud, data), model, or applications development, identifying the role partnerships can play. Plug into existing pre-trained models and data ecosystems to explore use cases. All throughout, TMT companies should ensure that secure data sharing and integration protocols are adopted by ecosystem partners.

  • Scale the AI competencies within your ecosystem through select partners and deprioritize less relevant partnerships.
  • Develop closer relationships with start-ups for co-innovation and consider acquisitions and joint ventures that can extend skills and expertise.
  • Conduct regular reviews of your ecosystem strategy to ensure it’s fully aligned with your evolving AI objectives. And pay close attention to policy or regulatory factors that may influence partner choices and suitability.

Case study: A strategic alliance accelerates digital transformation

EY launched a strategic alliance with a leading enterprise AI software-as-a-service (SaaS) company to enable companies to use GenAI at scale to improve business efficiency and performance. The alliance’s offerings are aimed at addressing back-office issues such as financial crime prevention and regulatory compliance. These solutions will initially be targeted at users in the financial services and retail sectors, demonstrating the potential for TMT companies and alliances to support and accelerate GenAI-enabled transformation in adjacent sectors.

4. Build stakeholder confidence in AI

Many TMT companies have designed ethical frameworks for AI. But GenAI creates a whole new set of ethical dilemmas and security risks. TMT companies should address stakeholder concerns about AI-generated content such as IP and copyright issues, fake content, security and data privacy for training LLMs. Employee concerns are no less important. One interesting example in the creative industry is the Human Artistry Campaign, which advocates AI best practice for artists, performers, writers and athletes. 9 Regular dialogue with policymakers is essential as the AI regulatory landscape continues to evolve. And in the absence of dedicated AI regulation, TMT companies should prize robust governance to build confidence in their AI applications.

Identify new risks emerging from GenAI and implement tools to mitigate them. Establish teams to implement and supervise ethical AI procedures, and ways to monitor and audit them. Before launching LLMs, stress-test them for model hallucination, jailbreaking, inappropriate content or other legal and reputational risks.

To encourage further adoption, address concerns about GenAI data privacy. Enterprises need assurance that technology companies won’t use their proprietary data to train general LLMs or inadvertently leak sensitive information during model training. Content publishers should prioritize solutions to track and control content such as deepfake audio, video or text.

Explore current solutions such as labelling AI-generated content and consider ethical AI training programs to educate and upskill employees. Take care to build controls that account for the probabilistic nature of the outputs and ways to verify the quality and robustness of results.

  • As GenAI goes mainstream in your organization, devise comprehensive ethical frameworks for data use and model implementations, educating users at all levels about risks and mitigation strategies.
  • Combine forces with industry players, regulators, non-profit and public bodies, academics and thought leaders to shape regulation of AI-generated content and address IP-related issues in regulation.
  • Create new feedback loops with customers around AI-based services, evaluating current levels of acceptance and future receptivity.

5. Inject GenAI into multi-year tech transformation

GenAI technology is evolving fast. While it’s important to keep your options open, understanding how best to harness GenAI within broader technology transformation programs will be crucial for long-term value creation. Many TMT companies are deploying a growing array of emerging technologies – cloud, edge computing, quantum computing – to accelerate transformation, and GenAI should not be treated in isolation, but as additive to other emerging technology investments.

Prioritize technology deployments based on your business strategy. Ensure your technology roadmap is in sync with the GenAI pilots you select. Prepare datasets for identified use cases in specific domains. Ensure availability of cloud and compute infrastructure to test smaller sets of GenAI solutions on proprietary data. Technology companies supplying GenAI solutions can offer synthetic datasets to demonstrate the power of GenAI solutions.

Consider how GenAI solutions will integrate with your existing technology stack, including content management, customer relationship management (CRM) and Enterprise Resource Planning (ERP) systems. In terms of infrastructure, ensure you build in flexibility about where you can run a full-stack foundation model.

  • Ensure enterprise architecture is ready for scaled GenAI deployment, including data, applications and infrastructure. Consider modernizing data and application platforms and upgrading compute infrastructure to support larger-scale models.
  • Evaluate points of adjacency and intersection between GenAI and other emerging technologies such as edge and quantum computing to maximize their combined impact and value creation.
  • Assess the impact of growing use of compute and datacenter facilities on your Environmental, Social and Governance (ESG) and sustainability commitments. Create metrics to track sustainability and measures to mitigate downside risks.

How EY can help

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Show article references#Hide article references

  • EY, Work Reimagined Study 2023, EY Knowledge Analysis
  • EY, CEO Outlook Pulse, July 2023
  • Light Reading, DT and Orange join protest against Europe’s planned AI rules, 5 July 2023
  • SAG AFTRA, SAG-AFTRA Statement on the Use of Artificial Intelligence and Digital Doubles in Media and Entertainment, 17 March 2023
  • EY ‘Decoding the digital home’, September 2023
  • EY ‘CEO Outlook Pulse’, July 2023
  • EY ‘Tech Horizons Study’, 2022 
  • EY ‘Work Reimagined Study’, 2023
  • RIAA, Human artistry campaign launches, announces AI principles, 16 March 2023

With a commanding lead in GenAI adoption, TMT companies are well positioned to drive value from this breakthrough technology. But to do that they must also be the first to overcome new and testing challenges that GenAI poses.

The five no-regret actions outlined here can help TMT companies do just that. As they develop GenAI capabilities, they’ll need to simultaneously focus on what they can do now and plan for later steps. GenAI will change the world. TMTs can make sure that they – and their stakeholders – maximize its value. 

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What the New York Times’ copyright suit against OpenAI means

Photo illustration of a gunslinger's legs as he faces off in a showdown with another cowboy.

Hello Eye on AI readers and Happy 2024!

As many of you know, I was on leave for the past several months, working on a book about the generative AI revolution and all its potential ramifications. The book is due to be published this summer by Simon & Schuster. I’ll be letting you know more about it as the publication date approaches. Now back at Fortune , I’m assuming a new role as our AI editor, helping to build out our coverage of this vital technology. And I’ve got some exciting news: Eye on AI will be coming to your inbox more frequently. We are dedicated to providing you, as business leaders, with all the AI news you need to stay informed. AI is currently one of the hottest topics in the corporate world, and considering its rapid advancements, Eye on AI will now be delivered to you twice a week, on Tuesdays and Thursdays. Imagine, you’ll be twice as knowledgeable as before!

OK, the biggest news in AI this past week has got to be the copyright infringement lawsuit the New York Times filed against Microsoft and OpenAI in federal court on Dec. 27. It’s a doozie, one many think will be precedent-setting. Some commentators speculated it could even spell the end of OpenAI, and perhaps the entire business model on which many generative AI companies have been built. The suit doesn’t include a specific claim for damages but says the two tech companies should be held liable for “billions of dollars in statutory and actual damages.”

OpenAI, which had been in talks with the Times since April over possible licensing terms for the newspaper’s content, said it had thought negotiations were progressing and that it was “surprised and disappointed” by the Times’ suit. “We respect the rights of content creators and owners and are committed to working with them to ensure they benefit from A.I. technology and new revenue models,” OpenAI spokesperson Lindsey Held said. “We’re hopeful that we will find a mutually beneficial way to work together, as we are doing with many other publishers.” Microsoft declined to comment on the lawsuit.

The Times alleges that tens of thousands of its articles were copied, without its permission, in the process of training the GPT models that underpin OpenAI’s ChatGPT and Microsoft’s CoPilot (formerly called Bing Chat). It also alleges that ChatGPT and CoPilot allow users to further infringe on the Times’ copyrights by producing text that plagiarizes Times articles. It argues that the integration of OpenAI’s GPT models with web browsing and search tools steals commercial referrals and traffic from the newspaper’s own website. In a novel claim for this sort of case, the publisher also alleges its reputation is damaged when OpenAI’s models hallucinate, making up information and falsely attributing it to the Times. Among the reams of evidence that the Times submitted in support of its claims is a 127-page exhibit that includes 100 examples of OpenAI’s GPT-4 outputting verbatim lengthy passages from Times articles when prompted with just a sentence, or part of a sentence, from the original.

The Times lawsuit is certainly the most significant of the copyright infringement claims that have been filed against OpenAI and Microsoft to date. The Times has top copyright lawyers, relatively deep pockets, and a history of pursuing claims all the way to the Supreme Court when it feels an issue presents a threat to not just its own journalism, but to the free press as a whole. The newspaper is claiming here that OpenAI’s copyright infringement undercuts the revenues publications require to serve the public interest through news reporting and investigative journalism. This sets it apart from most of the other copyright infringement claims previously filed against OpenAI, which simply pit the commercial interests of creators against those of OpenAI. But what really differentiates the Times’ case is the clarity of the narrative and exhibits it presents. Many commentators believe these will prove highly persuasive to a jury if the case winds up in front of one.

Gary Marcus, the emeritus New York University cognitive scientist and vocal AI expert, opined, in a series of posts on X (formerly Twitter), that this is OpenAI’s Napster moment. He claims the Times’ lawsuit could wind up bankrupting the high-flying AI startup, just as a landmark 2001 copyright judgment against Napster obliterated the peer-to-peer music-sharing company’s business model and eventually drove it under.

Having done a fair bit of research into AI and copyright for my forthcoming book, I think this is unlikely to happen. For one, this case is likely to settle. The fact that the newspaper was in negotiations with OpenAI for a licensing deal and only filed suit after those talks apparently reached an impasse (probably because the Times was asking for more money than OpenAI wanted to pay) is a good indication that, despite the public interest gloss the Times applied to its complaint, its real motivation here is commercial. OpenAI has signed a deal with the Associated Press to license its content for AI training and last month inked a multiyear deal with publisher Axel Springer, which owns Business Insider and Politico, that gives OpenAI access to its current and archived content. That deal is worth more than $10 million per year, according to one report. OpenAI and Microsoft have a strong incentive to settle rather than deal with years of legal uncertainty; chances are, they will.

Even if this case goes to trial, a ruling might not ultimately go the Times’ way. Microsoft has deeper pockets than the Times and also has access to top-notch legal talent. And there are more precedents here than just Napster. Copyright experts vigorously debate which cases might be most analogous—the Google Books case, the Sega case, the Sony case, or the recent Andy Warhol case. The specifics of these analogies are too complicated to get into here. But the point is, this is far from a settled matter, and OpenAI and Microsoft have decent arguments they can use to try to defend themselves. It isn’t open and shut by any means.

It is also possible that the U.S. Copyright Office or Congress will weigh in before the Supreme Court does. The Copyright Office has just concluded a commentary period on the implications of generative AI. The Senate also recently held hearings on the topic. It is possible Congress will step in and pass a new law that would render the Times’ claim moot. Some legal scholars have suggested Congress should create a “fair learning” law that gives software companies an explicit right to use copyrighted material for AI training. Meanwhile, those sympathetic to rights holders have suggested lawmakers should mandate that creators are compensated for any works used to train AI. Congress could also insist that AI companies apply filters to screen out any model outputs that are identical to copyrighted material used in training. There is a precedent for Congress weighing in this way: The 1992 Audio Home Recording Act exempted sellers of digital audio tape from being sued for copyright infringement. But it also set up a licensing fee that all manufacturers and importers of audio recording devices have to pay to the Copyright Office, which then distributes those funds as royalty payments to music rights holders. Congress could wind up establishing a similar licensing and royalty regime for generative AI software.

Finally, even if OpenAI is ultimately forced to pay creators’ licensing fees, it can probably afford it. The company is, according to some news accounts , currently bringing in revenue at a $1.6 billion per year clip, with some insiders predicting that this figure will hit $5 billion before 2024 is out. With this kind of cash machine, OpenAI can probably survive. While copyright infringement claims sank Napter, Spotify was eventually able to reach a settlement with music rights holders. And while those payments crimped Spotify’s profits, and the company has lately struggled to sell stock investors on a convincing growth story, Spotify is also not about to go bust.

So, no, I don’t think OpenAI will go under. But I do think the Times’ lawsuit signifies that the era of freely using copyrighted material for AI training is coming to an end. The threat of lawsuits will push most companies building AI models to license any data they use. For instance, there are reports that Apple is currently in discussions to do exactly this for the data it is seeking to train its own AI models. In image generation, artists are also increasingly turning to masking technology that makes it impossible to effectively train AI models on their work without consent. Similar technology does not yet exist for text or music, but researchers are working on it. And plenty of publishers have now taken steps to prevent their websites from being freely scraped by web crawlers. Pretty soon, the only way companies are going to be able to obtain the data they need to train good generative AI models is if they pay to license it. One way or another, the sun is setting on the Wild West of generative AI.

And with that, more AI news below.

Jeremy Kahn [email protected] @jeremyakahn


Ex-Trump lawyer blames AI for fake precedents cited in legal brief. The former Trump fixer Michael Cohen said in court papers unsealed last week that he accidentally provided his own lawyer with fictitious legal citations used in a filing submitted to a federal judge because he relied on Google's AI chatbot Bard. Cohen said he had not realized Bard could hallucinate, creating realistic-looking but fictitious citations, and had provided these cases to his lawyer not expecting the attorney, David Schwartz, would drop them into his brief without checking them for accuracy, the New York Times reported . Schwartz had filed a motion asking the court to end its supervision of Cohen, now that Cohen has been released from prison after serving time for campaign finance law violations. The Bard hallucinations could factor in the upcoming New York criminal trial of former President Donald Trump where Cohen is expected to serve as a key prosecution witness. Trump’s lawyers have seized on the fake citations as evidence that Cohen is an unreliable and untrustworthy witness.

U.S. Supreme Court Chief Justice offers thoughts on AI and the law. Chief Justice John Roberts offered his thoughts on AI in the legal system in a year-end report published last week, the Independent reported . Roberts said that AI would not replace human judges any time soon but predicted that AI would increasingly be used to help lawyers prepare cases and do legal research. He said that such AI software could help level the playing field, improving access to legal resources for Americans who might not otherwise be able to afford them. However, he cautioned about AI's risks, including the problem of fake citations leading to legal errors, using the Michael Cohen news as an example, and warning about possible data privacy issues. He advised legal professionals to use AI with caution and humility.

U.K. terrorism law monitor warns AI chatbot could radicalize people. A lawyer appointed by the British government to assess its terrorism-related legislation says the country’s laws are insufficient to prevent people from being radicalized by AI chatbots. The lawyer, Jonathan Hall KC, told British newspaper the Telegraph that he chatted with a digital persona created by AI startup character.ai that was designed to mimic the head of the Islamic State and that it tried to recruit him to the terrorist group. He said the country currently had no laws that would hold someone responsible in cases where an AI chatbot, rather than a person, generated text that encouraged terroristic activities. Character.ai's terms and conditions prohibit users from uploading content that promotes violence and extremism but does not prevent the chatbot itself from outputting such content. Character.ai told the newspaper that its products “should never produce responses that encourage users to harm others.”

Nobel-winning economist cautions on STEM emphasis in new AI era. Christopher Pissarides, a Nobel-prize-winning labor market economist who works at the London School of Economics, said computer programmers were now sowing the seeds of their own destruction with the development of AI. He predicted that many coding and engineering roles in the future may be taken over by AI, while the skills that will be in high demand will be the empathetic and creative ones that humanities and liberal arts programs emphasize. He said that jobs requiring face-to-face contact, such as hospitality and health care, would not easily be replicated by AI, according to Bloomberg .


Sharing the burden. Many LLMs require huge amounts of computing power, not just to train, but also for inference. So there is growing interest in how this computing power might be federated, allowing groups of people without access to high-powered GPU clusters to run big AI models using laptops and PCs with a few GPUs available. Researchers from Yandex, Neiro.ai, the University of Washington, and Hugging Face have now proposed a method for distributed inference and for fine-tuning LLMs, an algorithm they call PETALS. They demonstrate that it can work on both LLAMA 2, which is an open-source 70 billion parameter LLM, and BLOOM, which is a 176 billion parameter model. With PETALs, each computer in the network only has to handle less than 3% of the full model weights, and it can run efficiently despite the latency and information loss that comes from trying to integrate lots of machines across the internet. You can read the paper, which is on the non-peer-reviewed research repository arxiv.org, here .


Boards are woefully unprepared for AI. Here’s how they can start to catch up —by Lila MacLellan

IBM AI chief advises people who want a tech job in 2024 to learn the language and creative thinking skills you get with the liberal arts —by Ryan Hogg

These movies do the best job of accurately capturing AI’s power and nuance, according to 10 AI experts —by Kylie Robison

Queen Latifah feels the same ‘nervousness that everyone feels’ about AI, but she’s monetizing her digital avatar. ‘It’s a bell we can’t un-ring’ —by Rachyl Jones

This is the online version of Eye on AI, Fortune 's weekly newsletter on how AI is shaping the future of business. Sign up for free .

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How can generative ai be used to improve customer service and support.

Published: 24 May 2023

The advent of generative AI marks a dramatic leap forward in the realm of automation. Application leaders responsible for customer service should partner with customer service technology vendors to evaluate and adopt the generative AI product innovations that deliver the most value in the near term.

Included in Full Research

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