A friend recently shared a research paper from Oxford Academic about Large Language Models (LLMs) and their human-like biases. I found it fascinating.

The article explains how some groups use LLMs to simulate human participants. Since these models are trained on human-generated data, they can emulate human responses across diverse psychological and behavioral experiments.

It further notes that LLMs favor socially desirable responses that align with the Big Five personality traits – including agreeableness. Notably, during their experiments, LLMs would modify the responses if the researcher appeared to be evaluating it.

Confidently Wrong AI (Posturing)

I had planned to write about the phrase “confidently wrong,” which we hear often when talking about Artificial Intelligence (AI) models. Combined with the concept of AI hallucinations, this can mislead people who are expecting reliable answers from these tools.

More and more users are favoring AI over traditional online search. No doubt you’ve noticed that Google now shows an AI response above the SERP. This experience is often faster and feels more natural than manual trial-and-error clicks through links.

However, it becomes risky when the LLM is wrong. Once the AI selects an answer, it may be reluctant to admit it was wrong. Even if you challenge a correct statement, the LLM might apologize and change its answer to be agreeable. Users need to be cautious and validate the information received.

Confidently Incorrect Humans (Learning)

I have an 11-year-old son who is hell-bent on being contradictory. If I say the sky is blue, he’ll point out that it can be gray, yellow, orange, red, purple, or black. He’s not wrong, but he is frustratingly contrarian. When I tell him he’s being contradictory, he says, “No I’m not!” Even when he is flat wrong, he won’t let it go!

You’ve probably also heard the phrase “fake it ‘till you make it.” It’s meant to help those who are learning and to ease imposter syndrome. I used to hate the phrase because I prefer transparency. I’d rather hear “I don’t know” than to incorrectly think you have it under control. However, I now appreciate that it helps escape a negative mindset.

AI Confidently Mimicking Humans (Refining)

The Oxford Academic article points out that AI learns behaviors from us! It’s mostly trained on data created by humans, so it picks up our natural tendencies. If our writing is polite and avoids confrontation, the AI will be trained to follow those patterns.

Additionally, humans help validate the training – even crowdsourced to the general public. When you give a thumbs up to a response from an LLM, you’re teaching it what you prefer to see in the output. Over time it will lean toward agreeableness. While it’s not conscious, AI is learning to mimic humans.

The Con Man (Tricking)

The term “con man” or “con artist” comes from the word “confidence.” It refers to the act of manipulating or persuading people into believing something false.

Con artists have existed as long as humans have been able to communicate. There are fun ones, like magicians who amaze us with the spectacular. Then there are the bad kind that scam people out of their life savings. Even reputable sources like the BBC, CNN, Forbes, The Atlantic, and others can sometimes spread misleading information, confusing us even further.

AI is trained on a mix of data, including quality sources like scientific research papers but also the text of trolls attacking everything, and your mother, on Reddit. It learns from both the best and the worst of humanity.

The Confident Leader (Inspiring)

Confidence has two sides. We’re often inspired by confident leaders. When leaders seem uncertain, many people get nervous and may leave the group. It’s clear that we prefer a strong front over complete transparency.

Don’t get me wrong… We know that transparency is important. A quick Google search shows droves of experts saying that transparency is the best policy. We also understand the consequences of an over-confident leader.

But at the end of the day, we’re just regular folks looking for stability and security. Time and again, we’re attracted to leaders who exude confidence and instill inspiration.

Conclusion

We often laugh at poorly executed AI – it makes us feel superior. The same goes for poorly articulated statements from people – it makes us feel superior. We’ve all seen how we collectively attack and criticize each other online.

AI learns from us. It relies on us for continual improvement. It adopts our positive traits but can also mimic our negative behaviors.

As we continue to use AI, it will become a bigger part of our lives. Often, we’ll seek out the interaction, while other times, hidden AI will quietly work in the background. Just like with other people, we need to validate our interactions with AI. Trust, but verify.

……

If you are looking for a strong partner that loves AI but will verify results, reach out to your Perficient account manager or use our contact form to begin a conversation.

Artificial Intelligence (AI) has exploded into the mainstream, largely through chatbots and agents powered by Large Language Models (LLMs). Users can now have real-time conversations with multimodal AI tools that understand your text, voice, and images – even documents! The progress has been mind blowing, and tech companies are racing to integrate AI features into their products.

AI features today are being released with obvious interfaces and promoted heavily. My prediction though is that the future of AI will increasingly lean toward hidden, unnoticeable improvements to our daily experiences.

Visible AI – Current State

In our haste to compete, most AI tools today share a similar experience: either a chatbot interface or a feature trigger. What started as fresh and magical is becoming repetitive and forced.

ChatGPT, Bard, Claude… They all share the same conversational interface, resembling many lackluster customer service chatbots. The great ones now offer multimodal capabilities like voice or video input, but the concept is the same – back-and-forth dialogue.

Meanwhile, operating systems, web browsers, word processors, and other apps are tacking on AI features. Typically, these are triggered through a cool new AI icon to generate, summarize, or improve your content.

Invisible Enhancements – Yesterday & Today

Machine Learning (ML), on the other hand, has typically been rolled out as behind-the-scenes improvements that exponentially raise user expectations. Most users don’t even realize what ML processes are at play! Nearly invisible algorithms have transformed industries.

Google revolutionized search with its deceptively simple interface – a single search box delivering surprisingly targeted results. YouTube and Netflix ushered in streaming video, but they gained more attention surrounding their advanced recommendation engines. No more wandering the aisles of the local video store and reading the back of DVD cases!

The banking industry’s automated fraud detection is another perfect example of unobtrusive features. Instead of combing through your bank statement, you are notified in real time that your bank card has been disabled and the funds returned.

AI Ubiquity – Future State

AI is not going away – it offers tremendous opportunities for both businesses and consumers. Like subscription services where businesses cut costs and increase revenue, while the consumers enjoy better experiences, convenience, and options.

However, as with subscription services (access vs ownership), there are trade-offs. AI introduces trust issues, ethical concerns, and bias. Even so, the benefits are likely to outweigh the downsides. AI will reduce cognitive load in your daily life and have a far more natural interaction with digital systems. With AI, exciting products and benefits will be introduced.

Industries like healthcare, finance, automotive, retail, and energy are already exploring AI applications. At first these will be noticeable additions, but over time, AI will become seamlessly integrated and nearly invisible.

Conclusion

There will be bumps along the way (we should learn from our past). Legal disputes and unethical practices are inevitable, but progress will continue. We’ll need to get through some of the bad to reap the benefits – in the same way that fire is crucial to society but can also be destructive – we learn from our mistakes and move forward. Human creativity and innovation have brought us this far, and now we will integrate AI to amplify our potential.

I’m excited to see what is yet to come! We humans get nervous about game-changing technologies, but history shows that we are adept at adding safeguards and correcting our course. I think we’re going to surprise ourselves.

……

If you are looking for a digital partner who is excited about the future of AI, reach out to your Perficient account manager or use our contact form to begin a conversation.

Read more about my thoughts on ecommerce returns on MarTech.

With global ecommerce still on the rise, there’s been a corresponding increase in returns. About 30% of online purchases are returned, compared to just 8.89% from physical store sales. The financial impact is significant, with an estimated annual cost of $400 billion in the United States alone. The rising rate at which customers shop online has exacerbated an already growing challenge of managing product returns in a way that keeps both retailer and customer satisfied.

While securing the bottom line is essential, retailers also understand importance of building customer loyalty through a convenient, efficient, and pleasant return experience. Retailers can flip a usually negative situation – product returns – on its head and use it as an opportunity to build customer loyalty.

Ecommerce Returns: Customer Happiness versus Profitability

Recent statistics show that the average return rate for ecommerce is estimated at 20-30%. Of those returns, 60% are due to fit or quality issues. Online shopping is especially vulnerable to returns since customers simply cannot see the product for themselves before they receive it, and many brands still have work to do in ensuring thorough and accurate product information and inventory data. In a tradeoff for taking the risk, 75% of customers expect free returns and 35% will only shop from retailers with generous return policies.

What Does a Bad Return Experience Often Look Like?

For retailers to fully understand the scope of the issues that are creating a less-than-satisfactory return process, it’s important to gather anecdotal information from customers and pinpoint every place in the journey where the brand could have done better and retained that customer.

If the product doesn’t work as advertised or fit as planned, what is the customer’s course of action? In a bad return experience, it might be extremely difficult to find information on the return policy or how to contact customer service. Customer service might only be available in one or two ways, such as by phone call, email, or contact form. Customer service might not be reasonably timely in their response or reroute the customer to other departments and representatives. The process might require the customer to prove what they claim to be wrong about the item, deny their claims, and/or make the customer pay for the return shipping themselves.

A Hassle-Free Return Experience

In comparison, a great return experience that retains a customer might look like a partnership with a brick-and-mortar store that allows customers to return products in person with a free return label. It might mean that the customer does not have to have a conversation with customer service at all, but simply requests a label to return the product. It might mean that customer service is available over multiple forms of communication if the customer needs help, such as through online chat, text, and more. The smoother the process, the greater the probability of shopping again with the retailer.

Technology’s Role in Improving the Returns Process

While technology can be an incredible solution for return inefficiencies and frustrations, it can also cause problems when implemented improperly. For example, a brand might implement a chat feature for customer service, but the chat lags, conversations are deleted when the customer’s device switches screens, speaking to a live agent is not an option, and more. Virtual assistants and chatbots can be useful if they communicate clearly and respond in a timely manner, especially as they become more advanced and able to understand frustration, taking on a more empathetic approach.

No matter the solution, it’s crucial that it streamlines and enhances rather than hinders and complicates the customer experience. The best way to ensure technological solutions for returns result in the desired outcomes is to include these points in your strategy:

• Ensure your inventory includes clear descriptions, helpful data, and accurate imagery of products to reduce returns caused by mismatched expectations while also giving your technology, like an AI chatbot, the comprehensive data to use when curating information for a customer
• Offer accurate sizing guides and virtual try-on or see-in-room technologies to address fit and dimension issues
• Partner with brick-and-mortar stores and offer free shipping label printing for easy drop-off returns
• Implement efficient troubleshooting processes to resolve issues and help customers avoid returns
• Train customer service representatives to handle return requests promptly and empathetically

Effective returns management will not only improve customer satisfaction and retention, but also the retailer’s bottom line. Effective use of data and analytics can help predict and prevent returns as well as improve visibility in the supply chain, and developing more sustainable return processes can reduce environmental impact. Not to mention, the implementation of AI-powered chatbots that can provide instant, accurate assistance while maintaining a human touch can satisfy customers while reducing call center volume and demand on resources.

Turning returns into opportunities to build customer loyalty is critical in the competitive ecommerce landscape. It’s only through clear communication, efficient processes, and customer-centric policies that the returns process can become a competitive advantage, fostering loyalty and long-term success.

Develop a strong returns strategy with our retail + distribution, consumer goods, and commerce expertise.

To read more about my personal experiences with returns that led to this article, visit my article on MarTech.

When I started looking for my next car, the questions did not stop at whether to buy an internal combustion engine (ICE), an electric vehicle (EV), or a hybrid. I wanted to be a well-informed buyer before I made my final decision. I wanted to not only know all the information needed to make the optimal car selection but also wanted a hassle-free buying experience that included a future maintenance and services plan that works well for me. This got me thinking about automotive commerce and how businesses can leverage order management to help improve the car-buying experience for their customers.

We hear a lot about automotive commerce. This concept of buying automotives, parts, and accessories is nothing new. What has changed is the buying, processing, and follow-through journeys for the customer and the selling channels for the manufacturers and dealers. Let’s start with a one-sentence definition of automotive commerce. It entails all the end-to-end customer journeys, supply chain processes, tasks, and activities that go into selling, renting, and maintaining vehicles and automotive equipment.

Traditionally, customers would browse through paper catalogs or go to a physical dealership with limited inventory to browse for and buy cars. Modern customers have become more savvy. Ninety-five percent of car shoppers rely on online resources to gather information, bypassing dealerships as their starting point. They expect online research and comparison tools, broader and configurable product assortments, clear pricing that eliminates the negotiation process, flexible financing, self-served test drive scheduling, and delivery or pickup options.

So, where does order management fit into this? Concentrating on these areas will provide customers, manufacturers, and dealers with an enhancement of buy-to-sustain processes.

AI-infused Customer Service Chatbots and Virtual Agents

Chatbots have seen an enormous increase and adoption in the last three to five years. The chatbot market is set to expand at a remarkable 23.3% annually due to increasing demand for 24x7x365 customer services and a need to reduce operational costs. Chatbots accelerate response times, delivering answers faster on average and reducing human resource availability for live customer communication.

Empower and supplement the automotive customer service representative team with chatbots and virtual agents to help provide instant information to customers when they’re enquiring about vehicle details, comparative insights, configured pricing, financing options, test drive appointments, delivery/pickup options, and order status.  With 74% of online users preferring interacting with chatbots when looking for answers to straightforward questions, it has become critical for businesses to respond quickly and efficiently to customer questions.

According to HubSpot, 40% of consumers have no preference when it comes to engaging with a chatbot or a human for help, as long as they receive the support they need including services like scheduling for service and repair requests, information on replacement parts and warranty, and assistance with future maintenance schedules.

Managing Inventory Levels

Auto manufacturers and dealers are seeing a rise in days of supply. Nationwide, days of supply is averaging between 75 and 85 days that include domestic and international auto brands, and it’s trending upwards. As this industry expands with newer auto players and a larger dealer footprint, manufacturers closely track their manufacturing schedules and inventory balance positions while dealers look at efficiently managing product assortments and reducing overstocking in their showrooms and warehouses.

Empower manufacturers to improve their manufacturing operations by managing inventory levels, providing forecasting accuracy which can help optimize production fulfillment based on cost and geography. Automate the supply chain process that streamlines regulatory approvals and documentation that would ultimately result in reducing delivery times. Enable dealers to make data-driven decisions by providing them accurate supply and demand picture and flexibility to effectively manage, track, and audit their stock.

Dealer Integrations 

Seamless integration with suppliers and dealers to provide visibility to their orders and flexibility in managing their business processes is a key aspect to efficiently managing and providing proactive and enhanced customer experience. Dealers risk losing 37% of online leads through missed follow-up with a need for a seamless web-to-buy customer journey.

Empower the enterprise and its extended fulfillment network to leverage the capabilities of order management as an order and inventory hub that orchestrates and manages their order and optimizes inventory. Enable order modifications and processing flexibility for dealers to cater to their customers in real time. Provide visibility to order tracking and history that enables them to better serve their customers.

Maintenance and Repair

Manufacturers, dealers, and local service shops alike have a huge opportunity and incentive to provide a higher quality of post-purchase services and repeat customers. According to Infinity business insights, the Global Automotive Repair and Maintenance Service Market Size is projected to reach outstanding growth with a compound annual growth rate (CAGR) of 7.6% during 2024-2032.

Empower dealers and maintenance service providers to follow through with customers after the sale. Enable capabilities for services related to vehicle maintenance, repairs, and servicing. This includes proactive customer communication and monitoring for routine maintenance and diagnostics, informing about recalls and repairs, and recommendations on upgrades and new and latest accessories.

In summary, order management systems are well aligned to play a crucial role in these areas by enhancing the overall automotive commerce experience through improved customer service, efficient inventory management, seamless and automated integrations, and following through with exceptional future maintenance and support services.

In the contemporary era of digital platforms, capturing and maintaining user interest stands as a pivotal element determining the triumph of any software. Whether it’s websites or mobile applications, delivering engaging and tailored encounters to users holds utmost significance. In this project, we aim to implement DevSecOps for deploying an OpenAI Chatbot UI, leveraging Kubernetes (EKS) for container orchestration, Jenkins for Continuous Integration/Continuous Deployment (CI/CD), and Docker for containerization.

What is ChatBOT?

A ChatBOT is an artificial intelligence-driven conversational interface that draws from vast datasets of human conversations for training. Through sophisticated natural language processing methods, it comprehends user inquiries and furnishes responses akin to human conversation. By emulating the nuances of human language, ChatBOTs elevate user interaction, offering tailored assistance and boosting engagement levels.

What Makes ChatBOTs a Compelling Choice?

The rationale behind opting for ChatBOTs lies in their ability to revolutionize user interaction and support processes. By harnessing artificial intelligence and natural language processing, ChatBOTs offer instantaneous and personalized responses to user inquiries. This not only enhances user engagement but also streamlines customer service, reduces response times, and alleviates the burden on human operators. Moreover, ChatBOTs can operate round the clock, catering to users’ needs at any time, thus ensuring a seamless and efficient interaction experience. Overall, the adoption of ChatBOT technology represents a strategic move towards improving user satisfaction, operational efficiency, and overall business productivity.

Key Features of a ChatBOT Include:

1. Natural Language Processing (NLP): ChatBOTs leverage NLP techniques to understand and interpret user queries expressed in natural language, enabling them to provide relevant responses.
2. Conversational Interface: ChatBOTs utilize a conversational interface to engage with users in human-like conversations, facilitating smooth communication and interaction.
3. Personalization: ChatBOTs can tailor responses and recommendations based on user preferences, past interactions, and contextual information, providing a personalized experience.
4. Multi-channel Support: ChatBOTs are designed to operate across various communication channels, including websites, messaging platforms, mobile apps, and voice assistants, ensuring accessibility for users.
5. Integration Capabilities: ChatBOTs can integrate with existing systems, databases, and third-party services, enabling them to access and retrieve relevant information to assist users effectively.
6. Continuous Learning: ChatBOTs employ machine learning algorithms to continuously learn from user interactions and improve their understanding and performance over time, enhancing their effectiveness.
7. Scalability: ChatBOTs are scalable and capable of handling a large volume of concurrent user interactions without compromising performance, ensuring reliability and efficiency.
8. Analytics and Insights: ChatBOTs provide analytics and insights into user interactions, engagement metrics, frequently asked questions, and areas for improvement, enabling organizations to optimize their ChatBOT strategy.
9. Security and Compliance: ChatBOTs prioritize security and compliance by implementing measures such as encryption, access controls, and adherence to data protection regulations to safeguard user information and ensure privacy.
10. Customization and Extensibility: ChatBOTs offer customization options and extensibility through APIs and development frameworks, allowing organizations to adapt them to specific use cases and integrate additional functionalities as needed.

Through the adoption of DevSecOps methodologies and harnessing cutting-edge technologies such as Kubernetes, Docker, and Jenkins, we are guaranteeing the safe, scalable, and effective rollout of ChatBOT. This initiative aims to elevate user engagement and satisfaction levels significantly.

I extend our heartfelt appreciation to McKay Wrigley, the visionary behind this project. His invaluable contributions to the realm of DevSecOps have made endeavors like the ChatBOT UI project achievable.

Pipeline Workflow

Let’s start, building our pipelines for the deployment of OpenAI Chatbot application. I will be creating two pipelines in Jenkins,

1. Creating an infrastructure using terraform on AWS cloud.
2. Deploying the Chatbot application on EKS cluster node.

Prerequisite: Jenkins Server configured with Docker, Trivy, Sonarqube, Terraform, AWS CLI, Kubectl.

Once, we successfully established and configured a Jenkins server, equipped with all necessary tools to create a DevSecOps pipeline for deployment by following my previous blog. We can start building our DevSecOps pipeline for OpenAI chatbot deployment.

First thing, we need to do is configure terraform remote backend.

1. Create a S3 bucket with any name.
2. Create a DynamoDB table with name “Lock-Files” and Partition Key as “LockID”.
3. Update the S3 bucket name and DynamoDB table name in backend.tf file, which is in EKS-TF folder in Github Repo.

Create Jenkins Pipeline

Let’s login into our Jenkins Server Console as you have completed the prerequisite. Click on “New Item” and give it a name, select pipeline and then ok.

I want to create this pipeline with build parameters to apply and destroy while building only. You must add this inside job like the below image.

Let’s add a pipeline, Definition will be Pipeline Script.

pipeline{
    agent any
    stages {
        stage('Checkout from Git'){
            steps{
                git branch: 'main', url: 'https://github.com/sunsunny-hub/Chatbot-UIv2.git'
            }
        }
        stage('Terraform version'){
             steps{
                 sh 'terraform --version'
             }
        }
        stage('Terraform init'){
             steps{
                 dir('EKS-TF') {
                      sh 'terraform init'
                   }      
             }
        }
        stage('Terraform validate'){
             steps{
                 dir('EKS-TF') {
                      sh 'terraform validate'
                   }      
             }
        }
        stage('Terraform plan'){
             steps{
                 dir('EKS-TF') {
                      sh 'terraform plan'
                   }      
             }
        }
        stage('Terraform apply/destroy'){
             steps{
                 dir('EKS-TF') {
                      sh 'terraform ${action} --auto-approve'
                   }      
             }
        }
    }
}

Let’s apply and save and build with parameters and select action as apply.

Stage view it will take max 10mins to provision.

Blue ocean output

Check in Your Aws console whether it created EKS cluster or not.

Ec2 instance is created for the Node group.

Now let’s create new pipeline for chatbot clone. In this pipeline will deploy chatbot application on docker container after successful deployment, will deploy the same docker image on above provisioned eks cluster.

Under Pipeline section Provide below details.

Definition: Pipeline script from SCM
SCM : Git
Repo URL : Your GitHub Repo 
Credentials: Created GitHub Credentials
Branch: Main
Path: Your Jenkinsfile path in GitHub repo.

Apply and Save and click on Build. Upon successful execution you can see all stages as green.

Sonar- Console:

You can see the report has been generated and the status shows as failed. You can ignore this as of now for this POC, but in real time project all this quality profile/gates need to be passed.

Dependency Check:

Trivy File scan:

Trivy Image Scan:

Docker Hub:

Now access the application on port 3000 of Jenkins Server Ec2 Instance public IP.

Note: Ensure that port 3000 is permitted in the Security Group of the Jenkins server.

Click on openai.com(Blue in colour)

This will redirect you to the ChatGPT login page where you can enter your email and password. In the API Keys section, click on “Create New Secret Key.”

Give a name and copy it. Come back to chatbot UI that we deployed and bottom of the page you will see OpenAI API key and give the Generated key and click on save (RIGHT MARK).

UI look like:

Now, You can ask questions and test it.

Deployment on EKS

Now we need to add credential for eks cluster, which will be used for deploying application on eks cluster node. For that ssh into Jenkins server. Give this command to add context.

aws eks update-kubeconfig --name <clustername> --region <region>

It will generate a Kubernetes configuration file. Navigate to the directory where the config file is located and copy its contents.

cd .kube
cat config

Save the copied configuration in your local file explorer at your preferred location and name it as a text file.

Next, in the Jenkins Console, add this file to the Credentials section with the ID “k8s” as a secret file.

Finally, incorporate this deployment stage into your Jenkins file.

stage('Deploy to kubernetes'){
            steps{
                withAWS(credentials: 'aws-key', region: 'ap-south-1'){
                script{
                    withKubeConfig(caCertificate: '', clusterName: '', contextName: '', credentialsId: 'k8s', namespace: '', restrictKubeConfigAccess: false, serverUrl: '') {
                       sh 'kubectl apply -f k8s/chatbot-ui.yaml'
                  }
                }
            }
        }
      }

Now rerun the Jenkins Pipeline again.

Upon Success:

In the Jenkins give this command

kubectl get all
kubectl get svc #use anyone

This will create a Classic Load Balancer on the AWS Console.

Copy the DNS name and paste it into your browser to use it.

Note: Do the same process to get OpenAI API Key and add key to get output on Chatbot UI.

The Complete Jenkins file:

pipeline{
    agent any
    tools{
        jdk 'jdk17'
        nodejs 'node19'
    }
    environment {
        SCANNER_HOME=tool 'sonar-scanner'
    }
    stages {
        stage('Checkout from Git'){
            steps{
                git branch: 'main', url: 'https://github.com/sunsunny-hub/Chatbot-UIv2.git'
            }
        }
        stage('Install Dependencies') {
            steps {
                sh "npm install"
            }
        }
        stage("Sonarqube Analysis "){
            steps{
                withSonarQubeEnv('sonar-server') {
                    sh ''' $SCANNER_HOME/bin/sonar-scanner -Dsonar.projectName=Chatbot \
                    -Dsonar.projectKey=Chatbot '''
                }
            }
        }
        stage("quality gate"){
           steps {
                script {
                    waitForQualityGate abortPipeline: false, credentialsId: 'Sonar-token' 
                }
            } 
        }
        stage('OWASP FS SCAN') {
            steps {
                dependencyCheck additionalArguments: '--scan ./ --disableYarnAudit --disableNodeAudit', odcInstallation: 'DP-Check'
                dependencyCheckPublisher pattern: '**/dependency-check-report.xml'
            }
        }
        stage('TRIVY FS SCAN') {
            steps {
                sh "trivy fs . > trivyfs.json"
            }
        }
        stage("Docker Build & Push"){
            steps{
                script{
                   withDockerRegistry(credentialsId: 'docker', toolName: 'docker'){   
                       sh "docker build -t chatbot ."
                       sh "docker tag chatbot surajsingh16/chatbot:latest "
                       sh "docker push surajsingh16/chatbot:latest "
                    }
                }
            }
        }
        stage("TRIVY"){
            steps{
                sh "trivy image surajsingh16/chatbot:latest > trivy.json" 
            }
        }
        stage ("Remove container") {
            steps{
                sh "docker stop chatbot | true"
                sh "docker rm chatbot | true"
             }
        }
        stage('Deploy to container'){
            steps{
                sh 'docker run -d --name chatbot -p 3000:3000 surajsingh16/chatbot:latest'
            }
        }
        stage('Deploy to kubernetes'){
            steps{
                withAWS(credentials: 'aws-key', region: 'ap-south-1'){
                script{
                    withKubeConfig(caCertificate: '', clusterName: '', contextName: '', credentialsId: 'k8s', namespace: '', restrictKubeConfigAccess: false, serverUrl: '') {
                       sh 'kubectl apply -f k8s/chatbot-ui.yaml'
                  }
                }
            }
        }
        }
    }
    }

I hope you have successfully deployed the OpenAI Chatbot UI Application. You can also delete the resources using the same Terraform pipeline by selecting the action as “destroy” and running the pipeline.

Salesforce Experience Cloud is the platform for creating top-notch customer experiences. But what if you could amplify its capabilities even further with the power of Artificial Intelligence (AI) and Data Cloud? At the recent Salesforce Connections 2024 event, the focus was all about AI, Data, and CRM, highlighting how these technologies are revolutionizing customer engagement. This dynamic combination can transform your Experience Cloud site into a personalized, data-driven powerhouse.

The Data Cloud Advantage

Unified Customer View:

Experience Cloud engages with customers across various touchpoints. Data Cloud acts as a central hub, consolidating customer data from Salesforce and external sources. This unified view lets you tailor experiences based on individual preferences and past interactions.

Real-Time Insights:

Imagine understanding customer behavior as it happens! Data Cloud provides real-time insights into customer activity across channels. Use this to personalize content, product recommendations, and support in real-time, boosting engagement and satisfaction.

Advanced Segmentation:

Say goodbye to generic experiences. Data Cloud enables you to create highly targeted segments based on demographics, purchase history, and website behavior. Deliver hyper-personalized experiences that resonate with each customer group.

AI: The Intelligence Behind the Experience

Einstein Co-Pilot for Partners

AI analyzes customer data to recommend relevant content, products, or services on your Experience Cloud pages. This not only enhances customer satisfaction but also drives conversions.

Image from Salesforce Connections keynote 2024

Predictive Search:

AI powers intelligent search functionalities, anticipating customer queries and suggesting relevant results before they even finish typing. This streamlines navigation and helps customers find what they need quickly.

Chatbots and Virtual Assistants:

AI chatbots provide 24/7 customer support, answer basic questions, and route complex inquiries to human agents. This frees up your team’s time while ensuring customers receive prompt assistance.

Image is from a Salesforce official chatbot marketing site.

Putting it All Together:

By combining Data Cloud’s unified data platform with AI’s intelligent capabilities, you can create a truly transformative Experience Cloud site.

Here’s an example:

Imagine a customer browsing an Experience Cloud site for running shoes. Data Cloud recognizes the user as a returning customer with a preference for lightweight shoes. Using AI, the platform analyzes the customer’s past purchases and website behavior. Based on this data, the Experience Cloud displays personalized recommendations for new lightweight running shoes, along with helpful CMS content or blog posts about running techniques. The customer may have questions. A chatbot can pop up directly on the site and answer questions in real-time without the need for a customer service agent. A complete, comprehensive, data-driven customer experience completely handled by the Experience Cloud site, data, and AI.

Embrace the Future:

Salesforce Connections 2024 emphasized that AI is the new UI. We’re entering an era where AI doesn’t just build your website—it can be your website. Imagine visiting a brand’s website and instead of static content, you’re interacting with dynamic prompts driven by AI from your CRM, creating a fully personalized styling experience. Data is the foundation for these exceptional customer experiences, enabling us to work faster and more efficiently. With AI and Data Cloud’s native integration, every engagement becomes more relevant for customers and accounts.

CRMs built with AI and data are reimagining customer engagement. New frontiers like Commerce GPT and Customer GPT, along with Einstein GPT’s access to all customer data within Customer 360, allow for recommending amazing merchandise and even enabling customers to pay directly in the chat.

Perficient can help you craft personalized experiences, anticipate customer needs, and ultimately drive growth and customer loyalty.

By embracing Data Cloud and AI, you can unlock new possibilities for your Experience Cloud. Craft personalized experiences, anticipate customer needs, and ultimately drive growth and customer loyalty.

Hello Trailblazers!

Welcome to the Series of Salesforce Einstein Chatbots- Part 4

In today’s digital world, businesses continuously looking for new methods to improve customer interaction and accelerate support procedures. One such solution offered by Salesforce is the Einstein Bot Builder, a powerful tool that empowers organizations to create intelligent chatbots tailored to their specific needs.

If you do not know what Salesforce Einstein Bot is and How to create Einstein Bot, then you can follow these links to get a fair idea.

In this blog post, we’ll explore the Einstein Bot Builder page’s features, functionalities, and capabilities.

So, let’s get started…

Introduction to Einstein Bot Builder

The Einstein Bot Builder is a feature-rich platform within the Salesforce ecosystem that enables businesses to design, build, and deploy AI-powered chatbots effortlessly. With its intuitive interface and robust set of tools, organizations can create conversational experiences that drive customer satisfaction and improve operational efficiency.

There are seven major components in the Einstein Bot Builder page as shown in the above screenshot.

Let’s decode each one by one.

1. Overview of Einstein Bot:

The Overview section summarizes your chatbot project, including its Name, API Name, description, type, and creation date.

1. You can add your bot’s language in the ‘Conversation Languages’ section. Refer to the screenshot below.
   
2. In the Connections section, click Add to connect your bot with the deployment channel. We’ll learn about this in another blog. Also, you can add other messaging channel connections, as shown below.
   

You can also add Inbound Omni-Channel Flows that route conversations to your bot based on the rules defined in the flow. The Settings section offers a few functionalities, such as Bot Response Delay, Log Conversations, and Outbound Omni-Channel Flow.

Note: To work with WhatsApp, Facebook, and Text(SMS) connections, you need to enable the Messaging Setting from Setup first.

2. Dialog in Einstein Bot

Dialogs are the heart and soul of the Einstein Bot builder. These individual conversation modules within your chatbot define the interaction flow between the bot and the user. You can create, edit, and manage these conversation modules in the Dialogs section.

Each dialog typically represents a specific task or topic that the chatbot can assist with, such as answering FAQs, collecting user information, or providing product recommendations.

You build the actual bot by using the components in the Dialog Component Library, such as Messages, Questions (Static and Dynamic), Actions, Rules, etc. Refer to the screenshot below.

3. Entities

Entities are specific pieces of information that the chatbot can extract and process from user inputs. In the Entities section, you define the types of entities your chatbot can recognize and handle, such as dates, times, Boolean, email addresses, objects, locations, phone numbers, product names, customer preferences, etc.

By identifying entities, the chatbot can understand and respond intelligently to user queries, leading to more personalized interactions. Refer to the screenshot below.

4. Variables

Variables are dynamic placeholders used to store and manipulate data during conversation sessions. In the Variables section, you can define custom variables and manage their values throughout the conversation.

Variables can store user inputs, system responses, or any other relevant information needed to fulfill user requests or execute tasks.
Refer to the screenshot below.

5. Goals

Goals represent specific objectives or outcomes you want your chatbot to achieve during user interactions. In the Goals section, you define and track these objectives to measure the effectiveness and success of your chatbot. Here you need to select Dialog. (optional)

Common goals may include completing a purchase, scheduling an appointment, resolving a support issue, or providing accurate information to users. Refer to the screenshot below.

6. Performance

The Performance section provides insights and analytics related to the performance of your chatbot. The dashboard section allows you to view key metrics such as conversation volume, user satisfaction scores, response times, and completion rates. The event logs section gives you the bot session’s information.

7. Model Management

Model Management refers to the management of machine learning models used by the chatbot for natural language understanding and processing. In this section, you can train, evaluate, and deploy machine learning models to improve the chatbot’s ability to accurately understand and respond to user inputs.

Things to Keep in Mind with Einstein Bots

1. Activate: Activate your bot once you are done with building it. And you can not edit an active bot.
2. Preview: In a Standard bot, there are two ways to preview your bot. a) Rich Content Preview and b) Text Preview. But you can only “Text Preview” your bot in the Enhanced bot.
3. Revert: While building the bot, if it feels that any functionality is going wrong, use the “Revert” button.
4. Save: Often save your bot. When you jump from one dialog to another, ensure your current dialog activity is saved.

So, with this, we see that an Einstein Bot builder page has many components in it, and each one is special on its own and performs different functions.

Conclusion

By understanding and effectively utilizing each Einstein Bot Builder page component, you can design and deploy intelligent chatbots that deliver exceptional user experiences and drive meaningful outcomes for your organization.

Happy Reading!

The dream is not that which

You see while sleeping;

It is something that

does not let you sleep…

Related Posts of Einstein Bot:

1. Salesforce Einstein Chatbot
2. Chat with Customers by Einstein Bot
3. Learn Salesforce Einstein Bot

You Can Also Read:

1. An Introduction to Salesforce CPQ
2. Salesforce CPQ and its Key Features
3. Unlocking the Power of AI: Einstein for Developers
4. Revolutionizing Customer Engagement: The Salesforce Einstein Chatbot

Introduction:

A chatbot, often known as a bot, is a computer programme that replicates a human conversation, whether written or spoken, and can be a beneficial tool for automating interactions with users.

Salesforce Einstein Chatbot is a chatting tool that is driven by Salesforce’s AI. This platform allows businesses to create and deploy chatbots in a variety of business processes involving real-time interaction with customers.

In this blog, we are going to learn the simple overview of Einstein Bot covering it’s all the aspects. So, let’s get started.

Salesforce Einstein Bot:

Einstein bots are part of Salesforce Service Cloud (now in Sales Cloud too). In today’s fast paced world, every customer likes quick responses without delay. Thus, the main purpose of Einstein bot is to interact with customers quickly and accurately without waiting for a human agent.

Einstein bots can send messages, ask questions, evaluate insights, calculate possible outcomes, and perform the actions based on the rules defined or based on the customer input. It also detects data complications and promptly sends the case to a Live Agent in the queue to handle the difficulties efficiently. Later, if necessary, the agent can review the interaction between the customer and the bot to provide appropriate help.

Salesforce uses Natural language understanding (NLU) or Natural Language processing (NLP) technology, making Salesforce Einstein smart.

Pre-requisites:

To begin using the Salesforce Einstein Bot, be sure to meet the following requirements:

1. Salesforce Edition: Einstein Bots are available in specific Salesforce editions, such as Enterprise Edition, Unlimited Edition, and Performance Edition. Ensure that your Salesforce instance is on a compatible edition.
2. Enabled Features: Make sure you have the “Service Cloud” feature enabled in your Salesforce organization, as Einstein Bots are part of the Service Cloud offering.
3. Lightning Experience: Enable Lightning Experience in your org.
4. Chat Feature: Enable Chat feature in your org (shown below).
5. Salesforce Knowledge: If you want your bots to offer Knowledge Articles to customers, then enable Salesforce Knowledge.

Licensing:

Salesforce Einstein Bots require specific licensing to be used within your Salesforce organization. You must be on Enterprise, Performance, Unlimited, or Developer edition to use Einstein Bots.

Salesforce Einstein Bots are primarily available with Service Cloud licenses. It includes Service Cloud Professional, Enterprise as well as Unlimited editions.

And Salesforce recently announced and brought the Salesforce Einstein Bot for Sales Cloud from Summer ’23 release.

Note- Here, I’m using Salesforce Developer Edition to perform Einstein Bot activities.

Enable Service Cloud Permission:

To enable service cloud permission, you can follow the below steps.

1. Go to the Quick Find Box and enter User.
2. Click User.
3. Select the User. (Here, I’m selecting System Administrator)
4. Now, you need to enable the service cloud user checkbox from the user detail page as shown below.
   
5. Click Save.

Enable Chat:

To use the Einstein bot, it should be connected to the chat feature or messaging platform. Thus, it is important to enable Chat feature and Chat user in your org.

1. Enter “Chat” in the Quick Find Box.
2. Click “Chat Setting”.
3. Check the Enable Chat checkbox as shown in the figure below.
   
4. Click Save.

To Enable the Chat User, follow the below steps:

1. Enter User in the Quick Find Box and click Users.
2. Select the required user.
3. Now, you need to enable chat user checkbox from the user detail page as shown below.
   
4. Click Save.

So, if you try to enable chat user checkbox first, it won’t be visible in the user detail page unless you enable the “Chat Feature” as discussed above.

Enable Einstein Bot:

To enable the Einstein bot feature in your org, follow the below steps.

1. Enter and search “Einstein Bot” in the Quick Find Box.
2. Enable the Einstein Bot feature by clicking on enable button as shown in figure.
3. A popup will be shown stating Privacy Policy and Terms and Conditions. Check the checkbox.
4. Click on “Try Einstein” button.

In this way, you’ve successfully enabled the Einstein Bot Feature in your org.

Benefits of Einstein Bot:

1. Enhanced Customer Engagement: Einstein Bots provide immediate, 24/7 customer support, ensuring that your customers can obtain help whenever they need it. This leads to higher customer satisfaction and improved engagement.
2. Efficiency/Productivity: Bots handle routine and repetitive tasks, freeing up human agents to focus on more complex and strategic tasks that require human intervention.
3. Cost Efficient: Einstein bots eliminate the requirement for human customer service representatives to stay on the phone or in the chatroom to handle every customer query. Because chatbots do this activity using Natural Language Understanding technology, saving firms money on staff costs.
4. Data-driven Insights: This Chatbot collects data using information gathered from customer interactions.
5. Multichannel Support: Einstein Bots can be deployed across various communication channels, including websites, messaging apps, and social media, providing consistent support wherever customers are.

Let’s Talk about some Best Practices: –

1. To ensure a smooth launch, start with small and defined use cases. You can gradually expand the bot’s powers as you acquire experience.
2. Use customer data to personalize interactions. Use Salesforce CRM data to deliver relevant recommendations and solutions.
3. Test the bot extensively before deployment. Collect feedback from users and continuously iterate to improve its performance over time.
4. Continuously monitor bot interactions, examine metrics, and make data-driven changes to improve performance.
5. To ensure accurate and relevant responses, keep the bot up to date with changes in your products, services, and operations.

In the next part of this blog, we will learn to create a live Chatbot along with step by step procedure, working with it’s dialog and menu feature and many more things.

Conclusion:

Salesforce Einstein Bot is the powerful tool you can use to automate your customer experiences and increase engagements as well. Through 24/7 availability, instant responses, and consistent engagement, Einstein Bot redefines the way businesses connect with their audience. So only need to keep pre-requisites and best practices in mind, then you are good to go with Chatbot.

To be Continued…

Related Articles:

1. Salesforce Einstein Bot 
2. Planning the Bot Content

Previous Blog Posts:

1. Understanding Salesforce Knowledge

2. Introduction to the Approval Process in Salesforce

3. Multi-Step Approval Process in Salesforce using Standard Setup Wizard.

4. Introduction & Use of B2C Commerce Template in Experience Cloud Salesforce

The battle to use artificial intelligence is heating up as companies worldwide are investing heavily in the technology. Companies are investing in AI to gain a competitive edge in the market, as well as to automate processes and increase efficiency.

Google on Monday released its own chatbot similar to ChatGPT, called Google Bard, as the battle to use artificial intelligence heats up.

Bard is an experimental conversational AI service powered by LaMDA. Built using our large language models and drawing on information from the web, it’s a launchpad for curiosity and can help simplify complex topics → http://goo.gle/3HBZQtu.

Bard aims to “combine the breadth of the world’s knowledge with the power, intelligence, and creativity of [Google’s] large language models” by drawing from information around the web and presenting it in fresh, easy-to-understand ways.

Bard is a new voice-based search technology that allows users to perform web searches using their voice. This technology allows users to simply speak their search queries into their devices, and Google will return relevant results.

One of the key benefits of Google Bard is that it makes searching the web much faster and more convenient. Rather than having to type out long search queries, users can simply speak their query and get instant results. Additionally, Google Bard can help users save time by providing more relevant results. For example, if a user says, “show me the latest news on Elon Musk,” Google Bard will present the most recent articles about Elon Musk rather than having to sift through multiple pages of irrelevant results.

Another benefit of Google Bard is that it is designed to be highly accurate. Google’s speech recognition technology has been fine-tuned over many years, and the company has invested heavily in ensuring that the technology works well for various accents and languages. This means that no matter where you are in the world, you can use Google Bard to quickly and easily find the information you’re looking for.

In conclusion, Google Bard is a powerful tool that makes it easier and faster for users to find information on the web. Whether you’re looking for news, product reviews, or simply answers to your questions, Google Bard makes it easier to get the information you need quickly and accurately.

Picture this (Also, metaphorically, you link it with your department): You are working remotely as your office is testing the hybrid workplace model when your VPN connection suddenly fails, causing a delay in sending an important email to a client. Attempts to contact the IT helpdesk are unsuccessful as they also work remotely. A ticket is eventually raised, and the issue is resolved with a simple reboot, but the delay causes the email to be overdue. Implementing a self-service portal could have further reduced the turnaround time in this situation. 

What if you could have done it all on your own? Yes, we have now discussed IT-related concerns; consider B2B, B2C, and D2C; billions of customers across the world are waiting for brands to answer their inquiries, which can be readily answered with ChatBot, and organizations and brands can easily benefit from it:  

• They reduce consumer wait times. 
• They aid in resolving support cases. 
• They provide you with leads. 
• They enable your consumers to help themselves. 

Think of chatbots as personal shopping assistants for your customers. By analyzing every conversation they have with your brand, these bots can learn exactly what each customer wants, how they feel about your products, and their buying intent. This data allows you to create highly personalized experiences for each customer at every stage of the sales process. Not only will this delight your customers, but it will also drive significant ROI in AI technology. 

Smart Collaboration Saves Time And Costs 

In recent years, WhatsApp has exploded in popularity as a messaging app, with billions of users worldwide. With messaging becoming a preferred customer engagement channel, a new strategic partnership between Meta and Salesforce promises to help businesses build experiences to chat with customers on the Meta-owned messaging service WhatsApp. 

In a Facebook post, Zuckerberg said: 

Salesforce’s State of the Connected Customer surveyed over 13,000 consumers and nearly 4,000 business buyers across 29 countries and found that 90% of customers say the experience a company provides is as important as its products or services, and 66% of online adults globally prefer messaging as a way of communicating with a business.

Are you up to speed with this latest development? However, before diving headfirst into implementing a chatbot solution, there are several key considerations that you should keep in mind.

1) Understand Your Company’s Business Needs

The first step in implementing a chatbot solution is understanding your company’s business needs.

• Are your customers demanding immediate responses?
• Are your traditional online forms and call centres not meeting their needs?
• Are your customer service representatives answering the same questions repeatedly?

If any of these ring true for your business, it may be time to consider implementing a chatbot solution.

2) Select a Chatbot Solution with Omnichannel Support

Before choosing a chatbot platform, you must have a good idea of the platforms you will use to provide customer support. This is especially important if you are planning to provide omnichannel support, providing seamless, instant and efficient support to the customers regardless of the platform they use. Make sure your chosen chatbot platform can connect your bot with messaging and social media channels, such as Skype, Slack, Facebook Messenger, Twitter, Telegram, WhatsApp, and the web.

3) Multilingual Chatbots Are Even Better

With language barriers creating insurmountable challenges for international businesses, innovative chatbots provide another important advantage: the ability to understand different languages and converse in each one. Thanks to recent breakthroughs in deep learning and Natural Language Processing (NLP) technologies, this feature is significant for businesses considering expanding to new markets and countries with entirely new customer bases and languages.

4) Choose A Customizable And Accessible Chatbot Platform

When building a chatbot, choosing a platform that is easy to use and does not require coding expertise is important. This allows your customer experience professionals to build the perfect chatbot for your business with hands-on knowledge and expertise.

5) The User Experience is Key

The final consideration when implementing a chatbot solution is the user experience. A chatbot must be backed by human-centred design, industry experience, and domain knowledge. Additionally, it’s essential to choose a chatbot development platform that supports speed, scalability, and flexibility to support customers and customer service representatives around the clock. It’s important to remember that while a chatbot is designed to provide relevant information, there will always be instances where it may not be able to answer or understand a customer’s question or inquiry. Human agents and chatbots must work together to build the best experience for the customer.

Revolutionizing Customer Service: How Salesforce Chatbot Integrates with WhatsApp?

If you consider that a chatbot may be something your business is looking to invest in, you may now ask yourself just how to do so! You can do this in several ways, but it is recommended that you do so within your central source of truth: Salesforce.

WhatsApp prevents you from spamming your clients by not supporting promotional messages or large-scale, recurrent alerts. WhatsApp’s customer service comprises two sorts of messages:

1. Customer-initiated conversations – If the customer contacts the company directly over WhatsApp, the customer service agents can attend to their queries directly in Service Cloud Console.
2. Company-initiated outbound notifications – A brand can initiate a WhatsApp discussion with a consumer, but only through pre-approved notifications. Salesforce has issued a list of what you can send and how it can be used.

With over 1.6 billion users worldwide, WhatsApp has established itself as a go-to platform for businesses looking to connect with customers. However, WhatsApp’s strict guidelines on promotional messages and large-scale notifications can make it difficult for companies to utilize the platform entirely. Salesforce has solved this problem by offering an officially-backed solution that allows businesses to communicate with customers in a real-time, organized manner, with performance analytics and a history of communications at their fingertips.

But that’s not all. Salesforce has also integrated Einstein Bots to automate responses, alerts, and notifications, which helps improve the SLA (Service Level Agreement) and increase customer satisfaction. Therefore, as a result of the agreement between Salesforce.com Inc. and Facebook, Inc., customer service over WhatsApp is now a reality.

So if you’re a C-level executive in a B2C or B2B business, you should ask yourself: do you want to open up this channel to 1.6 billion potential customers?

Stay tuned for our next blog, where we’ll dive into the technical details of connecting WhatsApp to Salesforce and automating customer support with bots, step by step.

Notes:

• https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/
• https://help.salesforce.com/s/articleView?id=sf.mc_jb_whatsapp_messages_and_use_cases.htm&type=5

Whether asking Siri to direct you to the nearest McDonald’s or inquiring about the weather to Alexa so you can decide whether to wear a jacket, many have grown accustomed to chatting with virtual assistants powered by artificial intelligence and machine learning to gather the information they seek.

Given the convenience and intuition of such sophisticated virtual assistants, they have proved to be especially useful in enhancing customer service and the overall customer experience. Banking chatbots are one avenue of implementation that has demonstrated this. Many financial services institutions have found relief from industry-wide challenges such as branch staff shortages and keeping up with growing customer expectations by adding chatbots to their customer service repertoires.

Such benefits include:

1. Chatbots make customer service more accessible. Where the typical bank branch or call center operates from 9 to 5, chatbots can offer round-the-clock service. This makes life easier for virtually all customers, eliminating the urgency of having to squeeze in a trip to the bank during a lunch hour or the aggravation of having to stand in line or be put on hold just to have one question answered.
2. Chatbots reduce support costs. Because of their sophisticated artificial intelligence, chatbots can assist with many of the same tasks as human customer service associates. They can guide users through password resets, account balance inquiries, and bill pay, for instance. This gives human customer service associates more bandwidth to assist customers with more complex requests, helping to cultivate more thorough and efficient customer experiences across the board.
3. Chatbots can be engineered to execute automated tasks. For example, financial institutions can engineer chatbots to approve customers for a loan. Bots can simply ask users a few questions, check their credit history, and initiate the loan transfer right then and there, bypassing pesky wait times and the need for extensive manual data entry.
4. Chatbots help boost customer engagement. Chatbots can gather data about their users and offer personalized advice, suggestions, and reminders based on this data. This insight gives financial institutions that use chatbots an edge over less tech-savvy competitors.

Perficient can help your financial services company experience the benefits of implementing a chatbot.

We are partnering with Kore.ai, a Leader in 2022 Gartner® Magic Quadrant for Enterprise Conversational Al Platforms, to offer clients a free, 30-day “Try-‘N-Buy” trial of the Kore Environment. The Kore Environment includes BankAssist, AgentAssist, and SmartAssist products, detailed below:

• • BankAssist: Kore.ai’s BankAssist pre-trained Virtual Assistant understands simple and complex use cases, ranging from balance inquiries to discussions regarding fraudulent transactions — users can check balances, transfer money, pay bills, inquire about their transactions and spending, review account details, set up alerts, and more. Kore’s BankAssist Virtual Assistant allows banks to also customize capabilities and build out new use cases to fine-tune their desired user experience.
  • AgentAssist: Kore.ai’s AgentAssist is a live agent’s best friend. It works in tandem with live agents, monitoring customer-agent conversations and using artificial intelligence to identify customer intents and sentiments, enabling the prediction of customer needs. Live agents can also have AgentAssist execute tasks.
  • SmartAssist: SmartAssist is an end-to-end AI-native contact center as a service (CCaaS) that elevates the contact center experience. It enables users to design, manage, track, supervise, and collaborate, create omnichannel customer experience flows, and can be used in conjunction with BankAssist and Agent Assist.

Think Kore.ai’s digital and voice capabilities might be a good fit for the needs of your financial institution? Reach out to Aldo Sidartawan to get your trial process started and discuss how to fast-track the deployment of state-of-the-art self-service and experience for your customers.

Our life sciences and technology experts recently delivered the webinar, “Improve Medical Product Information Sharing With Virtual Agents,” where they discussed how automation and artificial intelligence can be used to optimize call center operations and improve end-user experiences.

Here are five key takeaways:

1. Just because a system is powered by AI doesn’t mean you have to sacrifice empathy. Systems can be designed to have a natural conversation flow and recognize whether a message is negative or positive. For example, suppose someone calls to inquire whether they should give their puppy more medicine because it vomited shortly after taking its dose. In that case, an AI system can be programmed to compassionately respond with, “I’m sorry to hear that,” before searching its database for the accurate medical advice to relay.
2. AI isn’t meant to replace the call agent, but rather, its purpose is to allow them to focus on higher-value escalations. The severity of adverse reactions and urgent HCP questions can vary significantly from case to case. Conversations with live agents can be prioritized and triaged to quickly route those calls to the best call center resource.
3. AI can help address specific call center challenges. Many pharmaceutical and medical call centers experience extremely high call volumes, especially at certain times of the year (i.e., flu season), resulting in unacceptably long wait times and excessive dropped calls. Call center agents are also challenged with having to meticulously record their interactions, which only exacerbates these issues. AI can use NLP to help address these issues and record interactions. The great thing is that it interfaces with current systems, works in any regulatory area (e.g., biologic, device, animal health, human health, dietary supplements), and can be deployed globally to overcome language barriers.
4. AI virtual agents can be fine-tuned to effectively handle various types of callers. For example, a virtual agent that speaks with doctors may be programmed to communicate using more medical vocabulary than a virtual agent designed to speak with patients.
5. An AI virtual agent delivered in a truly omnichannel capacity will greatly reduce frictions in the user experience. Suppose someone is experiencing an adverse reaction to a medication that gives them a sore throat. In that case, a texting method of communication may be better suited for them than a phone call, and AI systems can equip users with such options. If someone is at the point of sale and has questions about an OTC product or their prescription, they could easily text with questions or concerns.

READ MORE: 10 Questions & Answers About Using Virtual Agents for Medical Information Sharing

Curious to learn more? Watch the recording here or below.