This blog will explore how generative AI customer experience solutions can help you boost personalization, improve customer satisfaction and loyalty, and enhance operational efficiency.
Lamatic's managed generative AI tech stack can help you achieve these objectives, so you can seamlessly implement an efficient, AI-driven customer experience that enhances personalization and boosts customer satisfaction, loyalty, and operational efficiency.
What Is Generative AI in Customer Support?
Generative AI creates new content based on input data, such as text, images, or audio. For instance, if a user prompts a generative AI tool with, “Write a poem about winter,” the AI will analyze its training data, which includes numerous poems and winter-related content, to produce an entirely original response. This ability to create novel content sets generative AI apart from traditional AI, which can only recognize patterns in existing data to make predictions.
The hyper-successful launch of ChatGPT has catapulted generative AI into boardrooms and living rooms alike. Generative AI has reignited interest in knowledge management, the number-one solution to improve customer experience, employee experience, and operating performance, per Gartner.
How Generative AI Works
Generative AI uses large language models to predict and create text based on the data and content it has been trained on. The more information these models have, the better they generate human-like responses.
For example, ChatGPT, a type of generative AI, was trained on 570 gigabytes of text data from the Internet, books, and other sources. As a result, it can produce remarkably accurate, coherent, and contextually relevant writing on seemingly endless topics.
Why Generative AI Matters for Customer Experience
Research from McKinsey reveals that 59% of customers expect businesses to personalize their experiences based on the available data. Companies must deliver faster, more personalized, and innovative customer experiences across various channels. Using customer service AI tools to meet customer expectations can positively impact revenue generation.
Here are some general statistics highlighting the benefits of AI customer service for organizations:
Salesforce
According to a study by Salesforce, 51% of service decision-makers reported that AI has increased their revenue.
Gartner
A Gartner report predicts that by 2025, AI technologies will be used in 95% of customer interactions, and companies that invest in AI customer experience solutions will see revenue increase by up to 25%.
PwC
In a survey conducted by PwC, 72% of business leaders believe that AI is a business advantage that will help them outperform competitors and increase revenue.
Accenture
According to a study by Accenture, 73% of customers are willing to pay more for a product or service if they receive a personalized experience. AI tools can enable businesses to provide personalized customer experiences, increasing customer satisfaction and revenue.
Harvard Business Review
A report by Harvard Business Review found that companies that leverage AI in customer service can achieve cost savings of up to 30% and experience revenue growth of up to 10%.
Generative AI for Customer Service
The use of AI for customer service has been introduced previously. It has been a vital ingredient of the broader domain of knowledge management, though some might consider it as two separate areas. As a knowledge management pioneer, we have leveraged AI technologies such as machine learning for intent inference and case-based reasoning for conversational guidance in the customer service context, creating transformational business value for our clients.
Generative AI is another exciting new ingredient that can be added to the knowledge management recipe that will enhance various aspects of knowledge management, elevating the business functions knowledge management serves:
- Customer service
- Sales
- Helpdesk
- HR and more
On the flip side, robust knowledge management is critical to the success of generative AI. Suppose generative AI is not part of your knowledge management strategy and tech stack. In that case, it will create yet another silo of inconsistent information, resulting in even more chaos for the business and the customer.
Speeding Up Knowledge Management With Generative AI
The knowledge management process can be broken down into the following essential steps:
- Create
- Curate
- Deliver
- Optimize
Generative AI can help automate or accelerate these steps to speed up time to knowledge and, thus, time to business value. Here are some early examples of use cases pertinent to the knowledge management process and customer service function: Just like anything else, customer service knowledge is garbage in, garbage out.
Content must be consumable, correct, and compliant with regulations and organizational best practices. Contact center agents and, worse, customers must read lengthy documents, although they may have accurate and compliant content, to find answers. This results in poor customer experience and agent experience. Generative AI can create drafts of more consumable and findable content using compliant and correct long-form documents as reference sources.
How Generative AI Is Reshaping Customer Experiences
Meet Customer Expectations with Generative AI
Today’s customers have higher expectations than ever. No technology has highlighted the need for organizations to stay agile and be proactive other than the rise of generative AI (Gen AI). In a world where customers can easily switch to a competitor—with research from Zendesk showing that 52% of customers will switch to a competitor after a single unsatisfactory customer experience—companies that fail to take action to meet new, higher expectations face losing out.
Gen AI is a valuable tool for meeting these expectations, enabling unique, personalized experiences and customer recommendations.
How Gen AI Is Reshaping Customer Experience
Research by Google Cloud has revealed that 97% of retail decision-makers believe that Gen AI will impact customer experience. As explained by Alex Rutter, managing director of AI GTM, EMEA at Google Cloud, for retailers already utilizing AI, the technology has redefined how they understand and engage with customers. “Synergistic advancements in data analysis and chatbot innovation have seen Gen AI elevate the customer experience in two key areas: personalization and automation,” he explains.
On a customer-facing level, Gen AI can unlock personalized shopping through AI-powered virtual agents. Chatbot agents can take the load off retail contact centers by answering customer queries with data-backed recommendations, ensuring customers receive seamless and efficient service.
Examples of Generative AI Improving Customer Experience
We are already seeing AI take flight in business and create meaningful customer experiences. Google Cloud partners are among those leading the charge, reaping this transformative technology’s benefits. Victoria’s Secret, for example, has launched an AI-powered search feature that leverages Google Cloud’s Vision API Product Search to locate specific products in the Victoria’s Secret catalog. Customers can use this tool by dropping an image into the website’s search bar. According to Jim Rudall, Head of EMEA at Intuit Mailchimp, personalization is key for organizations looking to enhance customer experiences.
“In order to set themselves up for success, businesses must prioritize customer experience through personalization. Understanding an audience is the crucial first step to this—and we know that 73% of customers feel more valued when they receive personalized emails. The next step is to use AI-powered tools to scale up customer communications. Such tools will massively augment the existing capabilities of businesses to engage at scale, raising the bar for what activities can be completed quickly whilst driving down the cost of marketing processes and improving ROI.”
Navigating Challenges and Ethical Considerations
Gen AI solutions and offerings reshape industries’ operational, functional, and strategic landscapes. Still, regarding ethics, Herbert explains that CEOs and leaders struggle to know what to do and where to start. “Before organizations begin, they must develop a trusted, responsible AI framework and put in place an ‘AI Centre of Excellence’ to act as air traffic control for any future experiments.”
This responsible AI framework, Herbert says, must cover things like ensuring that models are fair and unbiased, that they are transparent and can be explained, and that there is adequate corporate governance and accountability over data and its use. “This means thinking through how to safeguard consumers' data against unauthorized access, maintain the privacy of consumers’ personal details, and ensure that AI systems don’t have a negative impact on humans, property and the environment.”
The Future of Customer Experience and Generative AI
Consumers want meaningful and personalized experiences, but Rudall explains that it is important to balance this with privacy. Ultimately, personalization is only possible with data, but companies must ensure it's handled responsibly and used only in ways customers approve. “In a recent study of UK and US-based consumers, we found that nearly 3 in 4 customers are comfortable with companies using their personal data if they are transparent with how they are using it—and ultimately deliver a rewarding experience through impactful, personalized messaging.”
Gen AI to drive the evolution of CX Going forward, Rudall predicts that customers will expect far more personalization in how brands communicate with them. “The future of email marketing and customer experience is evolving to be hyper-personalized. AI is accelerating new methods for marketers to connect to their customers, and it will help them tailor their communications for better engagement and impact. This allows teams to focus human creativity towards the ‘why’ and ‘what’ of communication, whilst AI handles the ‘how.’”
Hyper-Personalization and Generative AI
According to Conway, hyper-personalized journeys created by Gen AI promise to transform how companies connect with customers and employees totally. “Using true 360-degree customer data from across sales, marketing, and service functions, it can tailor experiences and determine the “next best action” that will help a company engage a specific customer.” He says.
“For example, a financial services company could use Gen AI to rapidly analyze their own customer data—as well as data from social sources and partner organizations—to determine which customers are most likely to take various actions, from opening a new checking account to investing assets to applying for a loan. Gen AI can then help bankers achieve true one-to-one marketing with a personalized strategy and automated, point-in-time customized offers, translated into the customer’s preferred language.”
Generative AI is the Future of Customer Experience
Research shows that 73% of customers worldwide expect brands to understand their unique needs and expectations. Rutter also advocates a 360-degree approach to customer service, starting with a strong customer insight foundation. “A data-driven approach to retail management helps brands better understand trend forecasts and custom journeys, ensuring that the shopping experience is catered to each customer and their unique needs,” he says. Gen AI is the first technology to integrate front-line service with internal data processing, resulting in retail systems engineered for memorable customer engagement.
Soon, AI-powered retail will become the industry standard, meaning businesses relying on outdated customer service models risk being left behind. Gen AI is adding a new dimension to customer-brand relationships by matching customer expectations with innovative retail solutions.”
Lamatic’s All-in-One Managed Generative AI Tech Stack for Seamless Integration and Deployment
Lamatic offers a comprehensive managed generative AI tech stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on the edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities.
Start building GenAI apps for free today with our managed generative AI tech stack.
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8 Best Low Code GenAI App Builder
1. Lamatic
Lamatic provides a robust managed generative AI tech stack, including managed GenAI middleware, custom GenAI APIs (GraphQL), low-code agent builders, automated GenAI workflows, and integrated vector databases. Their platform automates workflows and ensures production-grade deployment on the edge, enabling rapid, efficient GenAI integration for products needing swift AI capabilities.
2. Softr
Softr tops Zapier's list of no-code app builders because it's easy to use without sacrificing functionality. It’s the fastest way to go from the prompt window to an app close to ready to use. Start on the Softr AI page by selecting a category and entering what you’d like your app to do. After you send in your prompt, Softr captures the gist of what you want, assigns a name to your app, and lets you choose a starting color and design look before it begins generating.
3. Microsoft PowerApps
Microsoft is rolling out Copilot features across its product lineup, and PowerApps is no exception. Featured in Zapier's list of the best internal tool builders, PowerApps is already a powerful app builder offering deep functionality without requiring coding knowledge. It uses a function-based language for app logic, close to the one available in Excel. In the PowerApps dashboard, you can now start a new app from a prompt.
I suggest beginning with a small description of what you’re looking for. After submitting the prompt, you’ll see the generated database schema. A chat window on the right lets you add, remove, or change the fields with prompts. Once you save this, PowerApps will assemble the main page to display and interact with this database. Starting small is better because you can only generate the starting point, not the entire app.
4. Google AppSheet
Spreadsheets are the birthplace of valuable tools, from simple data storage to productivity systems. AppSheet, created by Google and featured in Zapier's list of the best database-powered app builders, takes your spreadsheet and generates an app based on it, complete with a data display and a few forms to help you create new entries. There’s no prompt window anywhere. Most of the AI magic happens in the background as the engine reads your rows and columns to identify what your app is about.
The structure of your sheet dictates the elements you’ll see on the screen. Customization also has very little AI; there’s no sidebar to enter prompts. The control panels are a bit more complex to understand initially, but there are many possibilities. You can leverage smartphone features, such as QR-code reading, which is excellent for creating site-specific tools. You can also train an AI model on your spreadsheet, helping you add prediction features to your apps, or use other tools such as character recognition and intelligent document processing.
5. Quickbase
Quickbase's AI Smart Builder creates internal tools adapted to your prompt with accuracy. Still, beta accepts a more detailed prompt than other options. You’ll start by entering your industry, describing your app's goals, and listing things you want to track, report on, or manage.
Are you having trouble coming up with the perfect prompt? You can click the link on the generation pop-up to access instructions and examples. Once you lock in on the prompt and click create, grab a cup of coffee. Quickbase can take up to 10 minutes to build the tables and user interface.
6. Pico
We're now moving on to the new kids on the block. Pico is the first one, offering an experience that feels like magic. When you add your starting prompt, a line appears on the screen showing the AI generating the code in real-time. It flows between HTML, CSS, and JavaScript with enviable ease, and on the side, your app comes to life bit by bit. With this starting point, you can write more prompts to change anything about the app.
You can change button labels, the background, and the input fields showing on the screen. A few settings help you change the style, visual properties, and connected data—and if you know how to code, you can jump in at any point and edit it yourself. If you don't, don't worry: English is its main programming language.
7. Construct
Construct leans on simple, minimalistic apps. When you start with your prompt, it prepares a product requirement document, a data model, and the interface components. After a minute, your one-page app opens with buttons and lists to display data and input forms.
Visually, the results are always the same: black outlines for all elements, be they lists or buttons. If you try to write a prompt to change any app's visual aspect, it'll say it can't do that. At first, this felt frustrating, but it may be for the best: the baseline design is clean and straightforward, and the focus on functionality makes sense.
8. Create
Where other apps in this list use a combination of a starter prompt and additional ones to continue building, with Create, you'll use a single prompt to generate the whole thing. This is its main advantage and, sometimes, its most significant drawback. You'll notice the input field on the right, inviting you to write your prompt.
Instead of compiling your app's first draft here, I suggest you write it in a separate document. Use ChatGPT to describe features: you can ask to "describe the user interface, including visual and interactive elements with extreme detail" to help Create do the job well.
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Generative AI App Builders vs. Building AI Features Into Apps
Generative AI app builders help you build apps, like chatbots or apps that summarize text faster. They let you use AI to automate parts of the app-building process so you can go from idea to launch more quickly. Once you deploy your app, you can get user feedback and improve it over time with the help of AI.
OpenAI’s ChatGPT, for instance, can help you write better prompts and even code to enhance your application. In contrast, integrating AI into existing applications can help improve the software’s performance and user experience. It also can make your app smarter, as it learns from real-world data over time. The two options have a clear difference, so it’s essential to understand them before diving into app development.
Pros and Cons of Using Generative AI App Builders
Building apps with dedicated generative AI app builders can significantly speed up development. These app builders let you implement AI to automate parts of the app-building process. For example, you can use AI to help you write code or create content for your application.
Using a Dedicated Generative AI App Builder
Doing so can help you get to the finish line faster or at least improve the quality of your app by allowing you to focus on more important features. You can deploy your app sooner and use AI to help you improve it once it’s live. Users can provide feedback that helps you fine-tune your generative AI app; the AI can even help you implement the changes.
Considerations When Using a Dedicated Generative AI App Builder
The main downside of using a dedicated generative AI app builder is that it may create a separate application that doesn’t integrate well with your existing software. Apps built with AI help can also feel different to users since they often rely on AI to function.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic provides an extensive managed generative AI tech stack. The platform's features include:
- Managed GenAI middleware
- Custom GenAI API
- Low-code agent builder
- Automated GenAI workflow
- GenOps
- Edge deployment via Cloudflare Workers
- Integrated vector database (Weaviate).
Managed GenAI Middleware
Managed GenAI middleware acts as a bridge between users' applications and the generative AI model. It handles communication and optimizes performance to ensure smooth operation. Lamatic's managed middleware eliminates the tedious setup and configuration process typically associated with GenAI integration.
Custom GenAI API (GraphQL)
Lamatic's custom GraphQL API allows users to customize the data requests sent to their GenAI model during application runtime. This feature optimizes performance and enables faster response times. The custom API also supports streamlined integration with existing applications and frameworks.
Low-Code Agent Builder
Lamatic's low-code agent builder allows users to create and customize GenAI applications without extensive programming knowledge. Users can build unique agents through an intuitive user interface that easily configures the agent's behavior and personality. This feature accelerates the development process and allows teams to focus on building applications that meet their specific business needs.
Automated GenAI Workflow (CI/CD)
Lamatic's automated GenAI workflow helps users manage GenAI application development and deployment through continuous integration and continuous delivery (CI/CD). This feature streamlines updating applications with new model iterations and performance optimizations. With Lamatic, users can automate workflows to ensure stability and production-grade performance for their GenAI applications.
GenOps (DevOps for GenAI)
GenOps, or DevOps for generative AI, focuses on improving the development and deployment of GenAI applications. Like DevOps, GenOps combines development and operations to improve efficiency and reduce time to market. Lamatic's GenOps features provide the tools to help teams implement GenAI solutions quickly and effectively.
Edge Deployment via Cloudflare Workers
Lamatic's managed generative AI tech stack is built for performance. The platform's edge deployment capabilities via Cloudflare Workers enable users to deploy their GenAI applications closer to their end users, reducing latency and improving performance. Deploying GenAI applications at the edge ensures fast load times and optimal application performance for users regardless of their location.
Integrated Vector Database (Weaviate)
Lamatic's managed generative AI tech stack includes Weaviate, an open-source vector database designed to store unstructured data and make it easily searchable. GenAI models create unstructured data, and as applications run, they can retrieve relevant information from a vector database to optimize performance and improve response accuracy. Weaviate can be integrated with other applications and frameworks, making it a powerful tool for enhancing GenAI applications.
