Enhancing After-Sales Customer Experience by Leveraging Generative AI with Salesforce Automotive Cloud


One of the latest offerings in Salesforce Industry Cloud is Salesforce Automotive cloud. With its launch, most of Automotive industry leaders are looking forward to leverage benefits realized by moving to Salesforce Industry Cloud.

Also, the rise of generative AI has triggered a surge in digital transformation, pushing every industry to adapt and integrate AI in some or the other form. This isn’t simply a desire, but a near-necessity due to the transformative power of generative AI.

This article illustrates some of the key use cases which can potentially be beneficial in the Automotive space for enhancing customer’s After-Sales experience by building Generative AI enabled features in Salesforce Automotive Cloud.

Automotive Cloud:

A typical Original Equipment Manufacturer (OEM) has fragment landscape leading to fragmented experiences and not having one source of truth for majority of the data. Also, the selling models have taken a huge trend upwards with customers wanting to customize the offerings from OEM seamlessly prior to purchase and would be interested to get updates w.r.t after-sales (spares, service etc.).

Automotive Cloud focusses around the customer centricity and provides enhanced customer experience. There are quite a few benefits of moving to any industry cloud solutions (Automotive cloud) as illustrated below:

Benefits of Automotive Cloud:

While the automotive cloud offers pre-built solutions, it becomes easier to set it up for any organization. It promotes Low Code/No Code approach, with minimal customization can achieve complex functionalities.

Generative AI:

Generative AI is one of the outcomes of artificial intelligence (AI) that focuses on creating new and unique content rather than relying on existing data. It generates new data based on the training models that we use.

At the heart of generative AI lies a complex network of artificial neurons, mimicking the human brain’s structure and function. These interconnected nodes, called a neural network, analyze the data, uncovering the hidden language within. These networks are trained on massive datasets of existing content, allowing them to learn the underlying patterns and relationships within that data.

Once the AI has grasped the essence of its data, it’s time to unleash its creativity. Different techniques exist, but two popular methods are:

Generative Adversarial Networks (GANs): There are two actors in GAN – the generator which invents new content and the discriminator which tries to distinguish generated content from real data. As they compete, the generator becomes adept at creating realistic and original outputs, fooling the ever-learning discriminator.

Variational Autoencoders (VAEs): These networks compress the data into a smaller code, like a secret language. They then learn to reverse the process, generating new data points based on this code. Imagine learning a new language for representing images, then using it to paint never-before-seen landscapes.

How Generative AI is disrupting the market:

Generative AI is disrupting the market in several ways by introducing novel capabilities and transforming industries. Here are a few key areas where generative AI is making an impact:

Content Generation:
It can automatically generate articles, scripts, or social media posts, translation etc.

Chatbot & Virtual Assistants:
Chatbots can generate human-like responses, understand user queries, and help across various domains, ranging from customer support to personal productivity.

Product Design and Code Generation:
By generating and exploring numerous design options, it helps with code suggestions there by improving developer’s productivity.

Image and Video Generation:
Create accurate and realistic Images and Videos with given prompt which can be used across different use-cases like marketing, designing, etc.

Generative AI is a powerful tool that augments human creativity, enhances efficiency, and unlocks new possibilities. It has the potential to reshape industries, create new business opportunities, and drive innovation in ways we are only beginning to explore.


Generative AI with Automotive Cloud:

Leveraging Open AI APIs with Automotive Cloud would give Automotive manufacturers and dealers easier way to enhance the After-Sales experience of the customers. With Omni Studio, being integral part of salesforce industries, the development and integration is easier with LCNC (Low Code/No Code) tools.

For non-industry cloud offerings of Salesforce, it can be achieved using custom LWC (Lightning Web components), were this can be achieved with help of developers writing Components and Custom classes to connect with Open AI APIs.

In this article, some the use cases are listed which can be achieved in Automotive cloud integration with Open AI APIs. FlexCards can retrieve the data using Integration Procedure and Data raptor without a single line of code.

Below would be an implementation approach for the same.


Use Case(s):

Using Open AI with Automotive Cloud, Service Advisors fetch personalized suggestions which they can share with Customers without needing customer to visit the workshop/ service center.

Open AI can guide customers through routine maintenance tasks and schedules. It can provide recommendations for oil changes, tire rotations, filter replacements, and other regular maintenance activities based on the manufacturer’s guidelines and the vehicle’s usage. Service Advisors can send customers reminders about upcoming maintenance milestones, ensuring they stay on top of their vehicle’s maintenance needs.

Get Spare parts recommendations:
Create a prompt (example below) and invoke Open AI API requesting for spare parts suggestions with approximate cost.

What parts most likely need to be replaced for a %messageBody:Year% % messageBody:Make% % messageBody:Model% with % messageBody:Milage% miles near % messageBody:City% , % messageBody:State% and average cost by part.

Sample Prompt:
What parts most likely need to be replaced for a 2020 Mercedes-Benz GLA with 45837 miles near Ahmedabad, Gujarat and average cost by part.


Sample Flex Card:
Below Flex card illustrates some Gen AI recommendations


1)      Data Available in Salesforce.

2)      Spare Parts Recommendations from Gen AI based on current vehicle data.

3)      Oil change is recommended as part of initial service by Gen AI.

4)      Tire change is not recommended.

5)      Service is recommended based on last service date.



The application of Generative AI features in Salesforce Automotive Cloud presents an enhanced After-Sales experience to customers. This combined solution allows service representatives to efficiently deliver relevant information on a single page, empowering them to provide more targeted recommendations to customers.

  • Enhanced Customer Experience: Based on available owned vehicle details, Customers receive accurate and personalized recommendations, leading to a faster resolution and a more satisfying interaction.
  • Increased Service Efficiency: Generative AI automates tasks like pulling recommendations based on vehicle information and suggesting part changes/ servicing, freeing up service representatives’ time to focus on complex issues and build stronger customer relationships.
  • Improved First-Call Resolution Rates: With all the necessary information readily available due to Generative AI automations, service representatives can address customer concerns efficiently, minimizing the need for repeat interactions.

This ensures value for both businesses and their customers.

Author Details

Karthik Chakrapani

With over 20 years of experience, Karthik has transitioned from Software Engineering to IT Solution Architecture. He has led large digital transformation programs involving Salesforce Service Cloud and Sales Cloud. Additionally, Karthik brings 6+ years of expertise from the Automotive industry. As the Subject Matter Expert (SME) in Salesforce DevSecOps, he also heads the Salesforce DevOps Center of Excellence (COE).

Leave a Comment

Your email address will not be published.