AI/ML

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, which can potentially transform the way we learn and work. In the corporate world, generative AI has the power to transform the way businesses interact with customers and drive growth. The latest research from Salesforce indicates that 2 out of 3 (67%) of IT leaders are looking to deploy generative AI in their business over the next 18 months, and 1 out of 3 are calling it their topmost priority. Organizations are exploring how this disruptive technology of generative AI could impact every aspect of their business, from sales, marketing, service, commerce, engineering, HR, and others. While there is no doubt about the promise of generative AI, business leaders want a trusted and secure way for their workforce to use this technology. Almost 4 out of 5 (79%) of business leaders voiced concerns that this technology brings along the baggage of security risks and biased outcomes. At a larger level, businesses must recognize the importance of ethical, transparent, and responsible use of this technology. A company using generative AI technology to interact with customers is in an entirely different setting from individuals using it for private consumption. There is an imminent need for businesses to adhere to regulations relevant to their industry.  Irresponsible, inaccurate, or offensive outcomes of generative AI could open a pandora’s box of legal, financial, and ethical consequences. For instance, the harm caused when a generative AI tool gives incorrect steps for baking a strawberry cake is much lower than when it gives incorrect instructions to a field technician for repairing a piece of machinery. If your generative AI tool is not founded on ethical guidelines with adequate guardrails in place, generative AI can have unintended harmful consequences that could back come to haunt you. Companies need a clearly defined framework for using generative AI and to align it with their business goals including how it will help their existing employees in sales, marketing, service, commerce, and other departments that generative AI touches. In 2019, Salesforce published a set of trusted AI practices that covered transparency, accountability, and reliability, to help guide the development of ethical AI systems. These can be applied to any business looking to invest in AI. But having a rule book on best practices for AI isn’t enough; companies must commit to operationalizing them during the development and adoption of AI. A mature and ethical AI initiative puts into practice its principles via responsible AI development and deployment by combining multiple disciplines associated with new product development such as product design, data management, engineering, and copyrights, to mitigate any potential risks and maximize the benefits of AI. There are existing models for how companies can initiate, nurture, and grow these practices, which provide roadmaps for how to create a holistic infrastructure for ethical, responsible, and trusted AI development. With the emergence and accessibility of mainstream generative AI, organizations have recognized that they need specific guidelines to address the potential risks of this technology. These guidelines don’t replace core values but act as a guiding light for how they can be put into practice as companies build tools and systems that leverage this new technology. Guidelines for the development of ethical generative AI The following set of guidelines can help companies evaluate the risks associated with generative AI as these tools enter the mainstream. They cover five key areas. Accuracy Businesses should be able to train their AI models on their own data to produce results that can be verified with the right balance of accuracy, relevance, and recall (the large language model’s ability to accurately identify positive cases from a given dataset). It’s important to recognize and communicate generative AI responses in cases of uncertainty so that people can validate them. The simplest way to do this is by mentioning the sources of data which the AI model is retrieving information from to create a response, elucidating why the AI gave those responses. By highlighting uncertainty and having adequate guardrails in place ensures certain tasks cannot be fully automated. Safety Businesses need to make every possible effort to reduce output bias and toxicity by prioritizing regular and consistent bias and explainability assessments. Companies need to protect and safeguard personally identifying information (PII) present in the training dataset to prevent any potential harm. Additionally, security assessments (such as reviewing guardrails) can help companies identify potential vulnerabilities that may be exploited by AI. Honesty When aggregating training data for your AI models, data provenance must be prioritized to make sure there is clear consent to use that data. This can be done by using open-source and user-provided data, and when AI generates outputs autonomously, it’s imperative to be transparent that this is AI-generated content. For this declaration (or disclaimer), watermarks can be used in the content or by in-app messaging. Empowerment While AI can be deployed autonomously for certain basic processes which can be fully automated, in most cases AI should play the role of a supporting actor. Generative AI today is proving to be a powerful assistant. In industries, such as financial services or healthcare, where building trust is of utmost importance, it’s critical to have human involvement in decision-making. For example, AI can provide data-driven insights and humans can take action based on that to build trust and transparency. Furthermore, make sure that your AI model’s outputs are accessible to everyone (e.g., provide ALT text with images). And lastly, businesses must respect content contributors and data labelers. Sustainability Language models are classified as “large” depending on the number of values or parameters they use. Some popular large language models (LLMs) have hundreds of billions of parameters and use a lot of machine time (translating to high consumption of energy and water) to train them. To put things in perspective, GPT3 consumed 1.3 gigawatt hours of energy, which is enough energy to power 120 U.S. homes for a year and 700k liters of clean water. When investigating AI models for your business, large does not necessarily mean better. As model development becomes a mainstream activity, businesses will endeavor to minimize the size of their models while maximizing their accuracy by training them on large volumes of high-quality data. In such a scenario, less energy will be consumed at data centers because of the lesser computation required, translating to a reduced carbon footprint. Integrating generative AI Most businesses will embed third-party generative AI tools into their operations instead of building one internally from the ground up. Here are some strategic tips for safely embedding generative AI in business apps to drive results: Use zero or first-party data Businesses should train their generative AI models on zero-party data (data that customers consent to), and first-party data, which they collect directly. Reliable data provenance is critical to ensure that your AI models are accurate, reliable, and trusted. When you depend on third-party data or data acquired from external sources, it becomes difficult to train AI models to provide accurate outputs. Let’s look at an example. Data brokers may be having legacy data or data combined incorrectly from accounts that don’t belong to the same individual or they could draw inaccurate inferences from that data. In the business context, this applies to customers when the AI models are being grounded in that data. Consequently, in Marketing Cloud, if all the customer’s data in the CRM came from data brokers, the personalization may be inaccurate. Keep data fresh and labeled Data is the backbone of AI. Language models that generate replies to customer service queries will likely provide inaccurate or outdated outputs if the training is grounded in data that is old, incomplete, or inaccurate. This can lead to something referred to as “hallucinations”, where an AI tool asserts that a misrepresentation is the truth. Likewise, if training data contains bias, the AI tool will only propagate that bias. Organizations must thoroughly review all their training data that will be used to train models and eliminate any bias, toxicity, and inaccuracy. This is the key to ensuring safety and accuracy. Ensure human intervention Just because a process can be automated doesn’t mean that’s the best way to go about it. Generative AI isn’t yet capable of empathy, understanding context or emotion, or knowing when they’re wrong or hurtful. Human involvement is necessary to review outputs for accuracy, remove bias, to ensure that their AI is working as intended. At a broader level, generative AI should be seen as a means to supplement human capabilities, not replace them. Businesses have a crucial role to play in the responsible adoption of generative AI, and integrating these tools into their everyday operations in ways that enhance the experience of their employees and customers. And this goes all the way back to ensuring the responsible use of AI – maintaining accuracy, safety, transparency, sustainability, and mitigating bias, toxicity, and harmful outcomes. And the commitment to responsible and trusted AI should extend beyond business objectives and include social responsibilities and ethical AI practices. Test thoroughly Generative AI tools need constant supervision. Businesses can begin by automating the review process (partially) by collecting AI metadata and defining standard mitigation methods for specific risks. Eventually, humans must be at the helm of affairs to validate generative AI output for accuracy, bias, toxicity, and hallucinations. Organizations can look at ethical AI training for engineers and managers to assess AI tools. Get feedback Listening to all stakeholders in AI – employees, advisors, customers, and impacted communities is vital to identify risks and refine your models. Organizations must create new communication channels for employees to report concerns. In fact, incentivizing issue reporting can be effective as well. Some companies have created ethics advisory councils comprising of employees and external experts to assess AI development. Having open channels of communication with the larger community is key to preventing unintended consequences. As generative AI becomes part of the mainstream, businesses have the responsibility to ensure that this emerging technology is being used ethically. By committing themselves to ethical practices and having adequate safeguards in place, they can ensure that the AI systems they deploy are accurate, safe, and reliable and that they help everyone connected flourish. As a Salesforce Consulting Partner, we are part of an ecosystem that is leading this transformation for businesses. Generative AI is evolving at breakneck speed, so the steps you take today need to evolve over time. But adopting and committing to a strong ethical framework can help you navigate this period of rapid change.

Technology is in a constant state of flux and Salesforce, the world’s leading CRM platform is on the front lines of innovation to bring transformative technology to businesses worldwide. Here is a look at how AI is poised to transform the way we work and a sneak peek into the disruptive power of generative AI. AI will transform the future of work Generative AI presents an exciting new opportunity for businesses to tap into the creativity and innovation of their workforce like never before. Here are 6 predictions from Salesforce leaders on what the future workplace will look like. 1. AI will transform how we imagine and measure human productivity. According to a State of Work report published by Salesforce, 60% of business executives say their primary method to measure team productivity is by tracking work hours and email communications. But this is set to pivot around AI. With AI at the workplace, much of the mundane, repetitive work regarded as productivity inputs up until now will be replaced by AI. In the months to come, companies will see a transformative shift in the way performance and productivity are measured by focusing on tangible outcomes such as products launched or leads generated. To achieve this, businesses will need to measure impact rather than measuring activity. Business leaders will have to clearly define the results they want and back their teams to align efforts in the backdrop of these clear goals. 2. AI will free up employee time for more meaningful work. AI is on course to becoming the primary choice for automating workflows so that businesses can achieve full autonomy over the next year or so. There is huge potential for AI to automate mundane tasks across the organization, from marketing and pre-sales to order processing to customer support. By identifying repetitive tasks and leveraging organizational data to drive intelligent predictions and generate automated next steps, AI is well placed to automate and optimize traditional work patterns to free up employee time to do more meaningful profitable work. 3. AI will offset a persistent and recurring challenge for businesses – Agent attrition. Retaining customer service staff has been a thorn in the flesh for businesses. With generative AI, they can automate multiple aspects of customer service and boost the adoption of self-service, translating to a significant reduction in recruitment costs and an improvement in agent productivity. 4. Companies will leverage data and AI to boost productivity. The average employee is fraught with information overload. Siloed data is responsible for over 10 wasted work hours every week. In the next months, businesses that adopt generative AI will see the real impact of this technology on how they work and interact with that data. Companies have already started deploying AI-powered knowledge bases to drive self-service, assist field teams, and get more out of customer data. In the longer term, the productivity of teams will go up a few notches with more advanced generative AI capabilities like task automation and automated trends and insights. 5. The workplace will become smarter with AI. Future-looking businesses have already adopted chatbots and AI-powered virtual assistants to simplify and augment customer service. Going forward, generative AI will also provide quick replies to inquiries, provide guidance to employees, and expedite service requests. It is predicted that AI will play a significant role in driving employee engagement, predicting the services they need to thrive before they even know they need them. 6. Businesses will adapt to an AI-driven re-imagined workplace. Today’s top talent want more from their employer over and above a paycheck. To address this demand, organizations will invest more time and resources in creating a work culture that supports employees beyond the confines of the workplace. These investments will eventually translate to greater employee satisfaction (ESAT), higher employee retention, and overall success. Organizations that focus on these areas will lead the way. Five ways generative AI is poised to reshape the future of business Over 75% of business leaders say they are worried their organization is missing out on the promise offered by generative AI, not just in terms of what the technology can do, but the snowballing effect it can have on the industry. Here’s how some of Salesforce’s sharpest minds see the impact of generative AI in the coming year. 1. Generative AI will become fully operational across the enterprise. Starting with empowering sales, marketing, and customer service teams, and writing code for engineering teams, AI will eventually impact every department. The next big leap will be when generative AI is leveraged not just for content generation, but analysis, decision-making, and business automation. With advances in AI and the wide adoption of chatbots and virtual assistants, businesses will see a marked improvement in efficiency across all workstreams. And while initial Large Language Models (LLMs) will continue to be the backbone of generative AI, organizations will also start adopting custom, domain-specific language models for cost and latency benefits. 2. AI will transform every industry. AI will be embedded into every layer of product engineering to deliver value to customers. Not just that, AI will also transform the way these products are built. By generative AI powered code development, engineering teams can improve their productivity and ability to focus on solving more complex problems. As products evolve, so will customer needs and preference.  In short, AI will impact every aspect of the tech industry including market dynamics and customer behavior. 3. Generative AI will supercharge efficiency. Advancements in semantic prompt processing, a machine learning technique in which a question written in natural language is interpreted by a machine will transform customer service. Companies will be able to deliver quick, personalized service at scale with AI using rich media such as images and video. This will set the stage for a more intuitive digital economy benefitting businesses as well as end users. As AI grows more proficient at surfacing insights from organizational data regardless of their original structure, we will witness a surge in businesses adopting semantic prompt processing capabilities with the amalgamation of structured and unstructured data, such as sales figures and customer reviews, customer demographics, and social media activity. 4. Businesses will focus on customer-centric strategies Today everything is digital – whether engaging with a chatbot, a mobile app, a website, or social media, and this includes generative AI. The value to the customer is the experience and its efficacy. To the business, the value is in the business outcome. The technology of generative AI is not the end in itself but a means to deliver that value to customers and businesses. 5. Embrace generative AI or perish Gartner expects that by 2026, more than 80% of businesses will use generative AI in the live environment, compared to less than 5% in 2023. Generative AI will percolate to every organizational layer, whether it’s for making informed decisions or performing routine daily tasks. From sales forecasting to talent acquisition, all departments across an organization will witness a transformational shift in the way they work. At Girikon, a Gold Salesforce Consulting Partner, we believe that the generative AI revolution will level the playing field regardless of the size of the enterprise. By 2025, AI won't be a good-to-have tool anymore but the axis around which businesses will revolve, signaling an era of unprecedented transformation.

As an IT manager, you would have handled several rollouts and migrations, streamlined legacy systems, and upgraded cybersecurity. And now AI is staring you in the face. How ready are you to build AI apps that your business needs? Do you have in-house skills to build and deploy AI apps? Whether you are building a customer service app or a marketing app, you can adopt a systematic approach to going about it. Here are 5 key steps to building effective AI apps for your organization. 1. Define exactly what you want from your app before starting to build one Businesses across industries have started to embrace the disruptive technology of AI for their everyday operations. Your competitors are likely deploying AI chatbots to provide 24/7 automated, intelligent, customer service. But before you start investing time and resources in building AI apps, you need to answer some key questions. What is the problem you’re trying to solve? Talk with your business’s leaders. Do you want to boost sales? Improve customer satisfaction score?  As a starting step, clearly define use cases. Next, define the desired end state for each use case. This will help you estimate how much effort is required, who to involve, and whether you have adequate resources. What are your competitors doing? Understand what your competitors are doing with their AI tools and for whom. And how can you innovate further on those ideas? And of course, you need to answer one important question – can you build AI apps in-house? Do you have the necessary skills and experience in your team to do this? Based on the use cases you have identified, will you require generative or predictive AI If you don’t have the skills internally like Machine Learning and Natural Language Processing, look for partners and ISVs for solutions and do a thorough comparison of their offers and capabilities. 2. Define the perimeter for ethics and security As an IT manager, security, privacy, and accuracy are not alien to you. But AI amplifies the challenges and raises many risks such as bias and toxicity. AI bias: Negative bias can be caused by algorithm error based on human prejudices or false assumptions. The consequence is an AI tool that works in unintended ways. Generative AI can propagate outputs based on errors and further amplify the problem. Toxicity: Abusive language and hurtful comments can appear in AI-generated outputs. Researchers have found that assuming certain personas can amplify the toxicity of the response. Before you start building your AI app, define trust and ethics parameters. Trusted AI should be empowering, and inclusive apart from being responsible and transparent. 3. Good data is the foundation of effective AI apps If you are building generative AI apps, your machine learning models will train on the data that is fed to them. AI machine learning models train on all kinds of data. And that data needs to be clean and free of redundancy. The more data your LLMs can be trained on, the better will be the output of your AI. 4. Choose the right technology for your AI app The technology you select for building your AI depends to a certain extent on your use case. If your app summarizes text, processes language, or a knowledge base, you will need an LLM. Over time, as the LLM learns more about your business and its data, it can make logical interpretations and draw conclusions. Building your own learning model can be expensive. You will need to hire data scientists and engineers with expertise in ML and NLP. While it is a lengthy cycle, if you do decide to take this route, once your team is ready you can take the help of libraries and toolkits and integrate them into your development. Generative AI platforms and libraries ML and DL platforms: Amazon SageMaker and Google Vertex AI have built-in libraries and tools to train your AI model and support multiple programming languages. NLP toolkits: If you are building chatbots or virtual assistants, SpaCy is a great NLP toolkit for Python enthusiasts. OpenAI allows you to customize their GPTs for your apps. Deep learning libraries: If you want to build apps for speech or image recognition, you can look at a deep learning library to find a framework for building, training, and deploying your apps. Open-source libraries such as PyTorch and MXNet can be used in combination depending on your use case. Computer vision libraries: If you want your app to analyze images or video, you can use open-source libraries such as OpenCV and TensorFlow. PyTorch is another option that can be helpful. Building AI apps with CRM data If you want to build customer-interfacing apps, you will need to leverage your customer data. And without all your data in one place, that’s hard to do. You need an enterprise-grade CRM like Salesforce to make your AI app work best for you. You can connect AI models to Salesforce Data Cloud without running into a wall. With the Model Builder (erstwhile called Einstein Studio), you can bring your own model into Salesforce. 5. Build AI apps and start deploying In a recent developers’ survey conducted by Salesforce, it was found that 70% of developers use or intend to use AI for development. The biggest benefit developers see is reduced development cycles. Try AI for code generation Whether you use AI or not for code generation, you can reduce development time with the Einstein 1 platform for Salesforce. Einstein for Developers understands natural language prompts to write code in seconds. The more precise your prompt, the better will be the quality of the code generated. Once the code is generated you can accept, revise, or reject it. Einstein for Developers uses a customized Large Language Model based on the open-source CodeGen AI model from Salesforce. Use an IDE to accelerate development A web-based integrated development environment (IDE) allows your teams to work from anywhere, anytime. You can modify and debug code and maintain source control in one place.  Code Builder, the new IDE from Salesforce is preloaded with frameworks, has built-in integration with Git, and is free for admins and developers. Salesforce also allows you to integrate other IDEs with it. Follow App Lifecycle Management and DevOps practices Building and launching great AI apps need solid processes across stages of app development, along with collaborative tools for developers, data scientists, testers, and project managers. Salesforce has inbuilt AI tools like Einstein for Developers and Prompt Builder to come to your aid. DevOps Center, available on the Einstein 1 platform, can help you to maintain version control, track changes and push your build for UAT and production. If you prefer working with your own tools for IDE, project management, and DevOps, you can bring them into the Salesforce environment. Connect with an AI expert today. With over a decade of experience as a Salesforce Consulting Partner, our experts are always available to guide you through the process and answer any questions you might have regarding the potential of AI in your business.

In the rapidly evolving business environment, it is essential for companies to utilize state-of-the-art technology to stay competitive. Nowadays, forward-thinking businesses are incorporating artificial intelligence (AI) into their operations, particularly through the adoption of customer relationship management (CRM) software, to automate and enhance their CRM processes. Salesforce, a leading CRM platform, has consistently been a pioneer in innovation, especially in the realm of artificial intelligence (AI). Notably, Salesforce AI has transformed the way organizations handle their customer service processes. The integration of Salesforce and AI is more than just an augmentation. It has indeed opened new avenues in Customer Relationship Management (CRM). Rather, it offers a smarter, efficient, and a highly custom-made customer interaction. To know more about Salesforce AI integration, businesses should consider partnering with a reliable Salesforce consulting partner. Salesforce and Generative AI: A Dynamic Relationship As a cloud-based platform, Salesforce is highly customizable and configurable and can be leveraged by organizations to meet their unique business needs by tailoring their services. By leveraging tools like Salesforce Flow, users can automate intricate business processes, create agile service experiences, while streamlining data management. The next phase of transformation will involve incorporating the capabilities of generative AI into a versatile platform using Einstein GPT. This integration holds the potential to transform the way businesses function and engage with their customers How to Leverage AI to Improve Customer Service? Listed below are ways how AI can help businesses provide better service to their customers:  Improved Customization: Utilizing AI will empower businesses to deliver personalized experiences by harnessing customer data and their preferences. This will pave way for tailored recommendations, quick support, and a deeper comprehension of customer requirements. Unified Omnichannel Support: AI-driven chatbots can integrate easily with several communication channels such social media, web chat and more. This guarantees uniform interactions across several platforms, offering customers a unified experience. Intelligent Automation: AI can be leveraged to automate repetitive and mundane tasks thereby saving a lot of time that can be used up by human agents to focus on more complex and strategic activities. This will boost productivity, quicken response times, and optimize cost for businesses. Sustained Learning and Development: AI systems will keep gathering insights from customer interactions, feedback, and real-time data, which in turn will foster continuous improvement. This continuing improvement will yield more precise responses, intelligent recommendations, and enhanced overall performance. What are the benefits of AI in customer service? AI in customer service offers several benefits that can improve the overall customer experience and streamline business operations. Some of the crucial advantages include: Increased Productivity: Leading IT players believe that AI can be adopted by organizations to serve their customers in a better way. Research conducted reveal that access to AI assistants and tools can increase productivity for support agents significantly.  Increased Efficiency: Carrying out tasks manually can be burdensome for service agents. This includes tasks such as navigating between different systems to access customer history, searching for relevant informative articles, sending field staffs to service locations, and manually inputting responses. These manual processes are usually prone to errors as they are executed by humans. The integration of AI in customer service can provide intelligent suggestions to service workers drawn from knowledge bases, and customer data. A more Personalized Interaction: When a customer interacts with a chatbot, artificial intelligence (AI) has the capability to retrieve vital details, such as the name of customer, location, account type, and language preferred. If the inquiry demands the involvement of a field service technician, AI can promptly convey all relevant information to the technician, allowing them to deliver tailored service as soon as they arrive on-site. Less Exhaustion and Enhanced Morale: AI empowers agents to do away with monotonous, time-intensive tasks, enabling them to focus on tasks that demand creative thinking, problem-solving, and intricate critical thinking. These activities significantly impact the overall customer experience. Consequently, it shouldn’t come as surprise that majority of IT leaders anticipate that generative AI will alleviate workload of teams, while reducing burnout. Scalability: AI systems can simultaneously handle a huge rush of customer queries making it simpler for businesses to scale their customer service operations without consistently increasing staffing levels. A Practical Service Experience: AI has the capability to draw information from contracts of customers, warranties, buying history, and marketing data. This ensures the identification of optimal actions for agents to pursue with customers, even post the conclusion of the service engagement. The future of AI in Customer Service: The future AI seems to be quite promising in the customer service industry. In the years to come, artificial intelligence is poised to gain prominence in workplaces given the ongoing advancements in technologies such as machine learning and natural language processing (NLP). Besides handling routine tasks, these AI programs will offer significant insights into consumer behaviors and habits through big data analysis. Organizations can utilize this valuable data to optimize their return on investment in marketing strategies and branding initiatives. As technology evolves, AI is set to play a key role in uplifting customer experiences and boosting operational efficiency. Final Words: The fusion of AI and Salesforce is reshaping the CRM terrain, presenting matchless possibilities for organizations to elevate both their customer relationships, as well as their operational efficiencies. This integration when leveraged by businesses enables them to position themselves at the frontline of technological advancement, ensuring they stay competitive and in agreement to the ever-changing needs of their customers. In doing so, organizations can provide value to customers and stakeholders while future-proofing their operations in this quickly evolving digital era. Organizations should consider availing Salesforce implementation services if they wish to make the most of the integration of Salesforce and AI. 

According to Salesforce research, close to 90% of customers say that a business's overall experience is as important as its products or services. In today’s competitive landscape where companies are juggling between staffing shortages and overwhelmed resources, they need to be able to do more with less.  Customer expectations are at an all-time high, and given the plethora of options available to them, anything less than an exceptional experience will lead to customer churn. Automation and self-service technologies have given many businesses across industries a significant improvement in productivity, cost savings, and customer satisfaction. In 2021, Salesforce reported that customers using its Cloud products and self-service tools such as AI chatbots witnessed a 30% increase in customer satisfaction along with a 27% improvement in agent productivity. To meet this ever-growing demand, Salesforce launched Virtual Assistant – an Einstein-powered chatbot solution built specifically for financial services businesses to automate routine customer requests faster across popular digital channels like SMS or messaging platforms. This enables agents to focus on complex cases while chatbots can promptly resolve routine service requests, such as updating credit card information, renewing subscriptions, making payments, modifying subscription plans, and more. Virtual Assistant offers multilingual support, allowing businesses to use a single chatbot across multiple geographic regions regardless of their native language. And in the future, Einstein-powered Virtual Assistants will automatically create support articles based on customer conversations. Salesforce Chatbots for financial services come with pre-built bot templates, leading to faster setup and deployment. They can streamline support and assist agents with routine questions such as “How do I calculate my tax?” or “How do I upgrade my insurance plan?” With Virtual Assistant, financial services companies can re-direct thousands of customer calls to the Salesforce Chatbot leading to significant cost savings. Salesforce Chatbots can handle thousands of concurrent conversations for queries such as loan application status, product information, insurance premium renewal, claim filing, technical support, and more, freeing up dozens of front-line agents. Salesforce Chatbots improve the customer experience by enabling seamless self-service for simple tasks, thereby significantly reducing wait times to speak with an agent. The rewards of embracing self-service technology can be substantial, and businesses need to leverage technology to scale quickly and deliver efficient customer service. Features of Salesforce ChatBots Salesforce Chatbots powered by Einstein are equipped with advanced features to solve customer issues by replying to their questions and understanding their behavior to evolve continuously. Here are some stand-out features of Salesforce Chatbots that you should be aware of before you hire a Salesforce Development Partner for its implementation. Natural Language Processing (NLP) Salesforce Chatbots use NLP to understand customer intent and provide relevant answers. This makes bot interactions more natural for customers. Multi-Channel Support Salesforce Chatbots can be deployed on multiple channels such as mobile apps, websites, online stores, social media pages, and on popular messaging platforms like WhatsApp and SMS. This allows customers the convenience and flexibility to interact with businesses on their preferred channels whenever they want. Personalization Salesforce Chatbots can personalize responses based on customer data, their preferences, past purchases, and browsing behavior, making every interaction more relevant and meaningful. Contextual Conversations Salesforce Chatbots can understand and maintain context across multiple conversations regardless of the channel, thereby providing more accurate and relevant responses to customer queries. Integration with Salesforce Salesforce Chatbots are built on the world’s leading CRM platform, allowing for seamless integration with other Salesforce cloud products. With Salesforce Chatbots, managing customer data has never been easier. Analytics and Insights With Salesforce Chatbots, businesses can get valuable insights into customer behavior. By analyzing customer interactions, Salesforce Chatbots can help businesses identify areas for improvement to enhance the customer experience. Continuous Learning Salesforce Chatbots leverage machine learning algorithms to learn continuously from every interaction and better their responses over time, improving the accuracy and relevance of responses as they gain more experience. Here are some generic features of Salesforce Chatbots. Re-direct bot conversations to human agents for complex customer queries. Understand the intent of customer queries. Rapid response times. Understand customer input and recognize errors. Available 24/7. Lead generation – collect customer data and qualify leads for sales teams. Scale customer service with personalized automation and connected customer data According to Salesforce, new features in Financial Services Cloud such as proactive service and call deflection will enable financial services firms to reduce operating expenses while delivering exceptional customer service experiences: In 2021, Salesforce customers reported a 27% increase in case resolution with self-service automation and AI-powered Chatbots (Virtual Assistant). Salesforce Chatbots automate routine request resolution across popular messaging channels such as SMS and WhatsApp, so agents can spend more time on cases that necessitate human intervention. With Customer Service Coordination, agents can collaborate in Slack to fast-track case resolution. With automated workflows and custom Chatbots, Customer Service Coordination gathers customer data and generates alerts in a central Slack channel allowing teams to respond to critical incidents faster like fraud incidents, executing time-sensitive trades, and claims processing. With the Customer Data Platform, financial services marketing teams can unify customer data from multiple sources with a point-and-click interface. This enables marketing teams to engage with customers across multiple channels such as web, email, mobile, and social media in a far more personalized way.  With Salesforce Chatbots, enhanced Insights, and Data Actions, one-on-one advisor interactions and transactions can be triggered in real time. A unified console with actionable insights and workflows for faster service New features in Salesforce Financial Services Cloud include AI-powered dashboards and Chatbots to deliver key insights: With Intelligent Agent Desktop, agents can get access to deeper customer insights from right within the console page. With Customer Identity Verification, agents can reduce the risk of fraud, and with Customer Record Alerts, Chatbots can serve up issues that customers may not be aware of when they initiate a conversation. With Analytics for Financial Services, financial services businesses can interpret customer data with insights for faster and better decision-making, eventually delivering more revenue, and strengthening customer engagement. Girikon has been a Salesforce Consulting Partner for over a decade providing unique and scalable solutions for the financial services industry. We have the necessary expertise in-house to deliver tailored experiences to every customer through self-service, automation, and AI to improve efficiency across your business. Contact us today to learn more about how Salesforce Chatbots can transform your financial services business.

In today’s increasingly connected world, data is the point on which the entire business world pivots. We are generating unimaginable amounts of data every day. And locked within these humongous stores of data are the insights that businesses can use to better understand themselves and more importantly their customers. To remain competitive, businesses need to do more than just collect data. They need to be able to capture and analyse that data and convert it into actionable insights in real time to succeed. Enter Salesforce Einstein Analytics Here are 40 reasons why Einstein Analytics is the no. 1 choice when it comes to data analytics for your business. Hit the Ground Running. Work with a someone you can trust: Enjoy peace of mind knowing that you are working with the world’s no 1 CRM platform. Cust Costs: Reduce operating costs by using a pay as you use cloud-based analytics platform. Say goodbye to expensive installation or maintenance costs, and onsite hardware. Get going quickly: Leverage powerful analytics within minutes, thanks to out of the box solutions. Cut out the fluff: Pay only for the features you use. Salesforce Einstein Analytics comes with flexible usage models, so you always have the tools you need, at a price that suits your budget. Customise your solution: Salesforce Einstein Analytics is fully customisable and can be easily tailored for your business. With Einstein Analytics, you can set up the solution that works best for you. Built-in support: Salesforce Einstein Analytics comes with comprehensive guides, tutorials, videos, and multiple support options across channels. Integrate your data: No need to depend on your IT teams to upgrade your software for data analysis. Einstein Analytics seamlessly integrates analytics tools with every application and system, giving you a coherent, integrated, easy-to-use solution that gives you faster results. Connect Across Departments. Integrate seamlessly with the entire Salesforce platform: Salesforce Einstein Analytics integrates perfectly with all Salesforce products such as Sales Cloud, Service Cloud, Marketing Cloud etc giving every user easy access to unified customer data. Collaborate: Collaborate across sales, service, marketing, and other teams with cloud-based data analytics that can be accessed from anywhere across any device. Unify your goals: Give your teams a unified vision and objectives they can strive for, with data that is insightful, reliable, and actionable. Generate stunning visuals: Use built-in tools to convert data into stunning insightful reports and dashboards for presentations. Get conversational: Leverage social media technology to enhance team communication, with Chatter for Einstein Analytics. Put it in context: Get consistent views across departments with embedded reports and dashboards. Be available always: Work on your data over any device, from anywhere on the planet. Analyse Your Business. Monitor team performance: Leverage real-time reports to view team performance and identify trouble areas early and optimize. Access KPIs: Discover key performance indicators across your organisation to ensure you do not deviate from the path of success. Track call-center efficiency: See customer support trends across channels right on your dashboard and make informed decisions to enhance the customer service experience. Empower teams to self analyse: Give your teams the power to measure their own performance and set new performance benchmarks. Find the Key to Sales Success. See the big picture: Explore all data in a unified dashboard. Get a 360 degree snapshot of the health of your business. Eliminate borders: Get a unified view of your business across geographies, products, customer segments and time periods, for a true picture of how your business is performing. Predict the future: View historical trends to intelligently forecast which strategies are most likely to work and which leads are the most promising. Reduce customer churn: Get detailed insights into each and every customer, deliver personalised customer experiences and ensure customer loyalty and retention. Prioritise leads: Analyse your leads to assess the likelihood of their conversion and focus on the most promising ones. Evaluate your lead sources: Discover which sources are the most productive, so you can focus your efforts where it pays off the most. Enhance the customer experience: Resolve issues and monitor customer satisfaction directly from within Salesforce, and optimise. Market Smarter. Dive deeper: Go deep into your marketing data and get a detailed analysis of funnels, campaigns, conversion rates and more across channels. Present the right message: Create messaging to attract your target audience, and get valuable insight into that audience. Be your own data analyst: Marketing data analysis is too precious to hand over to someone else. With easy-to-use tools and visually compelling reports, become your own marketing analyst. Take instant action: Act in real time with up-to-the-minute marketing data from unified dashboards. Specialize in B2B marketing: Leverage the power of unique and effective B2B marketing tools in Salesforce to stay ahead of the competition. Understand the brand experience: Analyze data to see what your customers see, and optimize the customer experience. Optimise Service. Set your priorities: Prioritise open cases with service manager, and give your teams a clear view of customers that need their attention. Evaluate your accounts: Identify accounts with the highest number of cases and highest opportunity. Connect with your agents: Get a complete view of agents and their cases, and assign notifications based on configurable conditions. Review your service backlog: Compare data and identify service trends over time to assess how service levels compare across years. Revolutionise Analytics for Your Organization. Integrate with third-party apps: Leverage advanced integration options for any third-party application and extend your analytics beyond Salesforce. Optimise your pipelines: Leverage data-driven strategies to manage your pipelines. Automate analysis: Salesforce Einstein AI is designed to automatically analyse millions of data combinations for informed actions. Data security: Share data across devices securely using the cloud platform security services trusted by over 150,000 businesses worldwide. Push the limits: Extend your analytics abilities with custom-made apps or find the right ready-made app for your specific analytics needs on the Salesforce AppExchange. Everyday, we are producing mind-bogglingly huge amounts of data. Businesses need to use that data as a foundation for data analytics, to understand themselves and their customer better, to drive enhanced customer experiences. Girikon is a Salesforce Consulting Company and has helped businesses across the globe achieve success on the Customer 360 platform. To know more about how you can turn your data into intelligent actionable insights with Salesforce Einstein Analytics, contact us today.

Generative Artificial Intelligence (Generative AI) is the latest next generation technology. Generative AI tools have made it very easy for employees and professionals to compose and refine emails, fine tune presentations and reports, write code, put together social media campaigns, and fast track customer service interactions. But not everyone is able to maximize its full potential. More often than not it comes down to the prompt, the statements or questions you feed into a Generative AI tool. The better your prompts, the better will be the Generative AI response. The key takeaway If you want to get the most out of Generative AI and the generative pre-trained transformer (GPT) models that generate conversational language, you might want to get your hands dirty in prompt engineering. This gives the Generative AI model clearer details for what you want instead of being ambiguous. Generative AI is getting smarter as you read this, but it cannot read your mind. It can only give you responses based on its understanding of the prompt you give it. So be specific. GPT works better when the prompt is longer. The prompt, which is the question you are asking the tool, needs to be precise and contextual for it to generate the right response. And that’s the key to unlocking the full potential of Generative AI. What you need to know When writing a prompt, approach the tool like you’re having a normal day to day conversation with a colleague. Use clear language and descriptions.  The devil is in the detail. The Generative AI tools will work better for you if your prompts are precise and detailed. You can have an interactive conversation with your Generative AI tool and dive deeper into what you’re looking for. These following tips would be helpful when writing prompts for Generative AI: Write clearly and concisely so your Generative AI tool understands your specific request. Write linguistically correct, complete sentences with descriptive words, that clearly describes what you’re looking for. For precise responses, ask specific questions, and avoid questions that offer a yes/no response. Add context to your prompt. Explain what is it that you wish to achieve and define your target audience. Engage in a back-and-forth conversation. Follow up the initial response with further questions to go deeper and get even more specific and relevant responses.  What is prompt engineering? Prompt engineering is the art of asking clear, descriptive questions or providing detailed information to Generative AI tools, such as a GPT tool or chatbot, to fetch the best results. With the meteoric rise in adoption of Generative AI tools for personal as well as business use, effective prompt engineering skills can help you improve the efficacy of these generative AI tools. The more specific and descriptive your prompt, the better the AI generated results. And you can get creative like you would ask an expert of the subject of your enquiry. For instance, you can even ask the Generative AI tool to reply as someone well known, like Isaac Newton, to get a response from that individual’s point of view. Generative AI uncovers information from piles of data available on the internet. However, narrowing down your query by providing specific questions or instructions in your prompt and adding context will deliver better results. So get creative. Whether you are an expert prompt engineer or a novice in generative AI, it would be prudent to follow these 6 tips mentioned below to get the most from this disruptive technology. Be specific: For example, instead of writing, “Create a social media campaign,” which is a very generic instruction, you can write, “Create a social media campaign for an ecommerce website that sells sports apparel for tennis fans of Roger Federer and Rafael Nadal.” Engage conversationally: Generative AI may not understand localized dialect or colloquial language. Imagine you are speaking to a co-worker, not a computer. Use open-ended questions: Avoid question with binary responses like a yes/no response.  These prompts limit the Generative AI’s ability to surface detailed, contextual information. Set a persona: Get creative. Ask the Generative AI tool to give answers from the point of view of a public figure (past or present) like Isaac Newton or Christine Amanpour depending on the subject you want to ask about. In fact, you can even define a specific role for specific answers like an operations manager or lawyer.   Define your audience and channel: Specify in your prompt whether you are writing for millennials or GenX. Specify where the audience is going to read it – such as on a social media platform, a blog post, or on website. Ask follow-up questions: The beauty of Generative AI is that you can engage in a back-and-forth conversation with it while maintaining context. It’s akin to speaking with a human. Except that it’s not. If you are not happy with the initial response, you can ask follow-up questions to get more specific responses. This technique is sometimes referred to as “prompt chaining,” where you split your prompts sequentially to get more specific and tailored answers and use answers from one prompt to draw out the next. Use prompt engineering effectively for Generative AI products to work with you and not against you Generative AI tools are new and evolving as you read this. They are not perfect and they’re definitely not human. They are designed to make you feel like you’re having a conversation with a human on the other side, but in reality, it’s a back-and-forth with a computer that has access to heaps of data. Keep these points in mind during your Generative AI prompt writing and subsequent usage of the responses: Generative AI is not always factual. Sometimes it makes up answers, so ensure that you verify what you get. Avoid any copyright infringements. Ensure that what your Generative AI tool gives you isn’t plagiarized from somewhere. Generative AI tools do not understand nuance, local dialects and subtlety. They are not from your neighbourhood. Ensure that your prompts are as specific and clear as possible. Generative AI is not always completely accurate. It’s a fundamental reality of this technology and as a user you need to ensure that you verify any factual data or information before publishing it. Generative AI is already creating a revolution in CRM applications. Girikon is a Certified Salesforce Implementation Partner with a global delivery model. To know more about how Generative AI can work for you, connect with an expert today.